{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8b818a75",
   "metadata": {},
   "source": [
    "# A.6 Hackathon exercise — South Korea 2020\n",
    "\n",
    "## Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ea2e480",
   "metadata": {},
   "source": [
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qiskit-community/qiskit-metal/blob/main/docs/circuit-examples/full-design-flow-examples/A.6-Hackathon-exercise-South-Korea-2020.ipynb)\n",
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/qiskit-community/qiskit-metal/main?labpath=docs%2Fcircuit-examples%2Ffull-design-flow-examples%2FA.6-Hackathon-exercise-South-Korea-2020.ipynb)\n",
    "\n",
    "> 💡 **Running in Colab or Binder?** Skip the desktop GUI install — the cell below grabs the lite (no-Qt) wheel, and `qm.gui(design)` auto-picks an inline matplotlib viewer with the same API as the desktop `MetalGUI`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "simulation-pathways",
   "metadata": {},
   "source": [
    "> 🧭 **Other simulation pathways.** This notebook uses Ansys, which needs a license. Without one, the same kind of result can come from the open-source ElmerFEM path for capacitance (tutorial 4.19) or the pip-installable gmsh + scikit-fem solver for eigenmodes, couplings and capacitance (tutorials 4.43–4.45); AWS Palace is available through SQDMetal, with a native integration in progress. [Simulation pathways](https://qiskit-community.github.io/qiskit-metal/simulation-pathways.html) summarizes what each solver computes, how to install it, and which tutorials use it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3896fec5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In Colab / Binder, uncomment to install Quantum Metal (lite, no Qt).\n",
    "# Locally you should already have it via `pip install quantum-metal` or\n",
    "# `pip install 'quantum-metal[gui]'` for the desktop GUI.\n",
    "# !pip install -q quantum-metal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "186ba0ca",
   "metadata": {},
   "source": [
    "> 💡 **Using this tutorial without the Qt GUI**\n",
    "> \n",
    "> This tutorial uses the desktop `MetalGUI`. To follow along on Colab, Binder, JupyterHub, or any environment where Qt isn't available, **replace any `gui.rebuild()` / `gui.screenshot()` call with `qm.view(design)`** — it renders the design to a matplotlib `Figure` you can display inline or save with `fig.savefig(...)`.\n",
    "> \n",
    "> See [1.1 Quick start](https://qiskit-community.github.io/qiskit-metal/tut/1-Overview/1.1-Quick-start.html) for a complete runnable walkthrough and [`docs/headless-usage.rst`](https://qiskit-community.github.io/qiskit-metal/headless-usage.html) for the full reference."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4123e509",
   "metadata": {},
   "source": [
    "*Make sure to have the right kernel selected!*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7df1cf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <h1 style=\"\n",
       "        background: #12c2e9;  /* fallback for old browsers */\n",
       "        background: -webkit-linear-gradient(to right, #d4418e 0%, #0652c5 74%);  /* Chrome 10-25, Safari 5.1-6 */\n",
       "        background: linear-gradient(315deg, #d4418e 0%, #0652c5 74%); /* W3C, IE 10+/ Edge, Firefox 16+, Chrome 26+, Opera 12+, Safari 7+ */\n",
       "        margin-top: 50px;\n",
       "        border-style: outset;\n",
       "        padding-top:100px;\n",
       "        padding-bottom:50px;\n",
       "        padding-left:25px;\n",
       "        color: white;\n",
       "    \"> Welcome to Qiskit Metal! <h1>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import qiskit_metal as metal\n",
    "from qiskit_metal import designs, draw\n",
    "from qiskit_metal import MetalGUI, Dict, open_docs\n",
    "\n",
    "%metal_heading Welcome to Qiskit Metal!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40e83bdd",
   "metadata": {},
   "source": [
    "Welcome to Qiskit Metal! \n",
    "\n",
    "For this example tutorial, we will attempt to create a simple two qubit chip. We will want to generate the layout, simulate/analyze and tune the chip to hit the parameters we are wanting, finally rendering to a GDS file.\n",
    "\n",
    "One could generate subsections of the layout and tune individual components first, but in this case we will create all of the layout. We will want a transmon (in this case choosing ones with charge lines), meandered and simple transmission lines, capacitor couplers, and launchers for wirebond connections. So we will import these, and also create a design instance and launch the GUI."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0c9d049",
   "metadata": {},
   "source": [
    "## Design the layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19ac6a2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.qlibrary.qubits.transmon_pocket_cl import TransmonPocketCL\n",
    "\n",
    "from qiskit_metal.qlibrary.tlines.meandered import RouteMeander\n",
    "from qiskit_metal.qlibrary.tlines.pathfinder import RoutePathfinder\n",
    "\n",
    "from qiskit_metal.qlibrary.lumped.cap_3_interdigital import Cap3Interdigital\n",
    "from qiskit_metal.qlibrary.terminations.launchpad_wb import LaunchpadWirebond"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d793466",
   "metadata": {},
   "outputs": [],
   "source": [
    "design = metal.designs.DesignPlanar()\n",
    "gui = metal.MetalGUI(design)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47be1ae7",
   "metadata": {},
   "source": [
    "Since we are likely to be making many changes while tuning and modifying our design, we will enable overwriting. We can also check all of the chip properties to see if we want to change the size or any other parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "21e3651d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'material': 'silicon',\n",
       " 'layer_start': '0',\n",
       " 'layer_end': '2048',\n",
       " 'size': {'center_x': '0.0mm',\n",
       "  'center_y': '0.0mm',\n",
       "  'center_z': '0.0mm',\n",
       "  'size_x': '9mm',\n",
       "  'size_y': '6mm',\n",
       "  'size_z': '-750um',\n",
       "  'sample_holder_top': '890um',\n",
       "  'sample_holder_bottom': '1650um'}}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "design.overwrite_enabled = True\n",
    "design.chips.main"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19a91ee2",
   "metadata": {},
   "source": [
    "We will add the two qubits. We know we will want each qubit to have two connection pads, one for readout, and another for the connection to the other qubit. We can see any options the qubit qcomponent has to figure out what we might want to modify when creating the component. This will include the components default options (which the component designer included) as well as renderer options (which are added based on what renderers are present in Metal)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a8e47694",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'pos_x': '0um',\n",
       " 'pos_y': '0um',\n",
       " 'connection_pads': {},\n",
       " '_default_connection_pads': {'pad_gap': '15um',\n",
       "  'pad_width': '125um',\n",
       "  'pad_height': '30um',\n",
       "  'pad_cpw_shift': '5um',\n",
       "  'pad_cpw_extent': '25um',\n",
       "  'cpw_width': 'cpw_width',\n",
       "  'cpw_gap': 'cpw_gap',\n",
       "  'cpw_extend': '100um',\n",
       "  'pocket_extent': '5um',\n",
       "  'pocket_rise': '65um',\n",
       "  'loc_W': '+1',\n",
       "  'loc_H': '+1'},\n",
       " 'chip': 'main',\n",
       " 'pad_gap': '30um',\n",
       " 'inductor_width': '20um',\n",
       " 'pad_width': '455um',\n",
       " 'pad_height': '90um',\n",
       " 'pocket_width': '650um',\n",
       " 'pocket_height': '650um',\n",
       " 'orientation': '0',\n",
       " 'make_CL': True,\n",
       " 'cl_gap': '6um',\n",
       " 'cl_width': '10um',\n",
       " 'cl_length': '20um',\n",
       " 'cl_ground_gap': '6um',\n",
       " 'cl_pocket_edge': '0',\n",
       " 'cl_off_center': '50um',\n",
       " 'hfss_wire_bonds': False,\n",
       " 'q3d_wire_bonds': False,\n",
       " 'hfss_inductance': '10nH',\n",
       " 'hfss_capacitance': 0,\n",
       " 'hfss_resistance': 0,\n",
       " 'hfss_mesh_kw_jj': 7e-06,\n",
       " 'q3d_inductance': '10nH',\n",
       " 'q3d_capacitance': 0,\n",
       " 'q3d_resistance': 0,\n",
       " 'q3d_mesh_kw_jj': 7e-06,\n",
       " 'gds_cell_name': 'my_other_junction'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TransmonPocketCL.get_template_options(design)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "481ed2f5",
   "metadata": {},
   "source": [
    "We will then add those two qubits with the options we are wanting to define, these can all be modified easily later too.\n",
    "The rebuild command is included so the changes can be seen immediately in the GUI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59b61226",
   "metadata": {},
   "outputs": [],
   "source": [
    "options = dict(\n",
    "    pad_width=\"425 um\",\n",
    "    pocket_height=\"650um\",\n",
    "    cl_pocket_edge=\"180\",\n",
    "    connection_pads=dict(\n",
    "        readout=dict(loc_W=+1, loc_H=+1),\n",
    "        bus=dict(\n",
    "            loc_W=-1,\n",
    "            loc_H=-1,\n",
    "        ),\n",
    "    ),\n",
    ")\n",
    "\n",
    "Q1 = TransmonPocketCL(\n",
    "    design,\n",
    "    \"Q1\",\n",
    "    options=dict(\n",
    "        pos_x=\"0.7mm\",\n",
    "        pos_y=\"0mm\",\n",
    "        gds_cell_name=\"FakeJunction_01\",\n",
    "        hfss_inductance=\"14nH\",\n",
    "        **options,\n",
    "    ),\n",
    ")\n",
    "\n",
    "Q2 = TransmonPocketCL(\n",
    "    design,\n",
    "    \"Q2\",\n",
    "    options=dict(\n",
    "        pos_x=\"-0.7mm\",\n",
    "        pos_y=\"0mm\",\n",
    "        gds_cell_name=\"FakeJunction_02\",\n",
    "        hfss_inductance=\"12nH\",\n",
    "        orientation=\"180\",\n",
    "        **options,\n",
    "    ),\n",
    ")\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55a9a069",
   "metadata": {},
   "source": [
    "We will next connect the two transmons together to form a bus using RoutePathfinder, an auto drawing CPW transmission line. We simply have to give the start and end location using the component pins of what we are trying to connect to.\n",
    "We can use the GUI to confirm the pin names we want to give as inputs.\n",
    "\n",
    "Pins also play an important role with rendering and simulations, as any unconnected pin can be defined as a short, open, or driven terminal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12532754",
   "metadata": {},
   "outputs": [],
   "source": [
    "bus_Q1_Q2 = RoutePathfinder(\n",
    "    design,\n",
    "    \"Bus_Q1_Q2\",\n",
    "    options=dict(\n",
    "        fillet=\"99um\",\n",
    "        lead=dict(end_straight=\"250um\"),\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Q1\", pin=\"bus\"),\n",
    "            end_pin=Dict(component=\"Q2\", pin=\"bus\"),\n",
    "        ),\n",
    "    ),\n",
    ")\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e838815b",
   "metadata": {},
   "source": [
    "Then the readout structures can be added, being the capacitor couplers and meandered transmission lines to form the readout resonators. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81402f43",
   "metadata": {},
   "outputs": [],
   "source": [
    "cap_Q1 = Cap3Interdigital(\n",
    "    design,\n",
    "    \"Cap_Q1\",\n",
    "    options=dict(pos_x=\"2.5mm\", pos_y=\"0.25mm\", orientation=\"90\", finger_length=\"40um\"),\n",
    ")\n",
    "cap_Q2 = Cap3Interdigital(\n",
    "    design,\n",
    "    \"Cap_Q2\",\n",
    "    options=dict(\n",
    "        pos_x=\"-2.5mm\", pos_y=\"-0.25mm\", orientation=\"-90\", finger_length=\"40um\"\n",
    "    ),\n",
    ")\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03f241f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "readout_Q1 = RouteMeander(\n",
    "    design,\n",
    "    \"Readout_Q1\",\n",
    "    options=dict(\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Q1\", pin=\"readout\"),\n",
    "            end_pin=Dict(component=\"Cap_Q1\", pin=\"a\"),\n",
    "        ),\n",
    "        lead=Dict(\n",
    "            start_straight=\"0.325mm\",\n",
    "            end_straight=\"125um\",  # put jogs here\n",
    "        ),\n",
    "        meander=Dict(asymmetry=\"-50um\"),\n",
    "        fillet=\"99um\",\n",
    "        total_length=\"5mm\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13431e0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "readout_Q2 = RouteMeander(\n",
    "    design,\n",
    "    \"Readout_Q2\",\n",
    "    options=dict(\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Q2\", pin=\"readout\"),\n",
    "            end_pin=Dict(component=\"Cap_Q2\", pin=\"a\"),\n",
    "        ),\n",
    "        lead=Dict(\n",
    "            start_straight=\"0.325mm\",\n",
    "            end_straight=\"125um\",  # put jogs here\n",
    "        ),\n",
    "        meander=Dict(asymmetry=\"-50um\"),\n",
    "        fillet=\"99um\",\n",
    "        total_length=\"6mm\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efadc91d",
   "metadata": {},
   "source": [
    "With the launchers and short transmission lines to connect them to the capacitors and the charge lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ce08419",
   "metadata": {},
   "outputs": [],
   "source": [
    "launch_Q1_read = LaunchpadWirebond(\n",
    "    design, \"Launch_Q1_Read\", options=dict(pos_x=\"3.5mm\", orientation=\"180\")\n",
    ")\n",
    "launch_Q2_read = LaunchpadWirebond(\n",
    "    design, \"Launch_Q2_Read\", options=dict(pos_x=\"-3.5mm\", orientation=\"0\")\n",
    ")\n",
    "\n",
    "launch_Q1_cl = LaunchpadWirebond(\n",
    "    design,\n",
    "    \"Launch_Q1_CL\",\n",
    "    options=dict(pos_x=\"1.35mm\", pos_y=\"-2.5mm\", orientation=\"90\"),\n",
    ")\n",
    "launch_Q2_cl = LaunchpadWirebond(\n",
    "    design,\n",
    "    \"Launch_Q2_CL\",\n",
    "    options=dict(pos_x=\"-1.35mm\", pos_y=\"2.5mm\", orientation=\"-90\"),\n",
    ")\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd38a5b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "tl_Q1 = RoutePathfinder(\n",
    "    design,\n",
    "    \"TL_Q1\",\n",
    "    options=dict(\n",
    "        fillet=\"99um\",\n",
    "        lead=dict(end_straight=\"150um\"),\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Launch_Q1_Read\", pin=\"tie\"),\n",
    "            end_pin=Dict(component=\"Cap_Q1\", pin=\"b\"),\n",
    "        ),\n",
    "    ),\n",
    ")\n",
    "\n",
    "tl_Q2 = RoutePathfinder(\n",
    "    design,\n",
    "    \"TL_Q2\",\n",
    "    options=dict(\n",
    "        fillet=\"99um\",\n",
    "        lead=dict(end_straight=\"150um\"),\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Launch_Q2_Read\", pin=\"tie\"),\n",
    "            end_pin=Dict(component=\"Cap_Q2\", pin=\"b\"),\n",
    "        ),\n",
    "    ),\n",
    ")\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "516fbd6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "tl_Q1_cl = RoutePathfinder(\n",
    "    design,\n",
    "    \"TL_Q1_CL\",\n",
    "    options=dict(\n",
    "        fillet=\"99um\",\n",
    "        lead=dict(end_straight=\"150um\"),\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Launch_Q1_CL\", pin=\"tie\"),\n",
    "            end_pin=Dict(component=\"Q1\", pin=\"Charge_Line\"),\n",
    "        ),\n",
    "    ),\n",
    ")\n",
    "\n",
    "tl_Q2_cl = RoutePathfinder(\n",
    "    design,\n",
    "    \"TL_Q2_CL\",\n",
    "    options=dict(\n",
    "        fillet=\"99um\",\n",
    "        lead=dict(end_straight=\"150um\"),\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Launch_Q2_CL\", pin=\"tie\"),\n",
    "            end_pin=Dict(component=\"Q2\", pin=\"Charge_Line\"),\n",
    "        ),\n",
    "    ),\n",
    ")\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()\n",
    "gui.screenshot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b536ff88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zlNS0BFwcZ1TZI4Uugz0zTv56OUlCyzICfzv6rLmmumaYDRxi/HD3r/ez5XRp3Kntf+d6De+m+Gm1NLxwk/HzeyXu+999iemwsTYPtm8ISBCRQEkL3oXefJpWW9M4w7z/IL3gvy6mEABU9s1jAakkQO1HsKGolMvnQrWnnwx9/PDhw4cPHz56cmGhWeT5S7WVSZeWiBV1TQ1UmEDl3v/rSk3LsZ1GDsUvLFt3TXW4TwcmAHzpoNPFL29ef5EpJoGjoqmhzMIZ9b7/F9d2dRO+OHwour1HRzYAsOzcOsQeORiu3snNqC6TS9VS1QpdhvcuOLXjVFwpLc++v+OPYMMhAy0YACw7D8fss0dDLTwcOAx9d5fiU0fCTGrprc3t5DuMf33NMn96oG+3Ty+o1H4oFVw82r85dizBwcOGyWrv4Rh/7Hhsh841pe5fwqbwzp6goQlMAi/MhtTXeF4ylhSDJcdgeclY2iusIBuYBK6hiQl1MTaFhgpHEARBEARB/lvq8QsY45t26tpB+5NOwpjmsF2nvlM+N9XNRN/IxmP0xhtJUhqAYeY9t8/7VQ6Ghuaj/8oxGb96cvHW3l36DB427QTboSPry9uh8/721dXU0jNx7Dd3/xvzpf7Xt3qpYrVtS/L+2k++LuYGBqYeS2J7/ra2f9XMX23Ilv0j8zf3sDB1GnPOaIiXEoPRPBPvMq3nH/zV/eVSd2OhzdAjzFnHtg1Swz6tlswGJ9gYv9ec8coXd51ONZxy8Khv8W4feyN94/aeU397mCmvtVSm7fz9q9WO+XTtPXDIvLsGnhX9jWs5gg1Evrt4PtJqwoweepoVdNynTnGJ8/OLqfmyBstp1tpeb77v3q3fEO+FtzRczGtqSmXa+XirJSQaDh9uWX74vnTQqbzwg3N72hgZGNkO/Zu3eOdMq3ofcqaVRyfFvEJDVyceAAAoOLlZFOXSTu52Xzl1/1m/xqrGVAduPTaHeXSYjdDYfclLt52HvitvOFZ06Wwvkxi6uQgwYNl6ODMlaq7uBjVHzbL3GaGfmaEydJQbt/p7tR1KTN3N3VQqt3V35ABwnNxt5FILj0716yUPwCKw1Fi8/EFaDG5pA31G0+JCrKwQ6zOatrCBtBicRWAAwKCw8gcIgiAIgiAI8t+BFRQUtHQMzYNKOzi0y6MpUce9eS0dClJPdN6Zca4nugUFzDZEraEtJOIcS6c9qWtDRZxjOfnKP4TjuYkVHQJoAA0TysiZLH8r/Q2e8Yrh5FuPsQoRBEEQBEEQpK37l2cqZOKjwJhCAmhJ0tVtB2Pd+noofX0lpJWRvD1xLKLTeG+Df/nZ2qqxCEyej72/wyp7z4g6wcZKGTY9CSdfuZOv3LYniZViUSfYZe8ZiXdY6no0arVGEARBEARB/mv+5ZPl0EURe2ZMnZEnB7aWy7jdv/lqo+ysbZEFLXGcfInrueaEd8NmAkcaB0OGSbOZOp3kxr3lNA2iZEbGU6YkhwEAHA2ynR1p0I3AMBClYkmBLIYUHSsEQRAEQRDkv+W/0yEcQRAEQRAEQRAEQZoEasRFEARBEARBEARBkG+CUmsEQRAEQRAEQRAE+SbMjIyMlo4BQRAEQRAEQRAEQdowTCaTtXQMCIIgCIIgCIIgCNKGMSmKaukYEARBEARBEARBEKQNQ/daIwiCIAiCIAiCIMg3Qak1giAIgiAIgiAIgnwTlFojCIIgCIIgCIIgyDdBqTWCIAiCIAiCIAiCfBOUWiMIgiAIgiAIgiDIN2HSNN3SMSAIgiCtCEGQkaFx4Y9i46KTM1LzREVlAMBTUdQRqlvYGTh3sXJwt2AyGc0QibhM+jT4zbOHMW+jk9OSckqKymgalHhcLT01i/b6Th6WXfp05KkoNkMkradOEARBEARpnTCxWNzSMSAIgiCtQmF+ydmDgVdOPSrIFX1hMVUN3pCxXUbN6CVQU26iSHIyCv/ed+v6uZBSkeQLi7HYzJ6DnCbO72dsqdtEkbSeOkEQBEEQpDVDqTWCIAgCNE2fP3Lv0PYrJaK6/lNQ5inMWDbEZ2oPDMMaMRKSpE7uvXXs9+sSsayOqzAYuPckz9krhykocRoxktZTJwiCIAiCtH4otUbqJDk52cLCQiL5UvMRgjSb/+YJ2XR7LSoqWzf30JPg1w1Y17W7zU/7ZjRWr+yCXNGqmX++eBrfgHUNzbS3HJptZK7TKJG0njpBEARBEKRN+Bem1nKC3Lj9UszbtMpXjHVKN8385OeRKKKo7C9txYmZDB05hrOzBBvMrB2bPdLqCuTUutjcry5mzWPPMRI0QzxVNe5v+iIR9jYBS00H8T/l0TQocEFfDyzNaBVey9//HxUVNXfREorNVcJhnK/P5MmTWzoi5BPNnFrLKfrvFJFfuuiVSAoA7XkcH13eeH0eC2/Wlskm2uvcrKIFo3/7EJfR4BKMLHT+OLNYQ0vlGyPJTMtf4Ptr6oecBpfAFyjtOrXQqqPhN0bSeuoEQRAEQZC2gtnSATS+1LT8qnn1V9GUbOv2YxOnsbt1bt90UdVFKUFJpPR8c1VFBpYjJWtbbMqzzMIy6gcbteaMrbEUi+DhU1wiAWsLul9PWlHh41tlYkhKwe4EYwoKWFdXis9ruSgBNDU1cRyft2Ung6d2+dDemBUrfvnll5YMqClt2rSpW7duXbt2LX9aUFAQHh6ekpJSWFgIAAKBQCgUuri4qKqqtmiYLaZARk1+ktlTW8GIxVJQwgEDLRajWEr6PMo45qatym7b8yyIikoXjdn1LTkkAHyIy1g0Zte+S9/zVJQaXEhRQenC0bu+Ja8GgOLC0kVjf//z8nJDM+0GF9J66gRBEARBkDaEsXr16mbYzIV0UbGcFiowAWDf+0J1NkOV3VQjqRYUlgYGv6r6iipP7uX0yc81WYZU/lKZ1bEE51EAcDVMcPF6rJmprqG+ZhNFVRciOfUiV8bGQE+B+dc7UUyh7PO/TupciZRWZuKPsyWdNRW+XmgjKSoqUldXJwjiWwqJjceCQ3BXJ9rNidZQA7kcE5UwRCV4aRkuleFMBuho0baWtCIXbt/H2SxMQ70Fmq+jo6NxHNfS0vLy7L5mwbwuvXrbdukRcNm/g6mRtnbDf6y3Zn379jU0NOzWrVtRUdGhQ4c2bdr0999/X7t2LTAwMDg4ODg4ODAwMDAwsLi42MLCgsvltnS8AI10QtYFTcOc0Gx7Ve69dHGaiHAWcNsxGRHZsuQSwkND4eQ70VAD5Wa7qbbR95qm6TVzDr0MS/j2ogryRB/iM3sNdW7YPcY0Tf8wbd+bFx++PRKpRP485O1AX3cmqyHXjltPnSAIgiAI0rYwVq1a1dTbuJRSGpBWejdLbKTIOpckSiyRn/pQ4qbGETRNa09BYWnQ/fql1jfC1XKLmPcfvTI30dEXtmuKqOqiWEZHZEl1uYzryWUMCqvxr4Ma568YkRqLEfC+lIVj9ursZoqtuFhdXV0ulze4hPCXeHIaNrgvIeBDfgEjLZNZWISXlWESCSaRYGVlWLEILyzEaZrWbEdZm9NPI7HiEkxXq7mza58xYy9evuzVw1MoFPbo3m3Nsu8HjR4rY3ITQu9Xtuv+y2zatMnV1RXDsBEjRgQHB+vq6s6dO3flypVr165dunSpt7e3qalpfHz8pUuXTpw4YWNjo6Ojw2C08CRD335C1lFQqpggsBUdBE/SZSONlBKLCBkB3oZKxWLY5KwWn09ICMqY30zdfxp9rwNOPz61/3ZjlZb8LktTR9WivX4D1vU/8eDc4buNFUlhXolYLHP1tGnAuq2nThAEQRAEaVua/Bfh5aTSW6llP7RXy5UQv7wo0FVi/M+tXaJIviwsb4eLuhGP1dQB1IxJA9DA/CRtk8uJVT+d2Lx+ooerdYsEhQEwScypHddIudZqUcKxTc5qADDcUOlKYlkzRvdNYuPxzGxsgBdJkPiHFIZMVvNVFZLCcvOZxSW0ng4xqDd19Q4eG49bmVPNGSqbx5//87YJU6edOHJYKBTSMikDx1UEqtmlpVUXk0qlHE5jjkXcsoKCgvz8/PT19WfMmNG7d28+n1/5lo2NjY2Njbe3961btw4ePDh37tzVq1ePGDHi37T7X3D3g2SanTJNQ4GI8jFRZpAYhsEwE6XLsWKahhHGioejS7yEzdd/pBGVisT7t15q3DL3b73Uc5CjEq9+FVIiEv/5y+XGjeTiseCh47rUd0iz1lMnCIIgCIK0OU2bWl9NLAtMES/tKFgXWrC5s9rhrpoL7+c9z5Y6anK2O6v/+Lhgi4eqIb8Fsmtcg1CamYEJqt/PLJcTqzac2LJhkpuLZfNHhdHAJLCXmbKX2V+fcsbXWolJtI1OhsUieB6N+QwmCBJPTmWS5FfClsmw5FSmgZDo14M6H8DQ1cb4zTiwGZMgdAwMF+zYPWXedxgh7zV2Ek3TCS+fO5iali8gFotnTp8qL0jRNnfc9fsfzRZYk4qMjHR1dd27d6+RkRGOV1z4WL58OQBs27YNAPh8/ogRI5ycnKZPn75x40ZdXV1PT88WDLjZZBZQftFlBE0XF8OOkOL3hQQAJGSTxSLY8qiQiWGZBc166acRXT75sDCvpHHLLMwruXzy4djZfeq11qXj94sLS7++XH0QBHly3+1Vv06q11qtp04QBEEQBGlzmnAAnhsJ4ruJkoV2gk0Pi7Z6qBnxmAI2/kc39X3hJS8yZcZ81hZ3tXX3i5KLmvxuyc9hGOCqZI23v8lk8h/WHcvMKmjmkCgabsWK36QSMWkEW46z5bgOi7nYRcXbTKn8abU/nMKYZNsYP+lRGKObG8ViYmkZjMq8Wi6H0FDs8CH8z/34n3/ihw/hoaFYZUdXksTSMhhMJnR1JR+FNetujhg62O/gPj1jk01/+208c7nHUG+Kpq3dux06eXrz5s19evUYPnTwnP7W/jvm5saGPnr0iKZbfjzzb0fT9JMnT9q3b6+srKyoWDFjUFRUVFRUVOUyOI6bmJgcPnzYwMBg3rx5RUVFLRRsE5ISdLU/BQrvZ6Yw2EJRk4kPsVBw0uQ4aXKGWChoMvHBFor9zBQUKPzztVp6P+rk8t8PW0OxNE1fPtkkkQRdCS8prl/Xns+DZ7EZeE3jwOOMelzZbKKqRhAEQRCkVWmqVuvbcZLgRMl8F/7W4OKfvQT6/9yLqMLBd3ip/XCzcJYL1lGX9VNXwcag4jU9+fqCJmw/lxMYTUNt48jQNMiIT5I3mYwoLCrV1mrW8ZAvvRSTBLbBS4ABcFmYRE7nlVEbbxbP7aI8wKKGnoQSGY19vW275RWJMLEEhLp0fsHHfuBFRXD+HG5tQ48bT5WPiiWRwPMI7PgxfKQvpaICACCT4QWFDH09MiwSiopBhV/7NhrV9OnTly1b9tuKxX19xyrxeG+eh3fq0dvOytzY2SM/5dXN4z+NnbuBJIjHoc8LxOSdv3/1v3Rpx86dzRRc06jXgAuGhoYzZsxYunTp33//PW/evKaLqkX4HMq10PqkH01JCS2TgrMhm03gZuqsd1kEAJiqMVkEbqfFDk+SlRTTPwZ8cpUhLkseMLvFhmyoo7hXKdXG4mZzGH0Gmevo8giCevsmV1wmj3yW3sFR28xKAwOgaZCI5SnJRW9eZpFkxbUDI1PV/kMtaZp++jiFJKio55kAkPohJ+5VSt3vLo6NSs5IyfPsY6JvqCKVkKEPkzJSRT37mWrpKJeVysNDU63tNNtpKZVfwsIwkMvIM8ejMAwG+1gbm6mJiqUfEgoinqYVFUq4XOaoSR1UVLlZGSX5ueI71+IfB0b39XZtWJ0MH23r3s0gNblITV2RycR/3/o4J6sUABSVWBt/6+PsLszLLVsx90ZcTK6hsWDkhA6uXfQT3ublZJWqt1MUFUsvn30T8yq7vKj61gmCIAiCIG1RoyW0d95IvKy55Rf3A2OkDxOkP/cXXH0lWdtTRajyyYhHKhxsa1/B2qtF09yV7PRYa3rwf7lVsrKPslC1qbLr1ByFFfvas6rcWZ2Zmuv5d9MAACAASURBVFr5WEbgSdktf9dochY10J6TmEUAgK0eKzGL6GfHlUrpC+G1TjwulcDeoJK5XsrNGGa9vU3AbCxomoaCworTQC6H8+fwvv0o/Sq/M7lc8OhMGxjS58/hkyZTLBYAQEEhQ1VA2ljSb9/hnRyaqdsthmE7duyIjIy8fv16aWmpqanp1rlTO3b11BUl/LFpEQb0mf3rR89eV5Cbe3XPMmWyrMfSYwRBMJlteB67eqXWOI737t27Q4cOV65cGTt27L9sRi4LFfZMNyUlDhabTljoMGUEKHLgf7dLOwpZ6izGyrPFeaUUANx7JddgMeQkfTFMsnmwiq7gk6+4pafbQHt+xOPYqk/baSn9emDQ4T3PDu5+hgH0Hmg+dZ7zdN8LUc8zp8xxlkqJ37c8VlHljprY8acdvZfOupYYn6+tq7x+e6+F0wJkUnLpmq7pqcXlqXV54XVPI5+HvAWA4DuJAfcnnTgUmfKhCACCbr67/3Lm2qV3kt4XZmaUPHo9e9bYS2+ishQUWcvWdTv7V9SkWU5sNuP3LY91hbzfjwyZP+lyUSFs2Nk7wC/m0b0PHRy112/vfedafGRoXN1T66p1smClh4V1u4XTAkiCAoB+QyyOXfSdNPxsbnZZ/6GWv6y7X5Av3rF/4KzFrktnXkt6X3j/TuLYqfZTRpwvEckwDEaOtzt2ceS6ZXduXo5rQJ0gCIIgCNIWMRulO+vZUElUiiwmiZjfV+l+jDzkrXSOp9KfgaXz+yoBwOeb4HFgw0D+Jv+SiZ3BVp+5opfyr1dLlw5Q0lVrhGGHa9ojLDVHserz1HTFz5apXkgzd/TF5bR/iITDAgB4m0QAwNukEvjixQ9VBh76ihjrSqooNVOX6QbUSWo61rcHUSbGiX9ubA8Px6xtaP2afmQKhWBjS4eHY25uNIYBQUKZGDPQI2/dY7rYN+vhsLe3t7e3L39sY2OzdcOKW6d2UBS94Ze9a5dMPbN/w5g5G55FRPsHPtNR5Qzu3f3khYB/WZIJ/xzuGj6/PJ6vr++GDRvCw8N79erVEqFVaPQPKZPA+VzgsjA9Aa7IBg6D1lRhuBqxZu4t6taeHf2BGGrHpYEOfiWzNmbN3Fs01JWjo4JXC4NJVH+lcTVK4W+jk6s+3fhbn3u33wXfTgQAGuD21Xg1jYovyaJCSYlImpEmykgTbVgeuG5br+37Bvj2PenkJiwRyYoKJADwy7r7w0bZVC287kHGvUoBAKChrExeWlLRFYckKKmELCuRAYBMShAEJZMSEjEhERO7Nj8CAPduBgEXYgAgPVW0Z0do+Vru3Qz+3PUUAKKeZ547EVVeeN0jqawTazvNiTOdJgw9W55XA8DNK3GTZzstWNl57ZI7F05VzEDx6O57IzO18scE8fHaH03DuRPRhqaqP27sERKcVFwkrW+dIAiCIAjSFjVOU1tkDLFlCv9CiHTT6VImC1YMV2bgMMbjS+krn4vN6aG43a9s12xloRpjhCP3wHXx+vGtugG2STFJzMuhYiYtdT7OqFuyTEgksha4V70eJFJQVID8wo/78+Y1Nm58zU3Q6emgqAjhz7CEBGzYMIrHA4kUVxNQYklzhVsTuVzep7sLhmEMBhafmj9u3oY9a6af3DzTZtDi5VMH/zGi+77j/gcPHly8eDGL1UIj3jc7Z2fn4uLilJSUlg6kkaWnknv8azjbBAw8PpHUVGBcuCsBDLPWZ8YnkgIGHv6aDH9dffyt9Mzq4yO2Qpmp+ZWPrWzbObsLf1oRVHWBM8de1rjiycORQ33H2Tvrvn6ZtXar19bd/Xb89DA3u/R2QHyNhdchkry6L9x/qOWNy28BICwkddWmnurtlE4diQx9kEySFAA8C0n985T39g33b1+Lv3ohBgAyUupReGXYvQaYSaXE29efdJgPf5I2bJTNBkZgeX94JhO3aq/526ZHtZV21S92zGR7j+6GN6/EQT3rBEEQBEGQtqhxUmsOgWMY+HTmnC6lMQA2EwMAdd6XVskvoo5eli4fochiYB/SyduPiQWjvtKSXF+KCpxRvt1ZNfXUzcjI+vxFgiBPnr5TVtYyaRyTwHEKO3xZYm6Ij+unoPzFiVpkcjohhSoopsVFIJEAqDRXlA1FEJ88Lr+/+nNqavD4EZaRAT160DweAAAhB4Ba75NvHqmpqVoaFW1TUjnhPG7J0J+OibJSBnRzmOvTPflt3LmHb3q6YvPmzD5w6HBLBtoESJKUSCTczw6Ynp6eRCIpLCxskaiajpk6a2p/bmLqJ5d+BDzoYMEUldFhr+VafBwA9DXxTrYsnmLN5+VPB9vArHhVR+S2sNEAgOzMipGx1TQURk3saNtR6/yJqPuB76utmJJURNO0rj4/4mnagqlX1m/v5R888dje8GP7Iz4WXlSP4b5FVUYa8+xtoqNX8Z+Dq/DJV/foSR3zB5Z59jEtT62P7HnGYGBzFruOGNN+2/r7j+59AIA1S26v/Mlzyx/9xkyx37zqXnxsbr2GMausEy0dZVGxlKI+aWTOzS5VVGLz+JzCAomOHu/7td08+5gU5Iv/90tIjaVlpBUDgKZ2xfXietUJgiAIgiBtUeOk1jZ6rOBQuac7a0wf7snLsuv35AN6fKn5rqCQ3ndSNm0410AbT0qlTl6WLRzPVeU3cq9mdXW+Q0ezGt/SUK+5efzW7bDklkqtSQynwUaPOXX4l278pii4ESyPiiOtTHENVczJnHXhhkyghI0cyFFq5EsTTab2PJnLBZ+RVEICmNV83FqATCY7eeLYxf0/AMDDp1EPn78dt9XBtVe/V3HvLiybcDHg3u7LT45snK2GSXy33WjpYOtt165d69evT01NVVau4RPh5eW1a9eu1atXr1ixol27Gsblwlr2mkcTYBH46zgyJ58y1Ku4OYWk6Fv3CaCw56/Krw9hAJCSRKck1TyKoEtHJotoA0P3Vz10ZaVyDMNU1RXLs+v8XPGL8PRx0+znT6phrmllHhsAioskAPD0UYp3zxOT5zpP/85FaKSy/vvAisIbGlXwncRrFytueB4z2b7qW2eOv3z1Iuvp44qOEjQNB34Pu+7/dumarrsOD140LeDRvQ9lpfK1S+9cPP1q+YbuR/x8Rvc/lZEmqvvWK+uktESmoFD9XxiThRNysqxUDgAZaaKls675jLNb+bPnqSMv8nJqSOCVeRwAEBVLKwqvexwIgiAIgrRN1X8CPn36tHxApsoHdTGiH/NtDB0eQQHA2CHs3Ez6/oNau0QWFtKHjskmDmMb6OIpKdQ5P/nc8VxVlTbwY7RJMQlMwMMHerK/sAxFwaHjMkU2tnIWd3hvtqM1q28X1qLJXA9H1r7D0uLiZgu23qqeSkwGSL54+cLM7ONvXCYLAKCl7lCkKGr+vHmLpwwU8JUBQFtT7eaxn7fNmViYk2VrbtLnx98X/vXk0E+z1DDpqA2nV2/Y3EJhNpyKigqHw8nIyKjx3QULFowbN87Pz2/Lli1i8SfD6aWnp3O5XBWVVt9fop6YBMbBsfdx9NNQsvzv2ROKx2SkpVImeoy+3dhf/jPQwjPS6JadcL6O3+HKvI+X4sJDU8Vl8u69jCtfIUm6chjwajx7m5SIZOGhaW5dDRSVWGIxsW/nk1WLbvcdbFE5H1XVwr+Kx6/Hwo+Dk8of9B1sDgCpSUWLp1+9ezOh1wCzyhdfhGdM9j6fnVXSuYeRcn0Krww74kmaMo+tK/xkWgITc7XIZ+ky2cd/bX4no/Nyy5SVa/7SdutmQBDUk0fJ1QpHEARBEOTfqnpC27lz52oP6oLJwKZNYD9/Qr56QWEYjB/FTk6gw0JquJ+2sIA+foAY68PW18NTk6mLp8kZk9mqKk37S7SkpGz7zpObthzfvPV4XHzy11doCUwSk0kgMeFL42AH3SGsTPEeXZgYBi8jqYMH5Jf8CACwMMVHDWOfPSX/wrothcuBMjFwOR/3y8aWfh5R6xF/+hSLjv74LpdDlYkxLqcFkmupVDpv3tzenQwH9/Yof8XcWOhoZ3F8y7xtcyYW5Wa7dnK26+x52j9o5PqTq7btdXZ2bv4gv5FQKFRRUYmIiKjxXQ6Hs3z5cl9f3zNnzuzYsaNqdh0eHq6iomJgYNBckTYTFoHbWTJ8BnAG9mSX/w3yYg/owWKQmLgQCnO+8icuxBgU1rKt1nX8DtcWqlU+LiyQ/Lrx4YwFnfQMapjjjsH8uDsOnXTnLHHb+ENQaYlMQYHpO6FD+esZacVJiYXUP9l41cK/SluoXv6AycQZVSaLZjCx8jEnMAxj4Hi18Se69zIxNqsYODA/TxwfkwsA3mPteHwOAMhlZEmxND42V0dfvT6RVIQddDMhJjp75AS7yrfaaSq5dzPcvT206vJsNiMrXZSSVASf3bQiNFSZtcj1+P6IjFRRtcIRBEEQBPm3qt6mQfxzUyxB1G90LCYLJk1j/r2H4rLAzBbGTmKe2kdyGVQH14+/h4ry4fQ+auREpq4QS0+iA/6mJs9lqgiauYWnlfbLYxKYAgfyUuDc24qaV1TE+nvjDGZFwDIZHR8Fc75nBt+ksjOpzDRgMbCYcDhXSgCA72Smhgqe9I42NG1dOyjUpZPTGJamJJMB5YOEOzvTx4/hBoa0UFh94dRUeP0KmzSpIg9nMkCBS8Um4AZ6zRqzWCyeMGVKZHKaj6PhpJH9qr1raar/19Z5E+dMXL73+MKNW29dvZb7+sDz58/9/PyUlJT69+/fhnJsZ2dnY2Pj8+fPDxo0qMY+4RoaGj///LOqqurZs2ednZ379+8PACUlJRcvXjQ2NnZ0dGz2kJsWi8Bev6BTkigt3YrPEUXB+ziqzxDGuzg6LfkrM8BxmJiJMf4moiUHgq7jd7iZrX7glfDKpxdOvcpIEy1Y0flDYoFETBgYqZTfQtzBUZvLZSro8mYs6MThMplMbOaYi4nx+QAgk5FqGoqLfuwil5O6evwVc69XlmZuW49Zpiza6wdefubZ2yQtudjMUkPfSCUjVdRroNmrF1n2zjppyUUdnHTCQlJcO+tnpBaXzywNAHGxucNHt5dICCUllqhYevr4SwCIj8mdtdi1tESm0U7p0pk3kWHpg0Z71D2SyjohCXrBlCsbdvaetcj13u132jq8cdMd1i65HR2ZCQAHTnvnZJU+Cv5g76yzefU9iqIFalz3boYAMG6aQ2a6yNhM1clV+OdvT/xOvmpYnSAIgiAI0hZVn3wrLCzMw8ODIIjKB3Uvi82BsXPws79SbAaub0mPmoGf+53mMGkLRwCA4jzw2w3DJuPaQkhPpG6ewMbOx/mCxp8+5/MClZUVly0dV99CmnmiFCaJifIhLxW6Dai4yfPBdaq4gBZoVCyQkgBW5gwA2skdO7UbHzkBY7JAXEZHPAAbR6BpuqM9lhBFG5g0YZANqBNzEzLoIcPSlBaoELn5TABgsWCkL3X+PG5jQzs60uUjZEml8DwCe/MG8xlJMf+5yVFFhQCg38RhvbqSzXY4xGLx2IkTFUZNGebsemfZfP+bD4f161r5Li0pBbnYXFPp+A8jJ84cu+LAqX5DButb2UY9vm/s5S0uEW3Yf9SMd3Lnzp1t4j5kgUAwePDg3377LSgoaPDgwTXGzGazFy9e3LFjx65du5Z/LgIDAyMjI5csWSIQCFp2PqHGn3xLhmEE5Cdh0n8GlqZpYOJYUTYkR9epBG0NminDWnDyrTp+h9u7Vh/PIOR+Usj9pGovRj3PXDbnOtTkcXBSZffsajq6mtW9BuzdzAEg+E5i8J3EyhdvXo6rnBE66X1hgF9MtbWO7auhq8WOnx5Ue8XB3aIekVSpk/w88XeTr3h0NzhwyltBkeXd60RacsUtN6sX3za1UBMVS29fjS+fnYsiaf9zr/3PvQYAmoZ7t97t2vy4WuH1qhMEQRAEQdqi6q3WHh4e1R7UC1cBfBdiF34Br0mgIcRYEowBNACI8uDKb9jA6bSWIWQlwt2jDJ/FFE+tDeQezYaLYywmaGliUQ8qfn5paWI8wccFSvJAVUADYMp8UAAs7hltZIPdvwCCdpimDg0AAjWsrB4TzTQTFR6twIW0TFxXiyguwWUyHABUVGDSJCo8HDt5EicJAAyYDLCxpSdNpipvDmWzKFUVIi0TV+DSfF7z/SRdu3Ytd+QkffeuANBj+54/juzp3xfjYDQA0OJiSUHOd1tOpBZROCUzNjTZNn/Gst0H21uatrcwZTFxOUl1dHG5dGj/wYMHZ86c2Wwxf4vx48fv37//4MGDHTt2NDQ0rHEZDodT3l4NAMnJyYcOHVJUVBw/fnwzhtlMWARm74oZmQCDCTxVrDCHBgCuIh4TTnv0xEzsvrJ6XCRWnEOzWvRe6zp+h9s4GGnpqmWlN/6MUFq6ajYORnVf3qqDgY6+er1myaojDpfl4dW+7st/Xich95PHDDr9w889fv61z6JpAeUzVGdnllSOpl6uuEha/lZt6lsnCIIgCIK0RY3WIbySgjIMXwLXtjIUeODgRZs60iW5cGMbo88cUtMIst/B/QOMoctJZXTf2aecvbDbB3A1bZr1z89yqghuH/i4QGE2beqElw/pxZJjDBIwCmMSQBThijwagJaKQImNtdyYX7XycCFuBLG8B5K62kRKGoskMQBgscDdnXZ3rzlaBoPW1SHkBP3oKaN/z2a9h1woFL58G6Pv0Q3DMAaLZTNrkc/hw1P16GGe9pL8nHE//jlh7oo+ffqUL3zkyJHw+3f7jx4fdOl80Om/MBZ7zs+/+MyYvXP6mLaSWquoqOzZs2fu3LmzZs06cOCAvr5+be3tNE2npKTMnj07JSVl9+7dfH4N9+W2dUwCe/MIPrzGRbngPox+EYSrCyE/jTZsjyVFQlnuV3Lm7GRQ1QFmi041X8fvcAzDBo32OPzr1UYPYNBoj3p12cAwbMjYLn/+UsNo5N+o52Cneg1jVmOdZKSKFky5YtW+3WAf65SkoodB7xvQ9lzfOkEQBEEQpC2qPtxOWFhY+aCylQ8aQEmV7r+UEr1j8FTokmy4voqlq49pGkNuPBa6hzVgGalcj5Fl/iva6cPotVTnkXRtf72nQMbLiuPFwTBMhuM0sAicEgGLQwNA8nNM36LV5dUAoMIDh/bknfssBk7p68nZrK/csMpmU/p6cgZO3bnPsrcl+V+cIL3RLVy40EVS+PLon5WvCEdMXH7x2Wm/G+N+3D9x3srKvBoA3r17Z+PoAgBBZ07cDLhydO/u/y1bIM3PIplfGum9tXF1dV21alVqaur8+fMDAgJKS2uYgLe0tDQgIGD+/PkpKSmrV692d3dv/jibAYvAGRTm6AlKLAynMNtO4DWeYhG4pTOmb4VxleDLfwbWYObQwsOY1f073Hty93plnnWhzFf0nty9vmsNm9iNL1Bq3EiYTMa4OX2+vtynaquT2Fc5Jw+/eBDYkLy6YXWCIAiCIEib08gdwivxNOnBm+Rhe1gkAb2WE3HX8Se/sgveY73WSZU0vr56oygpEZeUiFmsGn5cSqQ1zElLyMmSUvHnrzcbBgv4ml9agM+H9ChMtwOtqopnv6QZckyejXF4NIaBuAhSn+JOw4nWOU6bpRlVUgY37jJ7d5cbCKmCImZhEZP8bII2Bk6rCkiBCiEn6Bt3WTpatKXZV/LwprBp06ZVq1Y9O7LfYepsABAzGDmrdoxbOXfXiLG9e/euuuSkqdMKNPQBgMFRSE5ONjQ0PHHk8IQpU+nP960V43A43t7eurq68+fPX7JkiaOjo4+Pj4uLi66uLk3TGRkZ4eHh586di4yMVFRU3Lt3r6urK4fzpdnX2y4WgTFIwClgERhOAU4CADAJLCMKYgJxnEUTUszZl3pzG5eJK3IsBR5m2oV6eRlnsGlSjtH9yDbRIRwA+AKlaUsG/r7+fCNufdqSgQ1IkpV5CjOWDd656kwjRjJsYjdDM+36rtV66gRBEARBkDan8TuEV+Lp0l6bKjJYt++o13+xOkyQKzZXXg0ABYUlP649WuNbqSkpzRdH4/GYQt1ey2TPITymkslPcYqknXxobVtaUgjBm1mdpxGVw4m3Qk4dqLcJcPEau4srIdQhVFUIsQSXSHFCDgDAZAGXQylwKQyDtEz80VOGvS3ZInl1uU2bNq1evTrsyD7zybP8i6VFgOE8vp1d9XtttTTUd+3YMu2HtfM2b586a/bh/XuNjIzO/H0iJyenRcJuMA6H071790ePHp08efLKlStr1qwpLi6WSCQAUD5/tZGR0ZIlS8aPH/+v7AdeiSXHcYrKfQ14GY5TZFECI0UZYxGYvBhM7EHDDFIjaaIEqELM2bcitX5xHiNLMAMr0LGDrBiaKMFZchygxa6t1Os73Hty94e3Xz4PiWuUTTu4m3tP9mzYukPHd31462XYg+rDlTWMoZnWrOVDGrZu66kTBEEQBEHaFqyk5JPhWL5lhPBW4n1Szo8/nf3yMl9NrQ/tWWBp3rwzPtWNOB97soOt1I4y9KQUNSm5CEsPZ6Q+wZzmytvZNGFv8LS0NAsLixq7CtdLsQgeP2NKJGBlThkKaUWFjzGXibHkNPxNHKbApTu7NHc/8BqtXr16H6UoHTWFsX3t//p0nThxYo3LlCqqjJ63KC8rc8eCmSePHtHR0Wn+UBtRQUFBRERESkpKYWEhAAgEAn19fScnJ1VV1ZYO7aPGOiGrufEd22W+PDMKZyuBlh2d8gQDAGUtUGpHZ72quG6l50znxWGSiuGiQUEVVI2o9MiKTuDaHahnu1n9/1dDv5hvV5e9ru93eEGuaPaw7enJud8Ym66Bxn7/ZaoaDf/cFhWUzh62PfV99jdGwhco7bv0vYGpVoNLaD11giAIgiBIG4KJRKKqz8vvzSMIovJBy8T1DeQEueXXK7Fx6V9Y5gupNYZhttYGu36ZwWY38FbzZpD3mpEeiktycTYfNOxIHXcCb+Jg09PTLSwsql2IabBiERaXyEjLwCTSinSFBuByaH1d2sKE4im3WGP153766acDd+79Mnv6uHG1zt928uTJM+f95DiDlIh3bd9ma2vbnBH+NzXuCVnpQj9Fgcm3XqIqTMRG3CxrlHiqqcteN+A7PC0pZ/GYPzLTGj5auLae2m+nF+oZfmuvpMy0/MVjfk9LanhOyxco7fhrnlXHmse6r7vWUycIgiAIgrQV1VNrBKlRE2UyCNIw/80Tsun2OjezcPXsg28iPzRgXRsHo437Z2hoC76+aB0U5IrWzjn0MiyhAesamGptOjCzAbdY16j11AmCIAiCIG1Ck4wQjiAIgrSIhn2Ha2gLdp9fMmlhfzaHVfdtsTmsSQv77z6/pBFzSFUN3q4zC2cuH8JVqMcw+wwGPmKy56GrKxsrr4bWVCcIgiAIgrQJ/8IO4UhT+G82EiKt1n/zhGyiDuFVZabmndx7++bFp1Lxl+aT5yiw+nm7jpvbR1vYVFMp5mQUnvrzzo3zoWUl0i8sxmIzewx0HD+3j5FFU41x0HrqBEEQBEGQ1gx1CEfq5L+ZySCt1n/zhGy2vS4rkYTeex3xKDbhTVp6cm5JcRkAKPMVdQ00zGz0nLpYufewVVTmNnUYACAukz4NfhPx+O3b6OTMlDxRURlNg6IyR1uobmaj5+hu4dHLrnmmtmo9dYIgCIIgSOtUPbX+F4wQjjSF/2Ymg7Ra/80Tsi57jb7DEQRBEARBWkT1e609PDyqPWhO0puzTPr870MrGh+6GcmfrXZyXhPRtL+DycTfvczm3JIBgPTWbNNef7xvdZXdLPVQH9IrU4QDD6TVraLowpMj9XxPFX1S1VXJw1Y7uqxrrt37WJk1H2664KSP3qgzxTWv/cUC/83IN9u62Sy+35jzZ308MZpay36HIwiCIAiC/Gfh9KfkcrlcLq/64CuIV1s6a48+K/r6knUDAA1eV55yc+OYLpa6GupahtZuA6YeeCFrrLDqTfZgqbWAx+Px+YJ2euaOvcb9cORpNvHldeBLO0/lHB+u63uq8FsDg49b+eL2alrtq0tX3Wt9W68Z+54VUg0LsfZ3ibc7Pc3m3JLWv9yPRbza0lmNx+Px+CrqelZdxm29m0l+JaC6Fg1c56k/T3XmVqxW44r1qffq/qlhHp8vaGfQccCSM7HiLyyOGwxasXyQPvalzX7+8mcf6jK/8dquG1/KPy2wusY6ReukLiekLPSHDqazbpSVP5MEfWcmMKr61MJy4V1J7Vv4ctkPllqbzg+sPA3JlD8HaA/4M6X2E6lOn6CvqMte1/s7HEEQBEEQBGkM1Vut2zAq9fi8aWe5M848T05/H3F992xXjRbeO/UJfll5eZnvX9zeP8f67fah/VfcLfjWKXNbv4q9jg/e5vBq3Ywd4V8a96cFsQccSM7Py0568mf/3N0z198ubaRyORb9pvQ1r8fQxvWnPsEvKz8vL+PtrVXCwEWLjyXV3qCOa7qOHNmpXeN9EBq1QJIgG6Wc2rA6eLiQYY9fEwAARGzoc76p2vOQyqdhZU6dHTlNGgGCIAiCIAjyn9E0ySeVdW/rWHdzPW09c7cxW+5mkgAA4me7J3rZmwp1dIwcBi+/9L6ityWd93Cbr5Ox0NCm69TDUWUNTz2J+OhYxR4TRttrKbAV1I2cBs8cbseqZbvysNVO7ZcESypWFQVMM++67Q0JII7zWzHUyVRXW2jlOfPgC9G3ZcI4g8FkcpQ0jFy8V/19ZAqc2PTXOxIAaNGLI/P7djDS0TZo32/JuThJ9RU/C4N8u3faT49Lg390t7W167HxqfzroZIxf83u72xhoKNjYNtr3vFXZd+0K/VQvtcqJn3G9NFPfx1TRAOAPPXWxtHuFkJtXTP38Tsf5dIAQGcdGao79McdM4b09ers6Ob9892s6jni5yeS/Onmab+/yr00x8HWtuPwPW8bnpthOJPJZCsJu4wbbF385nUaCQBU7uPfp3a30dfWMXYcvu5GamW/D5WdCQAAIABJREFUZyL5yvL+9qb6+jb9ll36IAcA+ePlHTtviipfgny7w9N64T0ZAF14ylc46vSn/X7pkhcHpnY209O3cB21IyT/my+v4AwGk8nkqloNHdlV6UNCMgl09tFhwgkXxOXvS69OMxqwP5UCkD9b4+yytlr/bSL1+urBdgZCY7vei86/k9crnI8Fyt75Le3fwVior29k2Wncobeyz05RuvDZvume1vo6OsaOQ3+8/EEGUNHXetLmLZP7dHV2mPXzLGuPTS8rwqPSjww1HnY0vfHuUVBw6dwh8+mTVAqAygoLI/rMG8oN/+dpaJKNRyde7R/Gome7J3hYGQhNXcf/EVZUr1r68mdT/miZneusTct8+/fu7uLUc/bx1+JG2l8EQRAEQRCk5TRFak2lnpg39azK93cSMt4FrlA9P23uiRQKAFiGQ7ZejfqQ/v7hJosHi5ecSqMA6IKAFdOPK30fGP8h8tSw2PO3ShqcdzCtXR3lVzau2Of/MDq1uEoyUcN2WY7egxVvXnwsBgCgC+9cCBYOH2bJKH24bvTqhF5/hiWlxfrPIHZP3vCg0RJSBadBXu1eh0aUAJ17edGY38smnIlKTX3516AP66f99vKTpt0awpBazj28trOS5+bQ16+j7612ZX09VBrT7PHj+fCE9OTwPz3frpu3N6Zpmwg/RZYl3z0XmKJuYszDQB7927h592223H+X/i5otabfzGWXcsuPszzkYcn0kzeDHj3Y7RA4Z8n5rKqHv6YTieH64+GF7TWG74t8/frlpXmWjG8Lk5JkPj1zNUbBxFQLByrp6PRJZwRLr71NTwrd5RiyaN6RigZhebjfQ8udjxMSI/Y4Plk85+C7elWlOOTnKdvyx16MTXpzda74sn9q4+SOZEnidf9QysHFsl7zz1MpJ76bd9ts+5PExKd7HB+fedywbgXiwF9WRHQ59jI5JTn+6anvu6gzq52izILrKyb+Xjre701aUujO9g8XTP6jvL0Y6Oy7IYpL/R+GRx1eOXaAyP9C+elPpV7xi3YdNUin8b6UMA1XD9OYkLBiGsrCQ9937Dq8q2P6k4qnr/Tc3PSw2j6MdO61y9lTLkYnxV2bRe6dsiao7uOl1eFrhIwPeuOw88qd+6GXZ5X9Mn3788a8rRtBEARBEARpCU2QWtPZty+GWM9eNdxIAeMaDl012/bJxVtZNCjYD/ZxESoxMa5+vwUTLF+GRMkBxA/9AzXGLx1pqoBx9Actm+rY8IBw7dEHru3oJb61c/YAB2MTlzFb7mVSADVvl9lh+BCVOxcflgHQBXcuPjD1HmbGED/4+wJz7Jo5zuoshrLV6O/H8m8FRDRaf2ZcTUOVLi4UkXk3/75lOGv9WBs+g6nqOHPJwKLrN+Or5Gp1CaMOyzCt+o3qasxnYyzNLgtmuL0LDS9slt7oeceHa/D5qtp2oy6oLjnwQxcOyCPPnM4asHK5pw4HVzAcsHyGeXBA+VUNYDqNnerCwwBTdpwyzuyBf3CV1sHaTqTGIbs2XZfPF2ha9N8tGbf/58EqGJl46eTzTovXDjZWxNna3ZbM7xx9LbB8e7jQZ/FkGyWMpem5bJ7bK/+bKfXIjuUR/tdkQ5dM78DDme26L53b41v7IJfXMF9V12HaLeGiZYM1sXqsTGff8X/afsayPrpsTNFi3JIxJjV/5GSBi9ob/cP6u5vVcz+MxWIUJb58k1JMsgRmDlaf3X0hfnjhlmDCysm2PJyt3WPFYs8P/gEVnQwUe0yZZqcIAMB1HzWUDjj/TApAJl46/7bryH4a9dmbr2GYuXfiPX8cKZNHhbw0d3fi27vZRIdEyuRRIc+YLh42jNo/jMyus5b10GZhilYTl41SuOH/5PO5lfPPTrT4p4pMPNY9Kb9yUJfPL9ZuwLQR+iwApnD41H5FV6+8auGB4eji6DOrx3naGWm10zKyce0zYdXx8DwKAKSXJ2up8Pl8FYGaloG128BpP5+L/udDSoteHl0wwMFYS0NTz9y+x6j1NzIaetWIfL21i86Ycx+vX4gvTNBx2xRFlL+lzq+k5rg2gqht00TqrU1ju1rptdPQNrJxHzjt4Mtv+P6mCyOPfT/c1UKnXTs9Kw/fH8++Ka24Yb7o3s9DO7c30WmnoWPu7rv+WrIcAED+8HsbVT6fr6Kiqim0cOo9/sejYTnNeT0TQRAEQZAWVq/2rrqh8rLzWLrCitsxcQ19XU5eTi4JgkS/Tev2XHuZKcVwKM0t7VJKAl2UkyvX7SUsb3vENQ2E3zIrKMazGfHD/hE/AFWWHPzrzMlTVlg8PzZCIf7z7QIw2w8fquF9Ibikl8utC4+tvDcb4XR2eqYo+e5k18vl4dBSEelZ0mi9U6n83AKcL+BBdlqWJPqPYS6Hy3MIUixRHFn8cTN00dfDqMsyROqt7at3XHiWWkbjmDRPatlDTIOgsfamduqTLr35nyeZev+XyYvC4grJLupkZnpGwf1FnR9X3IEsK2F3KCnvnoCrt6vIyXD1dupUbnaVm9FrO5Ea5+5Y9sBDSad9mFn/Z+8uw6O4ugAAn5lZjScb90CE4BICSSC4JFAIFqBI0VKglCItUloKxbWluEspFClaKFY0SHAnkECIQEJcV0bu92PDssSNL8h5H5727ujZm53ZPXPv3Lm2ZuTww3eTNR0dRQlxCcoLP7fxma9dgssVO9bPFMACgLFztNXWNGVoZ2+c+iqlDB8LPiU53ba+Q96hZuDopKjgBS3FF/se/N5GQtSpD/ZM7Nt3jPO59V1LvbKQ8ipV7uCcFwPj4OzA3C9sOXGz6ceXd857XLDq6NiA9W/Pl7WZvnHYLwvHtZ0Wb1Dvs9EzZg9vbKo/n2QkJWtsWzjkdSuQOziap75K5gEAKCNLxeub0SUNQ7vJ++29/LOv1d97YltPbm9emZk1gKS+v0/uoosPn8ivG/t+YUmbNG5is+LiwyfyK6n1hzWSCUUcjCZAmdg7GGsn0jYOtjmPklWQ/6Nn1nnp6V8CtH9YkrB9UKezULpjE2gLK0VezdAKK/PU2LJ8oCqf8tbC7l02GA6ft2VeK29LJj362ok/1+67HOrTSQ4AjMe4/y78WA+UqbG3T26cNaXjqaiDR6c0kqvCZvaf/qjXxlM7mjtQKVE3zt6Ad3XxThK0KmJLDyMAAIoWSUSF71o73IZizs4bvWqa5L54cPF4TPkPNeWtRT27blaMX/7vZn8HIebsmoljPuufdWz3MHcRJakWPGn1gGoO5lJVzH/zh4381u3Kni/sAAAUA/bc/a01rU5/+eDc9nnTugbd335iYetK/lAjhBBC6D0lIqRiv4YIAZI3ZG3eFMrC2oI9F5fEEwcaQEiOi1eb+yog7o8J311vvuvEZh+FRIhd1ckvDAgBE0uFJO1VMk9MGQCSkZSi1hu9utwouVPLL/s0Wr7qQaymSXgh+yUEaK+uXW277D0VmbT3Sp3uixwoAibWVkZeI3aemvBWN+PyBaM3HrB2gvL6kVNJNfs3NKRMbCzlvkOP7v3irdZGwuYN4VxUGClAvambkkPNPPTDyMP2Gw5ea2Eno7J39/PeJOjWJvlKpX9XJSyre9e0zKHV5AU9WvWe/nenrSGWNjZ2oSsvzvV/a3QvkgiEj4+J5YgnA8DHP4+nFEFmQJLytlH4B4km2fpvoFx0H1pKat149ILh/7afurnHoeGWdtam7cefXfeZ4VsLqwnhE+JecsRRDECyX7zIsgiwoEiSVMxpWG0YQlZmtm5oQMhXwbSFpXl6cgpPqtEAwCYnZwgO5Q5f/3MlMa/Zo6fflK9P3NJ08ZGIgWVZQmQAoMzKZvWiAf1oKAtrc/X1pGyBSCgAPjk5VYD80RAChJKZ2dnbG2knKM1lFBB4a4OUVcCoZQGjQJVwadnQvmNX+p2dbPfWR9RSIU6IfcERhQgAlC/i0sy9FXReHLohy4Gp2aOHZffd/3W33pfcYWZLo7LVS8lLG/r61Xp+6Oghg+QGY6rThHZq3Eg59+ghyRPPAF8zoPhCD0b+AQiZL+IzBaKgAPiE+ERDK4X0rZ0RAKANLWzt7bUfaoGYSihCoMjjN13/gyEkxsSqSV0GADRxMa/MrS2oMrzxip4f82/u5a5fliWEbL44rY0xAADY1mg5YGbLvHCBAEXRDCMSGVm7B3w++0/TVL+v5u8dsrOv5t795FrdBrVyM6EB7Lxb9PEuf2wFvke0r0EbAAFaJJVKdVc2+JeF7lrz+M4jg5aretezlgDIXBp2Ht6w3OfvhD2zlyV027xvbEsjAAD3dt9vXPDEb+KCf3ut6WQic27op11OcKluZ6y5EvOKJ7bai4U0zTASA4WLT7ep2+xyAkNmbxkRONa9gjeuIIQQQuiDUDkdwgVW9ZpazVm269704br5h2NURBVzeN6aB026t7cBZXaOyNq9moUESNaNrbtuajtAypuFtI7bvfVaFgFQ3tvyx8Xy99/j7m6d8euBq09f5bCajMgTK7ZfM6pTz4UufL8AwHh0DXE+vXLi5msNuneypwFA3rJ/t8yNP62+kqgG4HJe3j3x3/3y3/sNAALPc5xGmfr8+oH5g4ZtgP5TBlZnKKugAW0fLfth+91UDogmM/ba4XNv3btbeBiUgbmZ+EV0jKaYZfQ2QjRZOZxFNQ9rGQWqiD+2XyjYnfVdk9Yb9lW9C0vX3SGN+vQx+XvWwtNxSgK8MunR2eM38u615u9vXbovWkXUMQcXbnoW2LWl6ZsMh7Iu7INE0aYWpsqY6ErraclUG/B1u6crl51VuXfrV/Pcgp8PRmbxIKjToi4evZz3NGshds9vfzzKIVzS+aWrLtf8rKMTLapWyzPp0rkoDQD/6sSWA8+KjEfcqEuQcGjrqWQBgH3215aTmRVNjASe5ziOVSbd2bnrgtrV3YWhjLxrOdw9ez6NACjvb952och7dynrtl197vy57ZEKgGRc2rzrXrkqkmTePvrPrZdKHqSmNlZGYpqhqXwf0ebd2qdtX7A9IoewiWcX/nrGpWvnwm6NZzx69HI9NmPirpxOoc0MyhNLsWjbpk2dbq1fd7e2fz0JAIjrNq0bsWHtVUWTpq4MFHMwchfWLT2byJLcx9uX/JXTsUuT0vaWKM1phGQdW7ksPI0X0q8t/+2oRfBntd9B/6HSUl07Gy5v1bWZcWkWpsxbd25GX7t8h6WtGzV2urVm0rw/jl99kqT8fz77oIhdi7ybNGQPzZ606sCFe28Nt1F2qqtnLstadQkw0k2hLNp2aaY+f/qO9iuKf7y8i7eHm6Nby0Vxrb7qXbOwv5+8Uac2VvcvXy/9bfoIIYQQ+qBVSmqtOTqqus1rXmNPWfVfvq570tw27vbV28x5FbJ2+UAnmqne/6dBmXPbBLTr1GXIVknDetqfIpTFZ/PWdH44rnVgh06hi9VNmhuXu+scbWYlurd2TFBDVxv7Gh2mhLtP3javs6mo8P0CADDVuoa43zgb4ds92Ea7V6MWv+ya5Xnm20B3e0f3Rl2+3/kwB8rflY+kbOtla2ll51a/w8hV9z0mHjw6v405BUBZh/y28xujvwb5ujo412jae+bRaNVbv0sLD0PWYthoy7+6VHdybfxDGFtSqJSi2+TvTTZ28msd/NnnS7JqN5L///sk0vY9R3fL2rz0cGbdiX8uD3z0c7saDg5udduPXncjLa//q6RVaMNTgxu5VW8+I6Hb6kW9bPWjpB0L+SABZRX01Rfsr/4uTm7tlzyohASbMmk3qr/R3iV/xrkM2bCpd+by7nWdHVxqBg5aej4xb/hssU/PgPvjm3m4Nfrqis/ilV+6M0ApQqb95Lw3tGnzNp3GnHYMrFF0u5Q8YNq6L7PmdGzWrlPIDxG1WjpVrAmLpGzrZatQWFo51um+Iq3TspUjPBgQ1R81JzTup5Z+rdqH/sr7+BX956adBi5bVO/EoMCWQZ2/2Gba3FdcriiElGtrR7au4ezoXPOzrcbjl47wZt7+iHLmnedvHiXeGFLD3qXpt7f8lm78plah+SPt1LVXrecR4i69fCtyO0hRRDUCfA1S0l2a+mhTR7lPU4+MZOLjV1cMxRyMtCL4M8WGbnVdPYNWkhHrZ7QtVeIJAKU6jYjcu3dUz2/v7Vrn80POP63/vtE7fVpb8UhOahpr/rqDOndrftsaHh4eHjVazb5WaHYqMlcY56Rl8CDx+WHfXyOdH2z7oV9gDbeaHb7dfr9Coz5qTo6r4/ZazTHHNIXN8ui0LKKoXdO2fdYcXtRWdXzRV8ENq1X3/XyedriNsiM5qalv6iSPWGFpnJ6Uoj3lMNWGbD577uyxP2YN7dK8hqLwQ/r1+BpV2tsfIYQQQv83VEZGRslLoU9eQkKCp6dnZmZm5W2SJG4K8Tk96PHWbvLK2yj6AOUe/7rRdJud53+sV/q223fwgfx/YS987zPZbPuZqWVvqn4n71p5aKjXRKMtd39rJQEANjslLUd5/oeWM6y2XZ/rJxwcVG2W2+GL0xu8DlZ5eFiNsfIND39v++Z6AJt6969JAyfe73Xi3NQ65WmA5+/Pb9H2yoCzyz97fQ/C0bHN1tc/fv6HOtTbs8Qm1lZGersofNdCbsyZJSMGb7Been1zd4uyX1ZUHhrmNcFg891lrd90Vsg9OLTGBMPN95e11r8Qwj9Y0LpPwszwJS2YC9/VH6xedn9Zm9fz2YuTG/Z8Pu3hjt5vjUOAEEIIoY/Uu3muNUIIlQ4Xt2fjCee+n1dln+hPmtQnsHHu6UMXtf2WxUYKaxtrMwO68HyUpJ/+J0zwaVr3rc4OYos6vYd0tH724HEFniJGyczt3zCXUYXOeiuvLnLXtIFzqxF9GqkiHsSVq2OLzCfQV3n6YNibvtwk9eTB8+AbULdgLw8+KTo6q5AO8cobR08l1WzayKjgLIQQQgh9jDC1RghVFe7GrGZufvNzh88cUh1HeqoitF3o1NGWe0YOnHPwVnyGUp2b8jT8WpTezdNE4DmOU+ckRV3c+VO/bw/ZjPyuuzVFko//OnPL6fvx6SpWmXDjz3VHXnk2qFk54/eXoIhdFz7cRrk+V5Rdr6mjrf7+ZvhvZ55latTpkScWDv3uqO2IcV0sKeDu7Ft/5MbTxMzs5MjTv0/fGOkV0Ph1y3ih42tU5ntHCCGE0PsLG4pQVaFsBh+IHVzVUaCqJGo47ULstKqO4v9M3GzB7QtVHcRb5I0m7TvstGDe4v7+wxNyKFPH2v6fzd441lcMoAbgn/zaxvY3RiwzsapWOyB4zrGxPWrJAUBuaZq2YWbf6Q/jMwUDG6+A0NUbR3v/X9JIqvBd07FWonvLx6waHZWkEpm7NOgwedsvnU3LOciEvOGkvfusf/llUvt5kUlKjrZpNXnH7gk+cgAA8urs4plzH8al8wb2tVoM2LR1TE0GQMgbB+EPWiQztnKu6dt+4sEV/X3wyVsIIYTQJ4NKT0+v6hjQByAxMdHT0xPvzEfviU/zA/lpvusqp3q4um+3DY6LD/3WyRY7eiGEEEKoKJhaI4QQQsUgaeHbN1wx6zK8s+e7GMYeIYQQQh8FTK0RQgghhBBCCKEKwd5tCCGEEEIIIYRQhWBqjRBCCCGEEEIIVQim1gghhBBCCCGEUIVgao0QQgghhBBCCFWIiBBS1TEghBBCCCGEEEIfMGy1RgghhBBCCCGEKkR0+fLlqo4BIYQQQgghhBD6gFE5OTlVHQNCCCGEEEIIIfQBEzEMo3uhf991UeVSLlZ8+fLly02bNq1g6AihT0RkZGRWVlZKSkpVB/LBUCgUpqam1apVq+pAEEIIIYQ+FaKqDgAhhIoTGRlJ03SrVq2qOpAPzPXr16OioqpXr17VgSCEEEIIfRLe29SapO/oW+9gyJ0dfUzZsEm+U8y2/Tel9nsbLUIfv/T0tNiYmIJPFKAAnJydzczMi1oxOyvr4cMHhc7y9q5pZGxc/H6zsrIwry6HRo0anT59OjY2Njc3Nzs7u6rD+RgYGRkZGRk5ODhgreqqoqoDQQghhN4j70eyyj9Y0Kr97TGPtvcyej2JkvsMnqGoJq/KsBBCb8TGxChVSlsbO5bV8LygnUjTVFpaWmxMTDGp9ZMnj8VisZWVNUVRuomEkKSkV0+ePG7QsFHx+8V+4OWWkpJiZmbm5uZGUZR+5aNyIIQQQuLj4yMiIiQSyadcq7qqiIuLc3R0rOpwEEIIoffF+5FaF0bq0WGQRymXFXieYpgSfuLwHM+ImOKXQQgVhQDY2tg5u7iEXThHUTRFUYLAM4zI0tIqV5lb/LqWllb//nvEy6tGs+aBABB24dyjR486dAhKSnpVql2Tgo3lqFQcHBwoihIEoaoD+RjQNO3g4PD48WNtXv0p16q2Kp49e1bVgSCEEELvkdI+1/rKlSsikUi/EB4enq9w9epVsVisX6gAkr6jj0vfnRl5L4Wk03N6+nq5uni3HrX1gRIAyKtN3Zx7zlgxuksrv8YDtr5gH20b1cm3hpujo1vd9mO23s8FAOAfLmpVe/Dc+UM6tvD1GbN9S1+Pz9bG5v0a4h8tauU1/FBmxeJE6BOi0WgunD8b/ezZs2dPo6Iinz19KggCL/ClWdfbu+axf4+cO3v6YtiFf48e8fDwfEdBCtlRbMxmLm4DG7+Bi9/AxW1gYzYL2VHvaHfvuU88A6xcgiBQFKVWq7FWtVXxKXeJRwghhAoqbat1QECArqBtQfL399dOKbRQya1MXOT+4113HXvQkLr164A+wxbVPvNjAwDQhJ1O/nv/fyvMKALcY+uWU/6a5+sqT7+4oP/AMat9T4yvAQBC0ulL8sN/n61tACR935k5q/dHDx1TjQHu/t59r9r/3MakMuNE6KMmCDxNM11DutMMo1arjhw+RNOl7RDrH9AMAI4eOQwA7dp3DGzRMi42trLjY9mn+ygDC7FjP0GdyUXvJwAi1xBaasIlnOUTrourdQO6glf9PjCl7LEsSkihc9XFLCAYSDlbRSUF9QHT1ifWKpS6EhBCCKFPR2lTa47jdAVt2qw/RVtgWVZXqOTUmjLt+PXXPuYMQKNRY4PW/XDo3pQGjgBin/7Dm5hRAECByKtDqBcAAFgHjBnWZO3ua+mkhhkAZdBy0JDaBgAAlFm70DbTFu6PGjXeU7i190BO0MLmBpUZJkIfPYqiJFKpRCKhaYqm6feopzbPqi6tl9TvThvZcDE3BGUCZVgTALiYK5TcVuzclo2/pby0Ud50CDCfVnZdIiYhVXo1grdTUKnafkIUAMlXEKtZ0rgGb2tRZVF+aLBWEUIIoU/Ne9shPF+Ylva2eRsU2dpbpielCgAAtEJh8fodcHHH5w3p4Fu3Vu06dZtNO6tW5ioJAABlpFBIXm/HqGVoh+T9ex9y6vA9h4ROvfxklRklQh8/QRBYjYZlWY7jBUHgeZ6hSzWEwcWwC0ePHG7XvmNQcOcTx/89d/ZM5QamuX1e5NGBexrBJ8TySSkSr2DawI02cJN4BQtJKXxCrJCYIq3TQ3XlWKXsjo9cFGA/9IimIttQ/zPErtniqCrvWEzlqjVSGU/RHIg4EPFQSEEjlVHFNsCifLBWEUIIoU9NaVNr/Q7h2kLxHcIrLUAtIflFQl6TOJfwItnMyiJ/4JmHfxp9xG7KwfB79+7euTinlVSvNU2v15rMv3dn5cG9V87uOSLt0rORBBBCpUbRNADcunXz+rXwO7dvOru4NmseKJGU6jh6+PBBx6DgwBYt/QOadQwKfvLkcSUGxr9KZmNzhFReyGQ0tx+CSk54XnUmTHUmjPA8qOSa2w9BY0hJjbnoDP5V8tsr353ZUMrImy2OfH3TuBD9eysDRlJ72nWuqD3SVm3Gz+5XqxT9fjQXJ3pZhe5M1Wvf5x/Na2LWevmLmv1mj2tjVeXdanmK5g3kvLkJbyDnDeScu2PhBaq03xcI3t9aJRm7BngO3JVR8pIIIYQQKpvSfqlzHKft+F1MgWVZbZ9wXaFMBFb1mlrNvt2QQzKOrVx1PV0QMm6uXnbULLhz/kdcE012NmdRzcNKRoHq8fY/w4pqCJA06tWV3vvd9H/Mu/Ws+/6Oj47Qe4cCSE9Ls7a2trS0NDe3UCgsbe3sYp4/T0h8WWJymJyc1LFjsJtb9Rfx8S/i493cqnfsGJycnFTKXZOSaO7HUxIbLjqNaAylgYHiBo1Vp26L3OuJ3eupTt0WN2gsCQwkKgMigMilvuZ+fL7VAUTVXJJ3//WQI4QQwj3Z9VecS3V5cfsFk/rdB7VypkoMjYgb9w21O7n9YKLwegp7d+fOJ/79uru4th7cvZ5xyW+vvEpZvQIwGlrM0iINLS6mIECh3RNIxu0/pvRtUa+6s7NHg9YDft7zKOfNjtkXZ1d82zOwnoeLi0c9v3Z9vl12IlpVyrAqjI/8Pajut6fK/F1UOcpfq/zDxW2d7L2H7H2VV5HKA8Pcg1dEFTlcoObY196frYou2P2BJGzpVb3r2hj9OZS84cAfBzYq/F4oNny6f/NfbhZ5RQkhhBBCxXh/WiE0/472sHvN+9uTb3W0FLl3bZs9t713tbp99ttPXfddgeZmShEy6TuTTZ/5t+3Utf+SrFqN5EX92BfV6dndIvKpS7duXvgkLoRKz8nZWSaTKVUqtUaj/adUKtMz0uUyuZOzczErenh4siz74kV8fHyc7t+LF/Esy1baOOFqA4q2oGgLYI0omVR5+ImkcQ0+huNiOEnjGsp/ntAyKVEbAKGANwR1wbRCVLdPD2HPX7c5AOAf/LU7p1ufRhLtSUSI3juuQ30Xa3NTC+fGfZZc1DY/85GLmzkMPaoBYM+O9ag3bM6k7i2aNvB0b9R/3b18uaOoXt8+7ue374vPS3DYGzt3xbfq39VWc2SIffMl2g7hQtK5RZ83cVWYmNp4B005HMMBSd7SxSZkWwoB4K5Pqy0z7707EwDYsIk16hXTnF4OPEVzFAMAHMWOfLchAAAgAElEQVQUUyi0fVV157d+ob/GN5u5/0Zk5NU9P9R/OKvnsC1RHACAkHDo265f7hE6zdpz8X7E7TM75/f3jD50/GnVJ24Cz7/zQQIqUqsAlDFcXrbmxrvoLC5xbzegbXUcbwAhhBCqdO9Huy1T8/tzr77PP7XD874AACAOmH/zAgDAmJ/05lLWg/c9H/zmpVGjUVsvjNK9nqj9n/fE03fzbZa2crA38QnsVh0za4TKwMzM3MzMvBwrGhkbN/ZtUunx6CMaA9Abr1hkb0NUtNjbDgCIihbZ2eQtQ2iikRNNIWmFqGZor1P9d16d1tDn9o69TM9NdSM27dFuG0x9x++Y1tLLLPfm7/27DJrV+O6S5tK3VuYiTj6Yeu7kfGcSsaR9iyk7uhwcbKN3cY/x6v15vQU79jz78tvqNKgv7diT1mFhsEJvCeHZugF9/qi+4lRMV6fMc7+EDhi2tta/I/yau/949qp6QPtXFy+pnRQ3z93Q9GoeHXZJ2WR6aXqilxpHMSpaSiixmpYCQFEFGcXk3ytJ3L9gVWLXNTtHtzACAKjeZvzqWVEtp/56svvvHeXXV8855jDx5NLBrtqTrUvDzqMads5blY0/uWTq7D8vPc+RurQYPmfBGD8FBSC8Ord00vQtF54r5c4BA3+eP66lDcM/Whr0eVTIl9zelSejSbWQGWsmWv393dSNl16Ia/ZftH56WxsagGTd2Tb9xxVH7yUJFnW6TV48o4fLnYUjV9xPYcf6XZCJPIZv29bt0ucBR+tNsL7x960Xdv2/NVu5xmHr8e/riABAeLm1b5sjnU/9OcCu0i43l79WAQAkzYYOfrF5yZ5h2/q9HVKBemvK7x4z+Uhm6sluvhsYadt5p+e1LfYODZKxa2Djw59d3RpqknZ5+YSp68PiVCA2qRa64K8p/OxRf8YmUoOaHhBLfL7fvyrUusrvVkAIIYQ+IO9Pq/X/iypi2+brvv27O396bx2hjxVhZUQj1f4DAHFd85xtKexdjr3L5WxLEdc1BwCikQJQhJUAW9jwhYx7z15Wh3dezLi844B5aG8vXb7DuLbt28FbIaUZ80YjvwlWXr4Unb9nLu3UbUR3ZzGApHqblg4RtyPytcvSrj37Nbm7Y1cED5B77s99ms7925nqzecj92y51vT7WSHVDWmJXctJ45rf2X88gXYL8DO8cu4um3U5LL7l+CH2Vy88ZlMvno9sEOjzdmpfQTzFcJSIUBRHiYop8FSBy5Hq6+euSlsE+xvpplDmrTv5q8PO3mP5Z2FhCbU6BTkXdhGTvb98yLgLNWYeuxd5759JVvtGTz2UQkCI3zHuq70m3x66G3Xv8ASzfaPG7YgTAABI6olj6QN2XH94aUndSz/0HrTd6LtDtx+emWpzcPqamywASfln0hcrc/tuufL48ZV1QTGzR/5+Fxp/t2p0LcVnv126du3yjhEeDACoL51J7rvx2NlLW4aF9OqQfWj/XRYAQIj/Z9+9xt2DbCvzW6H8tQoAAEyNL8a3ebRixRVlCfWWahX6+7xgk7pj94VfuxZWQl79VoBRf8zcJBp15O7jiIfXDi7o7i6VtJ218nMn1wGbL1+7dnE15tUIIYRQGX1a+aXm1PjaHkF/WE36sTv+aEDo40EEQiDvHwAQBgTIu7VZVEtOGAAA3QJC4TchM87d+7j+u+GX9f849u7p9ibfIUnnlgxuU8/dxcWtmveQPak52bn516dNTE20ZxRKLBFpNGyBBexD+rd4+tfOO1zWf38eFoX0a2GoP1t4Gfsy99wPAbVq1qxZs2ZN38lhYi47QxDXC2ycGhYWde38ndqterRuqgy7GHPl3C2vQD+zSj1/adtXNZRERUuLKXAFkkCSk5bGmlkq3voeESsUxhnJqYKQlppBKSzNaQAAknFgdKM6derUqdPwq72phL29Z/erDuPHN7eV0DLnDuMGe5z/55KSJJ06cMVr6PefucgoqXOnScO8ww+c1N5vLGsx7OsAa7HYumVnfwnXoP+QemYiiVNwcP2kBw8zCEk9seOk85AfQmuYMCKz+kPHBGUeO1HYrcmShp8P9TWlAICSNunZmRzdd10DwD87tO+Jf/f2ivejVl8zaz12uNW+xTuev3kjhddbOQOkRGKxJvHx/agkNSW3q1PHqVKv1yCEEEKfoPejQ/j/i6TNknuxS6o6CoRQJRMoAQRC0W9yI3EduUFHBQDk/JuSt4yGV55NJzwQKPxxV7R9177ek4YeDFi7wJGG23lTNWdnDloN8/693bOaIa05+ZX7RCjHbbqUZecB7b774c+TXnFHTXse8H+73Zy2trcxC5ocvi3E6K3ppEmLug/3bzr8yrbZOEU9M59nW9f9e9fEb4RL5V4T5SgRSwuEolhaBABFFTgq//cFZWBuLk5PTnmrPtmUlCwDUyOaNrMwJXdS0gQwpIEy6TjnXz9l3LbBPa+pBBASXySkXZjU+lJe13w2R1I7J4dPTUoV2TtYad8drXCwl6Qmp/DAAGVoYZ6X+EmlMgszc+0fWiKVUizLEiHjxSvlg1V9mm/RLiOoVPJumQIUeFw0baEwf1134vrdu8iG7A//wcfy4L64lhNaV+71ivLXqg5Trf/4zpu+W362c95zOYqot3LeNk67frFwVvKC3wY1G610CugzYcZ3QS7l2xJCCCGEAKBMqbVGo9E+ZUdb4HkeAGiaZllW+xRrjuO0D7gupSdPnmRmZqalpZUxZoTQx8bc3NzU1NTd3b3QuSUOdi3zMVJeySA0Ba+HExeIoF1LWyCECCAQNVFeyzQf7vjWBgkQot2HVfdfjzm+smxsDURNAAgAIXxWptKkmpeDAUU0z3evP5LEDwJCiDam18Nw64pv/ps/YtN2/Tt9O3zU1Fy7wWcaivIWy1uU9ug5sPbyX6b+7f1LF08jLjXq6sUkl2B/J1rRtJnr94u22n111ImWGjf3Gjp+O+m6rTZT+tG/S0QIvEplVJz8+W3O1EoOhKTHA1CMSzVaAFDSDADIKUZJMwZUgdxT1rBZI9XSIxezW7bMuyhA0k7/EwY+P9URMSb+/jYrjh6PGzLEmQZKamplbaw01nZXpi1trO16/HpmZlP9295JopUFF/YiWQB7GkBIefFCY95IwUB6Se+BVthYGvh8sf/P/m89yYyP0tZxEUTe3UIUff4+G2J9MKXtj4HGpaquUqpQrb5h1Hz0Vy5BS7faKLSvC6830ABQxb3Tosg9Q6atD5nGZ0Ye/HHAuFm1A9YFAQWkPNeOEEIIIVTWVmtCCEVRVGE/Bcr6U+/JkycMwzRo0KBMayGEPlbPnj2LjIwsKrsunshZptoZDzQldpJrp+iapnUFgeMMfRRsklrsXNi91gAAYODi0zJfy52s3aQ5fw8KbXDY1s7ctnHjpvYPyxEfAIBRq35dpVvXO4/oXXAMMqb6iD92qL6b0sljSDInU7j5hEz5LQgAGI/m/qYzY5o0ry0Cyty/mavylCLQV17OCAr1MoGSmTAObhR4izKy8s7tJkbC/QeUl7vAUtr2VWApEUdRkK/Bn7Lt9v2X23pPHO26ZEZ/P3sSc2HDD1OP2wzd1UlBAfh8NblN50mDJzAzvu7U0NGYT30Sfv+VAAAgbtirp3H/eUvbrBgT6CBRJ0ddvZ1bs3V9qzZdfOetXny03Zxg61dHF61/2PjL36ypklNryrJ931aL5//8V/1ZPWubkcz4u2HRZu2buZmYm6rinifxYF9op2umekh3586zp0hVHX8LKPxhVOVVoVrVQzv3Gd9t3dANjzVujaHIejMxNTd+FROnArfC3oXAadRqtbaxnqLFuqScj7lwNNauuW81U0MLGzMZraZpoM3MjdPvxWaThpXcho8QQgh9CqqsQ3hmZmabNm2qau8IofeNhYXFqVOnyr26rKGJ+km2rL4xAFBSmsvMe5wxl8lSUhoAQEJxqRqxS4G8mqn94/UCfWeknTcnagezFnkP2nhlkG7G3Fna/7tPuBCnLQX++vi6bls1p155VlSELZdFqZfp7yN4Q3xwXpm2ajZu8/lx+dcRN18apVqat22PiRdzJxax8XJjQURLIfolDQDpmXn5lKsDz4oYluJUtAgANBSvohmO4gsmgfL6E3bstJo3b9pni54mKznausWEzdvHNpQDANB2Ib8fMF294PfJITOfp7Nyy+oNWvVbM7u7JQVQ59vNS+Gn2Z3rD8kCA6vqvj2+m9caaIe+S1YkTJoRXHuMUubkP2D5kn6ONBT5OOc3KKvPFm7JmjVjRPMfEzViE4dagcNntgPKssOwfru+b1VjHl3j6337+hRYjXbs3L3m3CkxgxY2ruT7jCtYq3rkTUZ/3WDXhHPaV+JC603csO9XtUeMrO/JSjv+em1Z0Ftvhr06s5nbzLwXknbL7vz+eo4q+ujsiZMikzWU3KZBz4ULOhgBUz1kRNvD05t4fidqOv3U1n62mGAjhBBCpUepVG+ewarf8qxfzsnJycjIMDAwUKlUhBC5XJ6bm6vtBK7RaAwNDXNycgBALpdnZWUpFAqZTFbUprTly5cvZ2dn9+zZ812+NYTQB2bPnj1t27bNN/HkyZPdunUreWUBcm6lsdFKxlJs5K9Q3stSP84CAKmnsby2SfbFFC5JTQRi0dMRPpl0Yd++fS1bttTevFOU5y/FABD94s1lVjNj4mrP3oqQeLmyz1+IAMDWkk9IZmwteRc7tphNqR+tG9hns8PcvYsqd6ztdyj31LiAX2y2npxcp5SXmRmGuX37dr169f5vtfre0lZFo0aNqjoQhBBC6H1R8s+JlJSUCRMmKJXKkSNHrlu3juf50aNHL1++3NfXFwDCw8O//vrrFStWqNXqkSNHLly40MLCYv369TJZkV0uEUKo8tFg2NAcGpprYpQZhxKIQCgxDQDqqGz10xxDHzPjQMuqDvF9JJcKj6LzP7ApLZPmeMpWwdEU0c5Vs5RcWkzjKgCAtMawNeuNNoffeKoOdv8Qhpvm4vdv+c8pdF/NSu++VYm1ihBCCKEPRcm/KBITE5VKJQBs3bqV4zgA2LRpEwDcvHlTu8CmTZtYls3Ozl61ahUApKampqSkODg4vMOoEUKoCBJnucS5Um9H/qhZW/AAGqU6fzNzs/pK/bk1XDXWFiV2zabMfPp+6/NuAq1k3K35HUM3pNUbtXpgtaIef1V+lVqrCCGEEPowFJlaazQabedtKyurkSNHbt26VdvrGwC0BZbN68OWnZ2dm5urLRgZGY0cOdLExESbhFMUxTCV/6sFIfRJqcQBsVFB1hY8FH1Hc/FzP1ii+pNOPp70DnfwSdYqQggh9Ekr8oY4yWvJycnr1q3T5dUFaTQabSINANnZ2UuXLs3IyGBeq/yQEUIIIYQQQgih90lxHcK1D9kihBQ/Xov+QGgAIAhCUQ/oQggh9P/EcVzxJ3BUJtoOFFirgH1JEEIIoQKKTK3VarW2YG1tPXr06E2bNmkbrk1MTMaNe/OEmLS0tMzMzN9++037O8PY2Hj06NGmpqbal4QQkajKnu+FEEKfOJ7ndb2KUGXBWkUIIYRQQUXmvVJp3gCvUVFRy5cvL3QZlmUzMzP1p2RlZc2bN2/dunVGRkaVGCVC6JNF0x/Ig5zeP9bW1pcuXarqKD5CWKs6J0+erOoQEEIIofdFkam17pK8oaGhr6/vzZs3deOWaRFCUlNT860lkUiaNGkilUoFQdAug7dbI4RQlfD396/qEBBCCCGEPgn79+8vf29tpVKZ7y5rhBCqdLrrdFUdCEIIIYQQQoXQDjRWZGqtu0c6JycnPDw831xBENLS0gqupdFozp8/P3DgQOzDiRCqRNoEGyGEEEIIofeNtqd2kQmw+jVra+uvv/7a0NBQf25mZmahg7gYGxtPnjxZO4wZDvSCEEIIIYQQQuhTUKphzFasWKHrjZmVlbVkyZKMjAz9/pm6x5BkZWUtWLBgzZo1OIwZQgghhBBCCKFPRHH3WmuTZ4qiGIbRtT8TQl68eJFvSDN9NE0TQnTrVmq0CKFP1Cd1r/Xdu3czMjLS09MruB0zMzMLC4uaNWtWSlQIIYQQQqgYRabWGo1G+1vW0tJy+PDhW7du1T7XmmXZYvJqIyOjkSNH6p5rrU3L30HYCCH0cbp79252dna1atUqPmKFIAjPnz+/f/9+rVq1KiU2hBBCCCFUlCJ/ukkkEqlUKpVKk5KSVq1alZOTY2hoSAjJyckxNjbWLiCRSABA+xIAjIyMsrOzFy5cmJmZKRKJRCIR5tUIIVQmGRkZLi4uFEVxFUZRlIuLS6FDTiKEEEIIocpV8sO3zM3NGYbheX7gwIG///47AIwePXrevHlNmjQBgPPnz48ePXrBggU0TY8cOXLhwoUymczU1LQ0m6149Aihj0kxp4XK7RD+LIfd9DwzUcMxDEUI8ALxMpQMdjGxkJR8NTCnw0igKcrIQDrhC6ZpHb05LCW8AgBCWwOIdVP5y3fVi7eQ7FwQiOGxVSVuPz09nabposaAFCWlm2w9YXj6lig6MUFIJq72fCtf/osugrVFwYV5nheJROnp6cXX3vPnz7/88is7O5vNmzfrJvbvPyAtLW3lyhUuLi4lxlwMHx+fgwcP2tvbV2QjCCGEEELvv5JTazs7u0WLFqWmplpbW//444+CINjb28+dO9fExAQAgoKCnJycZs+eTVGUvb39nDlzHB0djY2NS/wdbGpqev369UaNGlXO+0AIfeCuXbtWmqtyFbc+MvNJtuZrTzMngzcnwDsZ6qm3U3o5GbexlRe/OqGAtjCVzRhFuzsBAJBckWYrw+6iuRsAGgAAkAiihrw4lJMMBMqAaVpHNnesavpKIaWi904bHbpk+eNmOkelfUkBRz2JoZ/EiLf/o5k5muvSsnybvX79ekCzFo8e3lMqlXK5HACUSmVGRlbbdh2uX79ewdRalPpgWGjH9bv+xewaIYQQQh+3klNrALCzs7Ozs9NoNJaWlhRFaTQaa2tr7d3UNE2zLGtlZUUI4TjOysqqlDt2d3ePjIw8deoUdlZECJmbm5uamrq7u5dy+cuXLwcEBPA8ryuUcsXVDzKNpHQ/Z5OEHF5CUYvvZPhbS9s6GtQ1la70sZpzO50WqFb2smK2QJubyH4eqZq+UvbzSLHbY3HuSEqIf3sRDc1dprnLIvVi1mAV+8xT9fMq2c8jVT+tKGWQhTI6eNH6u7UgFHbVMkcpmbiICAIf0rp8G6coAOpN1wBCCFBUpYxDaWkI39SKHBracQNm1wghhBD6qJUqtX4Xrly5oi3obtVGCH2yOI5LSUlJSUnR3mlSooCAgHyF0riXrMlWCgE2MgaoulaSHy6mja9v+s3p5KsJGkspPa6h6dR6ZhPPpzawkJjJih6H4qtetIez7OeRqulLpBP3UtVSilqSEuLpO4NVi3rIZoynPZwlX/UqasnY2FhPT0+lUlnUAkxCquWPm3V5Netqk9uiLkdymLPXqOcvAQAISH5aoWpSh9iV9vpmPmamZhUZHaOYt2AqA0iLxLZrhBBCCH3cypZaaxsxCu3sXY72jTZt2pR1FYTQB+fKlSv6J423mkb1CizLFtP4nO+0o7sVuah7kgu1617OKB/jiBQ20Fl2KU7tYSi6Gqfu72nsZy8dcyx1GZs5xtd4mLfx7gc5wxoYFbURJrARIUTkGmH63d7M+U1M5/zHWKgKXZJPlWUuaGI6aS/v2oknTtoViwmvmLlmW47TuWptOfPz1ik/9ScMAwDU1GHin1eJdv4LAJRSLd58QDNlWJm2rJvr5OysfXQivP2nKdNd7oUuLBXBjLYw/WTk4F4dN+46itk1QgghhD5KZUitteOB6wra9g1CiFicN2CPSFS2RP2TelAtQp8yf39/XZJGCgMAly5dKv0Gr1y5EhAQwHGcrlDiKmqOSHjaxpCxMWRYnhy4o5zZ3kREA0VRuSz5vYPFmsvZORribSn+62aRrcd5SK5EOYpySzGd89/9SPvDu5rwXP5WbkYkdA69UmvOf4yFilGOUhrdAsqg9G8wH4NTN7UFtrpd8k/94XXzMmEYzc8j6St36WfxAMD8Fw6FpdalcffuHY1GU9bTeClFpkDvunA3IbJX16DdBzC7RgghhNBHqMo6hAOm1gh9egrNqwkhZe32Uo4O4YmZgquRCAD+vaeKS+d71JSLmbydGoipS5GauET+UpSmrbdUypcQjEizVXt/NWOhOryrydIv52fb5B/b3CgxDdZOqrtoBwBQQrxIs5WTflXq9/c2QRDHvNIWc1o3gHzdthlGaNOEXv83ANCxCcDz+Rd4D9S0AQAwlEBNdWRQUNDRo5hdI4QQQuhjg6k1QugDkO90wbJsvkKJBJ5IBOroTdXTZO7xCz60oVx/m/UcRXXtTbacV7apIZFwdPFnJ4bdpSvzHJ1tY64yK6QDuX5TNsPuYiUjio+wyJ0SArpZdGFpP03rtkABFNxKoVvmOG7MyJEiQYiPi8vMzM7MzJg4cqS2OxLP8zEP7x9JTTaQy04fPszR9LJVq0rToF18vVVXwA/HYHKDyM+COx78B7NrhBBCCH1UPtjUmr83x99/dlzQhhs7+9hQAJC7p6/rssZhp8d7vHcNNgihShYeHu7v789xnK5Q4irWxnRKCmndXHLrMT+ileHKw8pRneXGcgoAWI6E3eGik/lODWU3IzlKU/yWWIa/WdaAGf4mAKv/vOuyrMywDpbi2CQAkJ+9AxN6gX4jPyHMuevaomBnRUrdZK3RaKhTp8Y7OdkYGICZCZiZQGLim9nV3PIKL1789PRppfQVpymY2xHuJUBPx6iBPYO27sHsGiGEEEJV79KlS8nJyfkmWlpa+vn5lWk7VZlaVxhtAmELl10Nme1b3KNyEELvmYMHDwJA586dy70Ff3//fIUSyaWUJpsyMaBHBMk3HVKHBEjX71MPD5FGvxCOh7NtfEVd/CV/HVd7ODOWRQ8PDgCU8Or186vLREMJrwjtUPYVAQByW9U33XoCAKSPYi2W7Ekd3zMvuyZEsngL9eiZdjGhVePSb5NhmFuC8FN0tLG4uISfJSSCkIoMHq5FCESmgIcl1LaFxGxQaKKG9w5a9xdm1wghhBCqYp6engcPHkxNTdVNsbCwmDhxYlm3U9wvyHetqLsuSwcISFqMHGGwbc6OOP6tAZJyrvz2eYvazjZWVg51gibsjVITQghR7R9o227amu871rSztKnVZf6Flw+2jQz0tLG0q9939Z1c7UY1MUdn9PRxs7G0dvHps+BcklCxGBFCBTqnHHzt0KFD5T57cBynbanWFUrDSk6fvcyZG1ODO0uPn+E6+0uWbtA8fMJ/Eyqr5y6KiBIENXX/gdDBV1ruwN6RzMEdiDQvATZbfdih+8/my/aJf9su6/ataM2evIWkYm5It9JvUyqVHrt710wkmuLoON3FZbqLy3dOTt5yeS25vIelpXbKdBcXY5r+5/ZtqbSidUIARh2WnHkKAOBiDpNbAJsSNbx30IsXLyq4ZYQQQgihilAoFBMnTrSwsNC+1ObVCoWirNv5YDuEEwJARN7DJnfYNnnphV4LmwMBkpdbi1y6zj+yuqGD6MWRKT2++vYP332DHWhCgLu679KAPZdjHGNW9Go3qMOFrr9subnM7M7M4B7Td4fsGWDN3V3c96szbTZd2BZo9vLIlNCh493CNvewLPNTxRBCRdAm1dryoUOHCCGlbLvOd7ooR4fw3FxQpkNCrvAkknevTvfvKt65m/O0pe0tKEEgJ0/xiSmCox3N8mBlUdzZiVBWAJKyN1xLBMoKyvvwLdbRKn1EZ/Nl+7Qvpfeipfei1fDWI7XZYT0EJ9sybVk7XcwwxhIJAFxKSVG3pgCox2dVfuZ5A7PJRSKKokp5ui5+sXbtgyJMjODp9pbVAADmB8G8s1EDewZt2X0E264RQgghVIUsLCwmTJiwePFiAJgwYYKFhUU5ctWqbLWuBJRZ++9GWe+au/WZ3uNwDRp06eXraCSiZM5BYwfWuBV2O2+cI8azz/g+XkaMYY2QTrUy7DuN6+QiZ0wbdWtv//juEw7YG3/+mdBpyuSWdlJa7tpp8gjP0wcv5FbNG0PoI3TgwIEDBw7oTzl06FD52q7L2iGcY8mB3UItd1Fod9GNK8KF04KxEdW3l8jJjjY3pBNeCAozyoRh2FyqTYsSez6LeaZBWQPmmQblvNH6tbQvg1kX66LmEidb9qteFdk+AIRnZfZsZfM8VtnNyko3McDdXffkxQqiaXrZ8pUR1v20bdfP0qCpE7SzjOraORjbrhFCCCFUtRQKxYQJEyZMmFCO9mqtSmi1fvr06aBBg2bNmtW8efM1a9acOnVqzZo1ZmZmJa5YwVZrAkCA0NUHTw5Z+82SUyGB2gmE5D7eM+un5Ydvv1RTDGQn5TTLYQmRECCUuaWZtvFFIpXQ5oq8F2KphE1lBcIlxL9MOzu26YW8379stqRuVrZADLDZGqHK0LVr1y5dupSm03iJdM3UpWmvTk+hdqwW/NpSEXeJiKchh8lIIrs2CHIZKKyoxDR48IoSAJq2BifXUh3tnKgXw1/RlhmRYJSYVnAZo8Q0RiTor1KaLRdHKkmePtBu6KJCRgCnQPPjlyCraJ9tOc2s3hf3JD53RWbcFFdX7cSzT540U6nExd6PXXra7Pqbr0Hbdu2ugHsJMNEsKjg4+MgRbLtGCCGEUFUqd1KtVdHUOjc3NzS0t0ZDVCoVAKhUqqio+EGDBu/fv6/EdUt/h2QheF4gQHiO42TNxoxyaTNvnbUCiMBxmudbxk242uyv4xt9FBIhdnUn/zCe5ziO4wkQnuc4jgbCCa9faLdECM9xxMLa2q7XyrC5fvotNHxFokQIvROl7xAeeYu6dYF06UudPwC1m9BP7kKX/pTMkAIAtRIyUoCiiZkliCVluITGigdI2KWUEM+nyjr3ugJrJ+k/Z5bZSmQAACAASURBVEuLEQmde13hU2WMhYrQDqx4QPneqT5l8zo5HRsbHr2abzrfpinfyrccG5TL5SqGEV5f2uhoYbE8Jd4xWzrO2fnNTmlaLpeXO+aC8mXXtW1hyr/wbZ2o0JDgXfsxu0YIIYTQh6qiqfXu3bslEhOx+E1/bFNTi5jnL8PDw319y/NTrxxol/7f91w9cG0EW60JAFFm54hs3KtZSIBk3di26yZb2iF5xQ379Dbp/cui9mvHtXSUqJOeXLmZW6tdQ7zXGqGql69lW9sPnBCiKxRcRRDg4m4aGOg+klA0tOxCHdtIfT5dEInzbnmWyMDq9emhjC3ncpV0ufjBsMz5TWrN+a/uoh2FLsSnyjKmtjaZdIWtuZyAvPgbrUsZQ8oP/eTn7tI5qjdryaWaacPLt2WGYSR+fomRkSYSCU1R4ZmZJI4s9vQQv35QdrJGQ3x9GYaplHutb9+4+sXAgdqyIAizz0kcTDQeljC7A0SlgL9J1NDewet3/oPZNUIIIYQ+RBVNre/du2dqqtCoc1esWHHo0KE7d+6YmzmIGOr+/fslptYVHsYsb9QyAFnTMWMb/jn2DBBC6Or9pn1x/vu2ASvtLc2qNW9QT/wK3vQ51f6fwJsyvJkuqTvhj99h6oz23v0ziaG1R5PQyYvaViRIhNDbKKpyLlWxLJuvUND5jYx9TcHKFSgaiAA3/qZDxgqiCvdr5s9dZwIbaZ565C7sYfr9XsZCVdSSjIXK5PsrGQt7iH70oD3yVqzg3jkb87Sx3RRz3iTz3KjexMGm3BucNm/euF69eiQl+ZmbtzQ372ZlpRQEpSDwhNzJzt4ukSycO7eCMWvRFGwJigOI002Z+AVQr2d5WEIuC/duRY3o22nNDsyuEUIIIfThqWhqbWFhoVZH2Nk68Byb+DLLwc5DIpGmpLyqYD/1kjE1vz8Xq3tF2w/Y9fx1f0vLFlP2XJmim5f3QDLpZxuiP8ubRFkP3vd88OsteU88fTevLHJoO2lL20nvMnCEPjVpaWn//PNPMQvornvJZKV9RP3Vq1f9/f1ZltUVCi7DpdIvr9NPT0HTIVzkaaZmC2JiVQlXyri1eyk7K/aXteIfv2GrBTHqrykhvtAlCe3A1lou+tGD/WWt+McvubV7K55aA0DmwHbGf1+QPIoFAKGaIzu0e0W2ZmNjs+nEif3793dbslSlftP/SCZlxo8fty4kpOKP3dKhi72uIqJhRjuYcTLqy76d1mJ2jRBCCKEPTUVT6+7du2/evF1hYSWXyWiaAgCe59Mzklq3bl3iuhVqtUYIfSDMzc39/Pze7jpSyDBm169fVyqVRW0k3+lCt0H9LedD51JOTfiXN5m0SJpiKWcftlJOOSQ9i/1lrfinEVR1RxacOPl1MbtNzO9h+Juvn8gl4ZkGLNOTFQ8gIKfcQfzTCHbmGpKeVdRJz9bWNjMzs7QBMEzyjC/s+84GAprpX4G45NN48SdbiUQSGhp68+bNs+fC0tLSAMDc3LxFYEBoaGiJ6+qU6S0UqpYNAMDPbSFDFTWib6fVfx7G7BohhBBCH5CKptZeXl7jx49ZsuR3b++GJkaGubnZDx7e/PW3JUZGRiWui6k1Qp+aQvPqcgwSXpoO4XV7c4+2S2mRkHKbaTBKXaG49QmEpGaw05aLvu1PN6lNQK4Rf6kRfwnA0iQJAATKSv85W8KVe9yvf5DsXBAq7YynauiR2bslr0wV/OtX1jbnzp176dKljZv/AIAhg/r7+flVymafpEmnHS9z5aflRPXo0ePSpUuVEgNCCCGE0P9BRVNrjUbj6ekZFNT27t2nxoaGsXHRixYvaNeuHabNCKGqZVGD9/+l8p9ML/3n9yLmiAWqkFZWukltyY55lR5G8sxBlb5NPz+/ysqode49e1W5G0QIIYQQej9VNLUeO/YbD2s4evhfl2oNgQKxWCwSlXabmH4jhFA+pqamgiAwDMPzfMlLF4thGEEQTE1NKyUwhBBCCCFUjAql1hqNJiHm8bbZP0VExcWkiCgAkUiclZVV+tUrsneE0Kfj07kSZ2Zm9vz5cxcXl9JfpiyKIAjR0dFmZmafTu0hhBBCCFWVCv102717d0i7RgAgCALDiICiRCJxdnZ2KVcPCwuryN4RQh8EOzu7qg7hQ+Lp6RkREREVFZWRkVHBTZmampqZmXl6elZKYAghhBBCqBgVSq0j74fPGB0MALkqDcMwFIC4LK3WPXv2rMjeEUIfBLyIVlZeXl5VHQJCCCGEECqbcqbWhBBBmbJgYncgAgDkKtWMXAQAFhZWLVq0rMT4EEIIPqUO4QghhBBC6ENUQmq9atUqlUqlP8XW1rZLly7fjBnzeXvPzm0aaydqNGzCq8cSsQgA9u7de/78uS5duuAjSRFCleXy5ctVHQJCCCGEEEKFaNu2LRSfWguC8P2aHXyfH9+aunl7/V8X/TKuly6vBoAVs0a+Sk7XvcxRZn81fPDBf45VcsgIoU9VYGBgVYeAEEIIIYRQkUrqEG5kAbVbAAAQQt0+Teq3hsdXl4yu18qvrv5Stb1cwMtFW+Z5IUepWrDu0DuJFyH0ScIO4QghhBBC6H1W6nuteQ6ibkD91gAgYphiFhz63W/P45Os7NwrHhxCCCGEEEIIIfT+K3VqLRKTHhNLs+DmJeMAoM3nP5U7JoQQQgghhBBC6ANSoYdvFWrS3M0JSWkmlk6VvmWEEEIIIYQQQug9RFf6Fr8Z/Nl3I7onJ8aVewvJycmVGE+heI5/17tACFWhdevWNWnSxM7Ozs7OrnXr1sePH6/qiBBCCCGE0MesXKn1Ox5PaO7cuWXKrjXRB3/oWt/BzNjE0tV32J/RAoCQcHJmt7p2pkamdnVCZhx/yQMAf2+Wj3Po9Bm9mzX09hz6dy7JvL5mWPPqlsZGFm6Bo7Y/UpW4I4TQh8LBwWHmzJnnz58/d+5cYGBg7969Hzx4UNVBIYQQQgihj1ZJqTUvULkq/X/AcrkZSZrcTJ5jBZ4r+O+3Dfvmr/jL0sax3DGlpKSUIbvWXJ/dbcgJ91lhLzLT48I3DKljRAkxG4f2/cPsh7CErMSLP1n82W/IxucCAICQeOK8wdRjNx5GbemZs3dkyMKcIQei0tMjd4U8m9Jv/k223CEjhN4vwcHBHTp0cHd39/Dw+Pnnn42MjK5evVrVQSGEEEIIoY9WSfdaCwKt0rw1geUXbzzq4WSpMDMsdI0fhrQGgC5jVlckLG12PWXKFEtLy+KXZMP/2J7UbfkvnV0NAMC6jr81kJdr/jpfa8y9XtXkFLj1mPnNqpp/HUkY9iUAZdh2xFf1DAGAJB/e9I/b17e+qG1KAzT+enLIivGHI6Y1qF3c4OcIoQ8Oz/N///13Tk6Or69vvllqtVqtVmvLhBCNRmNpaUlR1P89RoQQQggh9MErLrWmKEqaEJG7bID+RFvIHjztx9FLNrJZL4EUfscyJ4Bf664VjCwlJWX9+vWTJ08ufjE+8WWyXT0Xqd4kITkxWezobKNtkaetnB2lyYlJPDBAGVtZapcUEuNeKm8v6lBztfZnNK9UGvTNEAAwtUboI3Hv3r02bdqoVCojI6MdO3Z4e3vnW2DRokVz587Vn3LmzBkjIyMAMDU1tbOze/nyZUZGhnaWQqGwsrKKjY3NycnRTrG1tTUzM3v69KlGk3f90dHR0cjI6PHjx4IgaKe4ubmJRKInT57oduHh4cFx3LNnz7QvaZr29PTMzs6Oi8sbn0IikVSrVi09PT0hIUE7RSqV/vfff8HBwbpgTE1N4+LisrOz7ezstFOio6Ojo6Pr1q1rYWFRVHi3b99OS0tr1qyZSCQqKrzz589LpVLdZQiO48LCwnx9feVyuX54J06ccHLKG6syNTX1zp07rq6urq6uuvDy1V7B8Agh3t7exYcXHh6uVqubN2+uX3spKSmpqam68C5cuGBubl6vXr2iaq9geC9fvgwICEhJSdGFRwg5e/asfngREREvX7709fU1MDDQ/+M+fPhQd/Gl0PD0/7gFw8vJyXn58mWzZs104RkaGt6+fVskEumHFxER4eXlpfvjFvzsacNr3LixoaGhfnj6n72C4Z0/f75Zs2a6+GmadnV13bdvn354V69etbOz8/Ly0oXn5OT06tUrXZ0XDM/AwMDZ2bn48LR/3FatWukeUO/m5vb06VP9K1n5PnsFD42C4WVlZTVu3DgpKSklJUU7JTc3Nzw8XD887WdP/0+pPTRu374tlUr1wyvroaFUKr29vfWP3NOnT1taWurCK+rQiI+Pz8rK0g+vrEdueHh4cHCwfuc+AwODM2fO6IdXjkOjNEduRESESqVq3Lix/nnv2rVrlpaWxR+5xR8aLMvWqVNHP7ysrKzr16/nO3IjIiL8/f0lEol2ivbQuHTpkrm5uS68chwaBgYGNjY2uvAoijp9+nS+I7fQQyMqKoplWf3wynrk3r9/v0ePHvrh5eTk3LhxI9+RW9ZDQxue/qFRMLzo6GiZTFa9enX9L7Vjx465u7vnO3LLdGhoz+3Fh6f97LVr105Xe6amplKp9Pbt2xX8UnN0dJTL5cUfuRcuXLCysqpVq5Z2ivbQuHXrlkwm0w+vrIfGq1evAgMD9cN78eLF48ePS/xSK/7QKOWXmrZydOGZmZnt37+/4JdawUMjIiJCd1ou35fa1atXe/bsqTsdGRoaZmdnP3/+vNK/1G7cuNGpU6eXL18WFV5pflAVPDQq/nuvRo0aoIdSqd7cY0z0bqIuqlzKxYovX758maKoDh06QGEGDRqkfWOlarW+MN67b/aKx2s7yHX7eLmmvdfOrvdPfe1EAwhxK9vW3Nvr0ckvU2c36fRiwZOVrSUAJHF9UI09PR4dHW6DTVQIvVNhYWF+fn6kJDdv3lQqlU2aNMm3+smTJ0NCQnTNy6Wn0WhiY2MzMzP379+/efPmf//9N192rd9qnZWV5eXlFRsba2JiAgAURdE0LQiC7vRVcApN0xRFFZzC82+uOdI0DQC6L4ZCpzAMQwjRTdHuSH9KSkrK33//3b17d4VCoVtGqVTm5OToh0dRFCGEpmntr0xdeFlZ/2PvvuOjqPM+gP+mbMmW7GZ3QzokoYWmAoLYTlGR82wogr2BHuo95529d89y5+md95yPnoCgngUbFvSUoiinnOIpICBgCJAQElI2m2Tb1OePHwxrQpLNZjczm/28X7w0mczOfHfyze5+59fa6Os83Uc7rMlkontqWwKBgCRJpNNrvt/vLy8v5zhOC6+pqYnuGXtqVVVtNpvNZou9Vk1NTaqqxu5DH+V0Om02m3b1FEXx+/2x4dEjE0Kys7NNJpN29SRJ0so8ukU7bE5ODs/z2tUTRTEQCHQ+NcMw9J1F2xKNRunF7HA9CSHa5xJ6Pdva2kKhUGx49CEcx+Xk5MT+ctvb2yORSGx49CFms9nlcsX+ultbWwVB6PyrtFgs9EaPdj2bm5tpdnV4UjabzeFwxOae9qEh9lfJMIzD4dDqSbqlsbGx86lVVXW5XCaTiZ5aFMXKykq3280cEPukPB6PyWTSfpWiKLa2tna+5gzDuN1ueono9QyHw1qFGXs9GYahqa79aYRCoVAo1PnUPM97vd7Yv8FgMBiNRjufmuM4l8sV+6tsbm7WPtbH/mlYrVaHwxH7q/T7/bIsx/66KYfDQXNYO0hzc3OHFw0asN1up5/a6fWUJEn7BXV4UrTA0F5YZFmmfxed/zS0fKMkSQoEArHhabnn8XjoU6ZbQqFQe3t751PTv4vYV7lQKBQOh2letbS0aAnA87zb7Y593Wtvb9f+LmJ/lSaTiV527XoGAgF62Tv8aVitVpfLFfu619LSQiPpcD2tVqt2l4Q+qYaGhtjfgvakHA6H1WrVXpbpXxD5+V8u5Xa7zWazdhBZlunF7PyrdLvdsa+E0Wi0paXlkL9Kj8fDcZx2PSORSHt7e+ccZlk2Nzc39l0jHA6HQqEOf7l0T+0OAv3TaG1tjX1t1w5rMpno35r2q6Sv7Z3/KrOysux2e2wwfr9fkqQOT5wmAH0X0H6V9LW98/V0OBz0mPR6aq/tna9ndna2VnPSDG9ubj7ky1FOTg49Nd1NEARaBXX+09BymH4biURoddThT4PEvLbTX2V7e7tWR8Wemr62MzFvavS1vfOv0mw25+TkxL4CtLa2SpLU+U1Ne23XgmlqatJyIPZPw2az2e322I8E9KWj8/V0Op0Wi0Xboqqq3+8/ZA5rb6n0egqC4Pf7Y8PTHpKTk0P3VBRFluUdO3bQ/O98WO1i0i3RaLStra3zqQkh9LVdu57BYJC+dHS4nhzHeb1eJuZNbc+ePR9++GHsByGS0AcqJo5Pd523dP95j74m0N/s0qVLDVpax1lXE0KIsO7eo0775NQXl9z/yxK2cdN/6/OnjA7NP33ik8XPffS3c/Lr3vntafOqf7/uw3nFmx8+WFoTtX7JRZPu4R54/a+XHuFRAru/+7TSc+ZJw9FoDZBsepXWsc4444yysrK//e1vXe3Q2tpaWFhYW1tLS2tDaWhoeOutt2bOnJmbm9v/Z1dVVRAEs9nMoKt8pkIOZDgkQIZDAoDuOaDvB6E40dI6+Ytv9V0v6mpCiPnIu99+/sRNt0zOy3YXTZq7cGO7yg6es+Dl8/fdNyXPOWjKvfWzXlpw1ZCOT5TJm/Xcu7c4Xz5/tDc7p2jcmXe9VxVJ7cTnAKAbep9b7yjSldawABkLOZDhkAAZDgkAyIE4GfEy9aKuJoQQYi6b8dgHMx772bbC6fe/O/3+n23ixt69blfM90z2+Kv+b+VV/5d4oABgVPfdd9+0adNKSkra2treeOONL7744t1339U7qLSkKMr27duHDx+udXmCTIMcyHBIgAyHBADkQPyMWFr3qq4GAOisoaHh17/+dV1dXXZ29tixY999992TTjpJ76AAAAAAYMAyYmkNANBHzzzzjN4hAAAAAEAGMeJYawAAAAAAAACTyVRcXKwtF2JkaLUGADA0h8NxwgknaGt19DOWZYcPHx67zA9kGuRAhkMCZDgkAOieA263+4wzztDr7L3SQ2ldXV2d8KHz8/MTfiwAAFBZWVkdVuTuZ5Ikmc1mHQMA3SEHMhwSIMMhAQA5EKceSusRI0YkfOjW1taEHwsAAEagKEpVVRXmBc1kyIEMhwTIcEgAQA7Er4fSOhwOE0JU9WcrPsd+G8/XAAAAAAAAAEnXufBkGEaXSDDWGgAAAAAAANKGoiiKoqiqSv9L0R8xDMMwDMuy9L/0i/6JCqU1AIChKYoiSRLP83rNIILZawA5kOGQABkOCQD65kBLS8uaNWuOO+44t9utKIosy7IsaxU1iWmjZhimc5nNsizHcRzH9UONjdIaAMDQmpqa3nrrrZkzZ+bm5vb/2TmO68ukGzAAIAcyHBIgwyEBQPccEEWxpqYmGo1Go1FZlmnZTP+rlc20pZpup2RZliSJNmsriiKKIsuyPM/zPJ+6Aht3oQAAoEuqqra3t2P6jEyGHMhwSIAMhwQA3XOAnjoajUqSRAjhed5isfA8TwjhOM5kMvE8TxulaaXNH0Cbqc1ms9ls5jhOURRBECKRiCiKKXo6vWu13imF3ww2rA62bQmLjVFCZJOPsYwy2X5ht53ntQ82o1AHABhQFEWpqanBvKCZDDmQ4ZAAGQ4JADrmgKqqkiRFo1FCCMMwtIrWeqfTEXOyLGut1lqHcG0kNq20GYbheZ7uL0mSIAiyLJtMpqQ/o3hL611y6N62be+0N8oiT2QTkXiimoii7JWlvaHQqmbxoZ3hGTm2B0ptg60osAEAAAAAACBBtJFZlmX6LW2mpt/Srt0Mw9DSmrZmE0JUVdU6e9N9TCYT3cIwDHcAfZSiKLRWT2L/8LhK69eF6usDG9tFQlSTtjGb46a7PNuD0vcBkRAiq+pbDcK/9ql/HWGbnY/bWgAAAAAAANBrsiwLgqAoCsuyJpOJECKKoiAIhBCO4ywWC+37TVuwtWZqOmkZOTBJ+CGPTHuPi6JIm68VRTGbzcmqrntuYX4qsmVu+zftqhS70c5xX48+fMmI4d8eUXFxXo62PSirV/8g/rVKSUpwehLX3jpq9G1fSz3vCQAwcNFBSnqtDwlGgBzIcEiADIcEgP7PAdoJXFEUOqxaq5/pT7XpwUnMOls8z5vNZtrHu8NM4Frhrc0urqoq3Z+2e9MCOymR99Bq/ZpYdXd0vdppt7Nc3hGWrPEbNl7iHTTObiWk/WD0RL13m5Jv5mcXYsIDAID0xrJseXm53lGAngyVA7W1tVVVVdq3brd7yJAhgiBs2bIldrdx48axLPvTTz8Fg0GWZYcPH+7z+fo92AHCUAkA/Q8JAP2cA7TWVVWV9tY+5D6KonQzTFqbKpx+G/sF/VorpHmeF0WRzmpmsVj6fvugu9J6l9J+vbD2kNOnjbHa60Tx+2Do+9Y9ROIJMcX+VFXJ7zeSKW5msC2R6rqxsTG93gJlSeZ49IEHgAFIVdVAIOByudBkkbEMlQNOp7OlpUWLJBQKtbS0KIrS1tYWu9uGDRsYhgkGg7Tpo7CwML0+VxiKoRIA+h8SAPozB7R+4LFzjFksluHDh1ssFvotbabuMZjYxa7JgQKbYRjab1xbFpvOfCZJUlIa57vrEH6PtK5dFQ/5o+EW27ZIuJvHBiXmvi0JVpuPPvpoY2Nj/PsLO9+76+wjitzObF/p5Kte2akQotStePCcwwpcDlfBuBkPfLJXJoSIn/9u2BH3fk+7eMtbHplcMm+5QIha99y07FNveuTSU44/+oiR4067+5O6jh0Cwj+++vtTK/KyHe7iyZc9822rSgiRf3j4yMGz73vg/OMmjBox9+1g0xePnXt4YY47xzOo7KibPw4k9tQBADpyuVynn366y+XS5eyKotTV1SWroxSkI0PlgNPp9Hq9rgNsNhshhGVZ18/Rz0Z2u93lcmFa4z4yVAJA/0MCQL/lAJ23jPYD1166GYZxuVxTp051u90dquVeoQU57TpOpw3X1uuiHc4lSRLFQ1e+8euytN6ptr2jVHXefpGr8LKcgok2J88wl+X6xtisXR3h3VpuVyiRur+pqakX1bXw7R/OmbN82MP/rm1tqfl6wZxxDkbZvXDuhS+77/p3XVv9l/d6Xrl4zsJd3SaD8Pmnbde+/flX362bP/HjK697pS62sb39s9vPvmXb9MWbmgLVn1wnPnnBnZ8GCSGEKPXLv7Dd+fF/t1QuPrf6+VueMd2ytq7F37hzzbMXj7Qk8LwBAA7BbDaXlJSYzWa9AwHQX3NzczAY7NVDPB4PmqwBAAxOVVU6HzidxFsremkVzbKsxWLJysqiU5ppU4InJnZ4ttlspgt6qapK5zbry5G7LK3fUH6SD9UX/B+FYxeXjB1qyTrWmb142NDTc7psSJFV9a2a3q2brYm/uha/fvmfDec8+NAZpTaGtQ4ad8w4H6n/6PUvxvz2gVnlWYy1bOaD14/79+sf1nXXNd00+fJ5U7IZwjgnzbt8xKdvrmg5uHf40xde4y9/6HdH+Uycc/Sld13u+vCdb0RCCGHsp8y75nA7IYQwJrMpunfz+m31EcZWNH58aZc3HAAAACBBDQ0N7e3tPe8Xw2632+32FMUDAABJQRfEohUvrXU7NE3TepgupkVnI0vKeVmWNZvNFouF53laXfelfb7L0vdzsveQ231bVxSzju0jj5v9048fNLWJEtfdQRq4G0ck2LDe1NQ0f/7822+/vfvd5Pq9jQWHD4ltJVYa6xtNxYPz6G0DNndwsaWxvqG7q8/6BuXu39uX51Ma6pu10lpt2VPbuuuTC8a+RXslqNFW6eQ2hRBCGGeub/952aHXPPfXffc+MmPUJaEhU6+878kHZpSbOp4FACDtMAxjt9sxxC6TGTkHNmzY8N+NWzrcO2cZcuzkicOGDaPftrW1tba2Zmdn9394A4OREwD6ARIA+iEHFEWhnbFNJhNtl+4Kra4FQZAkKZ4R13HiOI4OuqarZCfcVbDLqniz2nzI7RFFKbFYCSE/hENhRSHq/n7wJpYRO1WvP7YlPsDJ6/VeddVVPe7G5RX66qp2C2RU1oFNrC/PJ35avU8hJSwhSkN1TdR7dC5HwhaTdKAHvdrW2nbwvViu2VUtkwqOELlmZw3jG+RhSAP9EePKz3OOmvre2jtH/ey50Od68JdpG33BY29e8Jgc+HHJ78+ce/sRU5fM1mdgJABAErEsW1JSoncUoCcj50Bzc/OeqMVX8rOpa5t2bvH7/dq3LS0tfr8fpXXCjJwA0A+QANAPOSBJEl3COp7ZMXielyRJkiQ6ZDpZMdAGc1pa02bzRA7S1Q8aSaSrH42w2CRV3RGNEEIeLS2cP7L4F27blmMHD8nq+NwaowneSPB6vXfccUc8g6NMky+6yPfOffd/tDusqtGGH776oZHknTb72B/+/uDSnRE1snPpA3/beMzsX+Uz/LDDKvZ98el2gRC5/qPn36w8eCdA2rDgsSU7Imp051sPP1c59bxT3DE188lXzg48d+vTX9ZFCZHaa7//6JONbR17l8tVq978dJtfUDmHryAni+W4RH4ZAACdSZLU3Nzcx8E/CVMUpaGhARPYZDKD54DF7igYOib2n9mCQVnJZPAEgFRDAkCqc0BRFPohhy5A3XmH5ubm119/vbl5f7svwzC0opYkST3kWlYJoU3WsfEkIJEKcITFtluIRFWFENIkSZfmuReNLqiPytXh5Hzyi7+uJoQQ85F3v/38iZtumZyX7S6aNHfhxnaVHTxnwcvn77tvSp5z0JR762e9tOCqISxhfLMefqT0tTPGTphy4lXLB08dc/BGgGXaxZM+vmCkb9CEO/fOXvz3iwpi7wg4T/7TB3+qWDFvQp7TlTfy5Otf2tROOt0xCFcuveNXIwe5XL6x12469blHznL2+TIAABBCiN/vX7JkSWwryu/ZkQAAIABJREFUXH9SVbWpqSmJb12QdgyVAw6HQ1t/BfqHoRIA+h8SAFKdA7Isq6pKW4lpT+8O55Jl2e/3xw6upvN7J3HENZ2cnA72ZhiGtqIncJwu29B9xLqXCIf80XDzwZW3FtQ13V9SWGDmf/VtTefz+yyJ/A56UVcTQggxl8147IMZj/1sW+H0+9+dfn+HHS1jrnr5vwf7mN9DCCFEJYQw1tFzFt3/m9h9TVP+uGXz/q+zRsz+07LZf/r5wcbevW6X9g03+uqX114df8gAAADQa0VFRdXV1cn6LAUAAPqia0oTQsxmMx1xHY1GOY4zmUzdDKWmDdeyLIuiSIdJJxwAbaOm9Tyd0ox+S8vs3h6tyweMZjxd/WiExb4tur+0HpllsbJMq6zsDB9iurIKZyJvflgkAwAAADqjH3d69RCr1Wq1oos4AIARKYqiKApdcIvO1M2yrCzLkUgkGo2KokjbtDs/kD5EUZQEbrbSnueSJEWj0UgkIoqiqqo8z1ssFm38dlfn7V6XpfUvSMEht/MMU2bO2h4NEUIsLLNg+JCfwoLPxF1YcIgu0L/IxX1lAIA0xjCMy+XC3LCZzFA5UFlZGc/CnLFyc3Pz8vJSFE8mMFQCQP9DAkBKc4AWxlrLM8/zVqvVbDbTAlsQhEgkIggCOXBrVVEUVVVp0autcd2hBlY7oRW4JEmCIESjUVpR09JdOymt6gkhdD1teqLePp0uO4TPYoc9yHzfuTIuNWeZGZa2Wv8i2znEYj76vzseLy+Y4rK+QMKxe3IMM7M4wZW3+hGTP295yzy9owAAMCSWZQsKDn2nFTKEoXKAZVn6OYl+CGMYhnRqr1BkSevFR4fP9XeUA4uhEgD6HxIAUpcDdOYwhmFiJwannb05jqM/1UpcURQjkYhW/WqlPl2FSzsCXUOL/LzA1g5CR1AzB9Cmb1pLxwbAcRxtMO9tn/Au9y5lnOewZZ23jzDbCSHbIiFCyPKW1tJ1mzYGwzM37Jm3eV+HPc8ulIfYMOcBAEAaUxRl7969mBs2kxkqBwoKChwORyAQoN/6fD65rVGJqa6j4SATDdKRZaqq+v1+OimOPuEOCIZKAOh/SABIXQ5ot0ppnaw1L9MymNbYZrOZrjJN621yYHi2KIrigVWVaeM2FQ6H6RfRaFQQBFEUabM2wzB0TWx6QJPJRFfYoj3DBUGg05jRU9OKOoGn3F0h/hB/pIPZv2Z3Pmd9Ne+oY6yeY2w5QUXeLe5fmqtRlAgh4U4ntvPqA6PQGxwAIL2pqhoIBDA3bCYzVA44HI6KigpVVSORCCGkqKjIysptzQdv7gf27XFa9jewhEIhk8k0atSoJC58moEMlQDQ/5AAkLocoI3JtDsSbaCm1ax2LloP00LXZDJZD8jKyqJduOnwbLPZzMcwmUy0hLZYLNafozvTuprW6rRcpyU97S4uSRKt9hPoE97dm80Q1vG0ecpc6T8qIfvkSLnJ/sXg4xhCnmrcrXR7GoYhfxlLBqPJGgAAAJKqqKgoGo02NjZKkuRyuXJd9qZ9e1y5+zsrtjbUVhTkms1mURTD4fCoUaMwNyoAgDFphSttLmZ+TtvNZrNNnDjRZrORA325OxxEK9Hj778dexxtOjQNrfYTeEY93Me9wFS21xK5W9qsEHJS7eczswYHRPW9luZuHsgQ5sER7OxC1NUAAEmQk5Mze/bs7OxsvQMBMASGYYYOHZqXl7d+/XpCSPngopofa+mPFFkWW+oHjz6MENLe3l5QUFBeXq5nrAAA0DVtILQ28vmQtbHdbp80aVJXB6EVMi2JzWZzAtOtaYOuaYFNR1lrEfbqgD13kbrBOqqAsV8f2NiuSC+2VhPJRIipq53tHPPXUabZ+QkW+gAA0AHP8x5Pl6shphrDMF6vF3PDZjJj5oDVaqWTuxYWFrLrt0WCbVa7s615n5WRioqKCCFHHnlkdnY2uoL3nTETAPoNEgBSlwNawUjnIUv4FLTzNl3Eqy8v+7Tdm+M4bZKO3pa0cTWan28u+dpz4nm2Qq7rJ8wxzMxc838m22fnc13tAwAA6YVl2dzc3N7OkAkDiTFzgOf5kpKSSCTi8/mcFjbQUEsICTTU+lw2m82Wk5OTn59Pew9CHxkzAaDfIAEgdTlAC1daD/eldNdmCO+8EFdiR6OrfyXy2Dj3G8LZFruP2JB/7AOe8pNs7gKTxcSwJpYtMPMnuWz3l7jWT/S9UOEYbMUfHgDAwKEoSnV1NeaGzWSGzYHy8vKCgoJgMFhaOKh1Xy0hJNhYW1pU0Nraiq5zSWTYBID+gQSAlOYAnais78eh033Txav7frReDdv+WRi92ruUz7rZNfim7IPvWLHvXngnAwAYYFRVDQaDeHnPZIbNAY7jRo8e3dramuN2i1Xbw20BNhJwOss9Hs+wYcP0jm7gMGwCQP9AAkBKc4DODd6X3uAUy7I8z9OO3H1sAyeE0OnKEwmjL2cFAIBUEwShurpaEAS9AwEwHIfDMWbMmPHjx9tYuXrLf62sUlRUNGrUKIvFondoAADQA1oAS5IUjUa7KWUbGxsXLFjQ2NjY/dFi16lOOCRVVUVR7D6ebqC0BgAwtEAgsGzZskAgoHcgAEZUUFBw3HHHFea6W/btKfC6KioqcnNz9Q4KAAB6prUty7IciUQEQThkQUvL3R6bzRmGMZlMhBBRFBMojFVVpUW+IAj0XJ0X+uoRZs4EAIAusSybn5+PCWwymfFzQBCEHKedZ5orRo7AaltJZ/wEgJRCAkDqckBbc4thGLrqlSRJdB0slmUTqGzpYyVJkiTJbDbH8xC6+hddu4sW5HRSNEmSEggg/f9OxLW3jhp929dJGLCeALV50enus19GYxIADFAMw7jdbiy7ksmMnwOKogQCgfb29urqaowITTrjJwCkFBIAUpcDWrluNpstFgttc6ZNx5FIhDYga5N+00WwewyVTu5N62Q1Bl2dS/uaVvKRA2irOMdxFovFarVyHEdXtM680hoAAFJGUZQdO3ZgbthMZvwcMJvNiqKwoeZIJGLkONOU8RMAUgoJAKnLAVq7aktwmc3mrKwsi8VC5yGTZVkQhGg0Go1GCSGiKNJmbVo2x5bKsWgzOJ0qXKuo6bd0ELVWTkejUboPPbXVatVOrTVf9/YZGbFDeGNjo8/n0zsKAAAgqqpqg44gMxk/B8xm88iRIzfWtvl8PnRbTTrjJwCkFBIAUpcDdOUtWirThakZhuF5nuM4WjbT/9KmY9rO3OHhJKY+19qZaWFMW7xJTHO31gTNxKBTi9P+59qREy6tjfgO9Oijj/Y4BdwhqPX/mO4+7/UQ/S669GLfiU/vVoj8w8NHDr70T09eeERBtjP/yKtf+WnPygfPGD3I6Sw69sZlexVCiFr33LTsU2965NJTjj/6iJHjTrv7kzp6W0bcveyesw8rdDuy8w+b+ejqBppRUvV7t5xc7nHnlh97zSvbRbzUAAAA6CcYDO7duxetagAA6YW2MBNCZFnWNmp9uWmZbTKZ6Khpi8VisVjMZrPJZKLlt1ZU0/3JgYHT5MB4aa1yph3OaWdvq9VKD0J3UBRFEARBELQYaCt3Ygtu9+IBoijOmHHO0Ucfs2rVqtjtq1atOvroY84777wONxIS1tTUlGB13QWlcdl7zb/+166m7fMnrL7+1BnznfevqW3c+EjBkhuf/Hp/zMLnn7Zd+/bnX323bv7Ej6+87pU6lYjrH585d9XYp9bVt9Z/9VDeq5f+9o0GlSi7Fl4998MRT2/ct2/jgkmfv7waC+IAAADoh07ryjCMzWbDiFAAgDRCy1dZlmlVTCf3puWx1qpMX9hpkUwrba1ItlqtWV2gg7dNJlOHgpzjOO04tMAmBwZ40xHXtFCnU6n1+unEv+vmzZsJw912+52vvvpq7PbXXnvt2t/8LjvbvXnz5t6evivJra6ZrGnX3zY132zKP/W8E83S5Kt/c6SHt5See+6k+g0b/bTV2TT58nlTshnCOCfNu3zEp2+uaBHWvfTi3rPuvefkQgubVXbWPb+pWPn25yG1/l9vfHnYb+7+VZGZsVdccfulw7ikxAgAYEQsyxYXF6OTbSZLlxygc9jyvBFHuqW1dEkASBEkAKQ0B2jzMi2qJUmiZW2HTtoOh+OEE05wOBwdHst0ixxof+6qKzttM6eFOs/ztN+71o2clty91Yt3IEVReJ7neT62yZ4QIssyz3Eczye3C35TU9P8+fNvv/32vh+KcXi9FvqVxZrlzfHSzDBbrYwoCDRo1jcol93/VZ5Paahvlmw1tc2rrj3i8/0Ttwtt5iPa2uXG+qaskiE+ui9XUlrCbex7hAAAXfF6vXPmzNGrYGAYpvObGWSUdMkBVVXpRyg0XCdXuiQApAgSAFKaA7TXNx1HTctsk8nU4WU8Kytr1KhRCRyZ9i2nhXo3e7Isa7FYOI4TBIH2wqaN2709IzHmWGvK6/VeddVVvXmE2WIiorh/Fa5wW1uvRkHLNbuq5f1f7axhfIM8/KCC/KKLF2/4cb8de/a8fUUe58vzRhsb2umxlcaGJoztAoBUYlmWriShy9llWd62bVuHO6qQUdIlBxRF2bVrF21tgCRKlwSAFEECQKpzgHa9ppON0Qm6k3JYWrQTQmg37x73p93FtQcmFkavP6uZzZYJEyYmcKZe8Xq9d9xxR+/mCWecYw4rXr/ys2aVkNDG5xeujvbmjNKGBY8t2RFRozvfevi5yqnnneI2T770Utfrdz+8fHdIJXJ43+aVH37ToDJ5v5x51HeLFm6OEKK2rPnHP9fjTRwABjTMDgUGzwGr1erz+dBYnToGTwBINSQApDQH+lLKdo/2KlcUJc67rrGDuhM7Y69La1VVWltbEztZnBKpqwkhhJ/4+ycvrr510rjJx575uHTUcbbe/IIs0y6e9PEFI32DJty5d/biv19UwBDzhLveWXDS5juOLc7O9pUdO+eZb5oVQtghV/3j7xM+mj1h0i+mzl7gnnq0uZdhAgAAQPLwPJ+VlYXSGgAgTWk1cJwtzHFiGMZkMhFC6Cju7nema1/3sc7vdUUuiuJPP21P7GRx6l1dbZryxy37p09jBp36hxU//kE7Dv3f2LvX7dJ2PuHpyu8OfGM591X/uYQQohJCGOvoOYvu/83Pj82XnHbfm6fd1+GM/JAZT66a8eSBbzv+GAAAAPqPIAiBQADr7gIApClaA9PmZVVVtYm7Kbqdlt+9PTIdNU3LZrqIV2d0tjM6OTmdWSzhJ2LEsda9b68GABiwwuHwli1bwuGwLmdnWbasrAxzw2Yy4+eAIAitra0orVPE+AkAKYUEgP7JAbogFjmwDlY0GqVlNiGkqalp4cKFTU1NCRxWG3EtSRKd/Vt7s6DrbAmCQE9HJzwzm8196QPVc1He2tq6c+dOQsj27dtbWwM7d+4MBAIbN27UYgoEAtXVu1v8/p9++oneYCgtLcVcggAASdHe3r569Wqfz5eVlaVLAFjNCNIiB1iWLS0tTYtQ0w6uaoZDAkD/5IDJZFJVVVvaWpIklmVZlqUzqCV8/5Qu4kWraFpp0+W+NHQ3hmH6Pmtsz5dp8eLFnz32WJHFYuP5iYTsefqpkYS8OmuWtsNIQlo3bRpByPc/bPiekOpodPKNN1533XV9Cat/MfnzlrfM0zsKAADjURRl+/btw4cPT2wVChgA0iUHGIbRFkGFJEqXBIAUQQJAv+UA7Rauqiod88wwDP1aEARCCG1e1l7q6at97Gs+/bpDBa6ty6h9SxfQ1h5Cx3jTurrvdxB6fryqquf6fFOys/NstniO+Lnfvw2dsgAAAKAfKYpSV1dHx+PpHQsAACSCdsmm9TDLsrTSpiU9LbNjd46trmNrbFpIx5bTHeptOgCbNmWLokhLetodvY96fvtxOp1/a2paUFdni+O9SlSUIM9fgd7gAAAA0C94nrfZbISQUCiEEdcAAGmNZVmLxSIIAp1azGw206LXYrFYrVZFUdROGIaJXXabrpJNYhbTim245jiODqgWRVEURUJIUtqrqZ6PcuWVVw4ZMuSza665Mj/ffKD3eYskbQ4GOYZxctxou13beWFd3ZS//W3q1Kl4bwMAAIB+YLVavV4vw1TpHQgAACQBbbumPbej0SgtmxmGoa3NdB/ash3bZB1bTpMDjdW0y7ckSXRZL7pmtaqqtHRPYns11YsCnWcYl8VCv55fX994mJRt4z77sPnjI8Zr+1gxDAMAYABhWXb48OGYGzaTGT8HDqzXonccA5TxEwBSCgkAuuQAra5ZltXalmnLs1Y5y7JM5/SmOjyc1tV0ijLasZzjOG1jNBql/cw7rPKVhLATe9j3oba7Ly8bUWI/f1Be7Pbj8/NHjx6djMAAAMAQOgxtggxk8BwIhUJ79+5VVUXvQAYsgycApBoSAHTJAdqkbLFYaPVLpzHTVs+iS1UTQg5Z82vzk8WutiXLsiAItFCPPXISJVJay6q61yo0tIivLq8rsFiUmBvFX9XXb9u2LXnhAQBkOvrWote8x4qiVFVVaUtTQAZKlxxgGNbn86FtLenSJQEgRZAAoG8OcBzn8/mmT5/ucrlkWY5Go5FIhC5DTSvnQwZG5zyjA7NFUYxEIpFIhBbVPM9bLJY+rl/dlbg6hHc4saKqEyTnPz/Z63Wbnvmu5pScHPbADpKqxg4iBwCAPvL5fHPnztU7CgCjY1kmOzsb6wMBAAwwFoulrKyMDq6mBbPWFk2/pX3CY0dZ0+7idB86BzhtqIgdrZ0KcZXWPp+vQRS1GwImli3PytreGg5vkV4fO9YUc4e4ThB8Pl8K4gQAAADokqqq7e3t9DOW3rEAAECSMQzD8zzHcbS9WiueOy/Kpe1P622WZbXVtlIdZFyl9ZgxY7Z7PK2i6DKbeZYlhHwdCKjfkPkVo8wxb2DtsrwhO3vMmDGpChYAAPodChVIixxQFGXfvn10KVS9YxlocEkzHBIAjJMD2jzhdMlrrbrW2qiZA2hdTfVbeHFdJoZh7n3qqdv37KkKBunI6ovz858f9bO6ukEQrt2x464//1mvAYEAAJB0HMeNGDECnWwzmfFzwG63FxcXM4xRPvkNMMZPAEgpJAAYNgdomc3zPJ2TzGq1Wq1WOo6a9v2O7SXeP+JdfOv444+3v/32w/fdF9mwwUEIIeSV+nrtpxGWNY0bd+frr48fP76rIwAAQNpRVTUYDNrtdtw2zVjGz4F+b5bILMZPAEgpJADongOSJLW2tmZnZ/N8L9aN1kUvbvFOmDDhzfffv/+ttzY43K2HTdb+bXC471qy5PWlS9OgrhZXXz90/H3rO3bHl3964piCOR8KusQEANCdYDD4zTffBINBXc6uKEpNTQ3mhs1kxs+BUChUX19v5AjTmvETAFIKCQC654Df71+yZInf79crgPj1uvdUYWHh0VOObKzftWnjt5s2fttYv+voKUcWFBSkIrh+w+aefNMjl4zhCRH/feOI0bd9jeX7AMAoQqHQt99+GwqF9A4EwKAURREEgWEYq9WKhjUAANBLr1vVCwsL58+f/8QTTwTaIoQQl9N60003pSCwfsW4xs+8khBCCBYOAwAASEMsyxYWFhq/uyAAAAxUCc75cfPNNz/0wN0PPXC3Aepqte3b/73wiHy3p2js2Q88PmfkmDu+kYi8+Q+TBl+7gnbxVvfN/6V75mv7e1Mq9cvvPW1UQY67aNIV8zeGCDnYITy67PqLF+z86bkZw0pLR1z4Yp3S9MVj5x5emOPO8QwqO+rmjwO6PUkAAF0wDGM2m9ESmMmQAxkOCZDhkACAHIifsW7urlmzprGxscNGn8933HHHdfmY8Bf3XPiHwP98Uv0/oyOf33PW2buU67o9h7T1jWXnLft3zWTm28fOPeOiPxz+7R8mHZjwznL60/+cu/Jq08sbHp/MEyL/+NiMZ0y3rK27ZLAptGf9VtHSt6cHAJBuWJYtLy/XOwrQU7rkgCzLO3bsEEXRZDLpHcuAki4JACmCBADkQPyMtVJFRUXFF198sTTGF198UVFR0c1DxK/ffFeaffs1hzlZPvfE239/qq2HczDuM2+6aYqHY3Mm33DrWc3vvN1pUrODu5rMpujezeu31UcYW9H48aXWxJ4WAEC6UlW1paWFrhUJmcn4OWCxWDweD1pUUsT4CQAphQQA5ED8jFVa+3y+O+64w+v10m+9Xu8dd9zh8/m6eYjcuM+fV1ywv93ZVlTs7eEpsYOKC830S1NhcW5zfWOX092xQ6957q/HbHtkxqi8wvEz7ly6Q+zFcwEAGAAURamrq8PcsJnM+DlgMpmwMlDqGD8BIKWQAIAciJ+xSmsSU13HU1cTQjjfoJz6mr37px8L1+5por92k9kkifvbo9W2QJuWDMq+mtr9q2yJtTUNnjzfz68BQ1Si3ZSxjb7gsTe/qmzY/emtOW/Mvf0dDLYGAAAwFkEQ2tvb0aICAAD6MlxpTQ5U1/HU1YQQ06SZZ3Fv/PEfP7SrcuPnj//lY7o+DVc8bgz3n1XrgoSoLV89/89vtQZnteWDp/7ytV9RWtb99U/v5Zx9zuGx483ZHK+ruWpnq0oIIXLVqjc/3eYXVM7hK8jJYjnOiJcLAAY2juNycnI4jut5V4CMJAiC3+9HaQ0AMCDl5OTMnj07JydH70B6ZtBa0efzxVNXE0KI7RcP/fM2+z9OLvIOPvEJ89kzh9CnlDXt7iePX3PZ4Ycfdfz5i1zHTzYf2J8fed5p7fcdU+QtPfON4of+effBnxBCCDfyghtOr7xxqNs96Mz5taHKpXf8auQgl8s39tpNpz73yFnOZD5JAIA4eDye888/3+Px6HJ2hmHQ1TbDpUsOsCxbUlKCxbeSLl0SAFIECQC65wDP8x6PJy1e3tMgxJ4w2ZN/9/qG3xFCCBG/uuXVF+lmbsisZ76cpe11B/3fCU9XrieEkJsfiT0EN+zmL/fu/7L8ooXfXrTwwE9eXnt16iIHADA6Wq7oHQXoKV1ygGEYk8mEAiDp0iUBIEWQAIAciF8PpbUgq1ubJEXpspPVz/tfqSM8nJnDuxoAwAChKEpTU5PX62VZg/ZyglRLlxxQFKWhoUGSpLRo2Ugj6ZIAkCJIAEAOxK+Ht5+//Cfoy2I2N4gca97bFpk21Eo6ltMHCZLyr0rh5ik9LX8FAJBiTzzxxHvvvbdt2zar1TplypQHH3xwxIgRegeVllRVbWpq0qs7OhiB8XOA4zir1UoIaWtrw4jrpDN+AkBKIQEAORC/HkrrhqB86zGulzaE1uyWB9nZS8ZlkZ+X1h2+nv1WyxNryfWTskz63NQwHf2nHzfpcmYAMJI1a9b8+te/njBhgizL999//9lnn71u3Tq73a53XACQfFlZWbm5uQyzS+9AAAAg+QRBqK+vz8vLM5vNPe+tq7gqYJaQIqfKsT339M61W0TFsq1Z7nNgAACJW7p06SWXXDJ69Ohx48Y9++yz1dXV3333nd5BJaitre2zzz5ra2vTOxAAg1JVVVEUNFcDAAxIgUBg2bJlgUAarIIc13ikmaOsJo6JSHEtFM6zDLpjAYBxtLa2EkLSYs2GQ4pEIj/++OOYMWOcTh0WKWAYxuVyYWqoTGb8HAgGg3v27FHVuD6lQG8ZPwEgpZAAgByIX1yltYVnAlF1Xa1IpzPrqkM4IaRdkOvaxf/WqSXZrMuCXwAA6ExRlNtuu+3oo48eM2ZMhx9Fo9FoNEq/pm3CsizLskwIYRiGZVlFUbSXuM5bWJZlGKbzFnoEbQuNoZstHMfRNrfYE8VuocdXVVU7cj+Hl5+fryhK7JXpHHD3weh49RBe38MjhAwaNIhmoAHDo8mpqirDMG6324DhGfmXG094JCYBDBiewa/eAAhPVdXYBDBaeAa/egMmPJoD5EAvoX4Ojx4/9oOQca4ex3EkRryzaD79dTDHauN76hN+3GArISQkqf+7rv2uYzGfGQD8TFe3PFN3K/SGG27YvHnz8uXLO//oiSeeePTRR2O3VFZWOhwOQojL5SooKKivr9d6H3m93tzc3D179gSDQbolPz/f7Xbv3LlTEAS6pbi42OFwVFZWai/iZWVlPM9v375dO8Xw4cMlSaqqqqLfsiw7YsSIYDBYU1NDt5jN5vLy8kAgUFdXR7eYTCZCSDgc1o7Tn+ENGzaspqYmFAp1FZ7dbi8pKWlqampqaur/8Hq8egiv7+E1NjY2NzcbNrympqY9e/aEw2FZknme5zhu7969hgrPyL/ceMKLfQUwYHgGv3oID+EhvKSEZ7PZVFU1YHgVFRUkBhOJRLRvOjdH37I88KdpLlVVb1kemDmmgMTtzR9qHzvJ0dVh165dyzDM9OnT4z8gAKSpd99912azdbir1wG9E6mq6lFHHdXhRytWrJgxY4bWvNwrN95447Jlyz7++OPS0tLOP+3Qaj1y5Mjq6urs7GxisFvUTU1Nb7/99rnnnuv1ersKJnXhEUK2b99eXl5Of4PGuYMe59VDeH0PTxTFyspKmgMGDE9RlLa2tscff/zryn2XnHbcZZddRmI+eBghPCP/cuMJLzYBDBiewa/eAAhPEIQdO3ZoCWC08Ax+9QZGeLIs0xwwm826hNfY2PjOO+/EfhAyztXTPt9aLJalS5f2eu3Hn5rChJBh3qygIL+/1S/IytQyFyHk06qAmWPPHJljN3f3AdpQtLsRaQrTHUNacDgcZWVlWVlZ3ewjy3IoFNJuPfadqqo33XTT+++//9FHHx2yriaEWCwWi8USu4XjuNhbAGyn9Rvj2dL5JkKPWxiG6WYLbdLvvE//hKe9c8RuTFYw/XD1EF6ywovNAaOFR2t+RVHq6upkWaYdPYwTXgLBGDC87hNA9/C634Lw+h5ebAIYMLxebUF4iYWn3WHv//DojzqfqHPA+l49En+HcE27sL/u/6k5MsxjLXVb/r27jRDyq+E5ikoEWUW1BwC6u+ExY38sAAAgAElEQVSGG954443XXnvN6XTW19cTQrKzs7sv7wEgTdlstoKCAmZ7vd6BAABARutYf8dPVQnPMjzLqERViRqRlKU/Nu0LikkMDgAgMfPnzw8EAqeddtrQA9566y29g0pLDMN4vV4G84JmMOPnAMuyPM8bOMD0ZvwEgJRCAgByIH69brXWMAwRZVVSVIYwhBArz04ocMS5QBcAQEq1t7frHULSmEym4uLizn1c+wfLsrm5ubqcGgzC+DkQDocbGxtjJ3aBJDJ+AkBKIQFA9xzwer1z5szh+cTr1n7T6xBdVm71ztafmsOnDc95f6t/c0OIjrX+cLufjrVOQZCZoqnZHwxHet6PkMFFvZhSDgDSmtvtPuOMM/Q6u6Ioe/bsKSoq6jzKCDKE8XNAluVwOKyqql53oAY24ycApBQSAHTPAZZlzWazLqfurV6X1mU51rIcK/36gnE+bftlRwxKWlCZqqEtROyeHncLNDcO7odoAAAIUVU1GAyiPTCTpUsOcBxXUlKC6jrp0iUBIEWQAIAciF+8pXW2lVu3p73Hda0pSVGdZtzZAgAAAAAAgIwQb2l9/WT7ulpBUQk51DrVmpc2Cj6b6bBB6oWTMBMvAAAA9BNZlnft2iWKIhquAQBAF/GW1i4Lc3LZ/gVguyqtVVV9Z6uc7zBNyFdcFkwiBwCQ9liWzc/PxxC7TGb8HDCbzW63m2Hq6DLskFzGTwBIKSQA6J4D4XB4586dpaWlxl9FFX8nAACG1tLS8sEHH7S0tOhydoZh3G43ltzIZMbPAbPZ7HQ6jRxhWjN+AkBKIQFA9xxob29fvXp1Wiz+gtIaAMDQRFGsqakRRVGXsyuKsmPHDkXBwoqZy/g5IIpiKBTCFDspYvwEgJRCAgByIH4orQEAoEuqqgqCgKIlkxk/B6LRaFNTk5EjTGvGTwBIKSQAIAfi10NpzTJEkOO9joJM0FkEAAAA+h/LsoWFhRzH6R0IAABkqB6mMZtRkXXDv1oaw8Rt7XnCM4aQo0qcdW1CkmIDAAAAiAvDMFarFZMtAQCAXnoomI8uNpdkcw9+Hip1W+I5XF2bUNUS9WTFtTMAABgcy7LFxcUoVzJZuuSAoijNzc2yLKPhOrnSJQEgRZAAgByIX89t0cXZ3D3HZzWHDz1yvfNCXJ4sS5GTRXd8AIABgGEYh8OhdxSgJ+PnAMuyJpNJVdWWlhZFUVBaJ5fxEwBSCgkAyIH4xbWudXE2V5x98I2qm3WtkxgZAADoTpblysrKoUOHolzJWMbPAZvNlp+fz26p0TuQgcn4CQAphQQA3XOAYRiTyZQWK8DFVVoDAIBerFZrRUWF1WrVKwCstwHIgQyHBMhwSADQNwd8Pt/cuXN1DCB+KK0BAAzN6XSeeOKJekcBYFzt7e01NTX49A8AAPrCePTeCbS2bdm5p7WtjRDyw/aqXXvq9I4IAAAg06mqyjBMdnY2JtoBAAC9oNW6d0LhsJLlCofD2U5nq0haG/1WU9JGHYTCEZs9WQcDAEgClmXLyspQrmSydMkBlmV9Ph+GgyZduiQApAgSAJAD8UNpnbhQOKzwlmYmadVwlheFNQAYDs/jnSLTpUUOqKoqCAJtvtY7loEmLRIAUgcJAMiBOOH2Q+KsVotFDtvF1mT9CzfX6/2cAAB+RlGU7du3YxRrJkuXHFAUpaamRpIkvQMZaNIlASBFkACgew4Eg8FvvvkmGAzqFUD8cAcicSbe5HVkDS7MT9YBQ7v2JOtQADBgNDc3L1++fNq0aR6PR+9YAIwoKysrPz+f2Y7ZTwAABqBQKPTtt9+Wlpba7Ubv4dtDaV1dXZ3wofPzk1ZzAgBkLFmW/X6/LMt6BwJgUBzHpcuSpwAAMID1UFqPGDEi4UO3trYm/FgAAACAeEQikebmZlVV9Q4EAAAyWg9jrcPhcDgcDv1cMEZ7jLYYmVBX5zt4n8OqdxT9S1x9/dDx961Px5Fsqn/xme6zXmzp15NGl12Zf+yfKzE+CdIXy7LDhw/HvKCZzPg5IElSMBhUVdXIQaYv4ycApBQSAJAD8cM1StyQosJcr8GGPsobHxzvnPHPNr3jSFz0rfOzR9+5LinFu/jvG0eMvu1reizGdtS8P847ytaH46m1z55iZRiGZTmzw1c6/tQ5j7y7LQ1mVADoG8wLBWmRAxzHlZaWmkwmvQMZgNIiASB1kACAHIgTSmvIFJaKM359+khzH49iOvbP2yNCJFCz/oPHz2bfvOLYc/7+I15sYABTFKWqqgpzw2Yy5ECGQwJkOCQAIAfih9J64FLqVjx4zmEFLoerYNyMBz7ZKxNC1KYvHjv38MIcd45nUNlRN38cIIQQcfeye84+rNDtyM4/bOajqxtUQoha99y07FNveuTSU44/+oiR4067+5O6A39PSvOaP547Ls/pGHT4xf/4IUIIIcHvnr3sqMFed44nd/D4ua/VKIQQYcfbN08fOSjbmVMy6eK/fOVXCSEk+vaFOcf+7o+/+dUxk8aWDz362iU7u56ZKfrORZ6p/1tNTyuuuWH44ff8VyJE/uHhIwfPvv/BC06cMn5k2Ziz/vhVgA6vC23+5++mj8pzOV2Dhh1/y4eNkWXXX7xg50/PzRhWWjriwhf3Nsd0CA/+sOiaX5R7nU5v2fG/fmFjsBexMSzH86Ysd9HYU3/zj/eemLzu4cc/DnZ1zIPkTQuuOGFUocfp9JQeM3f+BvpjcfX1Q8fN/cOt5xw/+fBh5eMPXE8AAOgtLL4FAAD6Qmk9UCm7F8698GX3Xf+ua6v/8l7PKxfPWbhLkbc+f8szplvW1rX4G3euefbikRZCxPWPz5y7auxT6+pb6796KO/VS3/7RgOtVYXPP2279u3Pv/pu3fyJH1953St1KiGESNvefJ+9eVWNf+eSaZtuv+2VelWtf+2u++ou+qTa72+qWffydROyGSJtevKiq1eNefr7xsDuj64K/XnW9Uub6VHFdSuqZyxe880PG1854cvf3/9hewLPrX7Vfzz3/2vtd1vW3Mo+deOz22RC2pbffOZNm37x7Pf72pp3fvaXmUMtltOf/ufc0mHzlv60c+e2Vy/LPzh1bHjNvefdsnX6P7f5/dtfPe2n2867+4twQrGxRWfMmNS6ds1mqetj7qcyeac88MGWutbmLYtP3nL73L9u2l+4S1uXb57w11Vfr9/80SXVd9/+Sj3m4QEA6BWTyeR0OgkhgiBgMjMAgAGG47icnByO4/QOpGddltaFhYU2m81ms+Xm5ra396L8CQaD+fn5LpfL5XINGTIkGUFC76n1H73+xZjfPjCrPIuxls188Ppx/379wzpiMpuiezev31YfYWxF48eXWom47qUX95517z0nF1rYrLKz7vlNxcq3Pw8RQggxTb583pRshjDOSfMuH/HpmytaVEIIO+icW244JtfE+4676Izireu3SoQzmZSGrd9v3hNULYPGTByRzchb33lj+9Rb751eaGadY+feP8e77LVVtKGWH3fxtSflsoTYx0871rRpw+7eryjEZE//9ZwKKyFswcnTRm1bv1kioVUvvmGd89htJxRYCGcrnjhlpLPLVVjEr5e8HTrvrpuO9vK8Z8pNd8+KvP36WjGh2Bi3z8sF/AGl62Pux48+45ITh7rMjCnvhJuvO3b7mv/QdnzClpx7zczBJkLMQ0+ZWvTj+q1ocIEObDbbxIkTbba+TBTQJ5i5BAyeAxaLxe12Y/Gt1DF4AkCqIQFA3xzweDznn3++x2OwKa4OpcvL1NLS4nA4Xn311V27djkcjviPaLfbq6qqXnrpJbvd3tLSv/Mxg0ZprG80FQ/Oo79gNndwsaWxvkEdes1zfz1m2yMzRuUVjp9x59IdIpH31tQ2L732iIqKioqKilFTH99kFtraVUIIYX2Dcvc/3pfnUxrqm1VCCOvJ9dGbRow1yyIIgsr4LvjronNbn710QkHe6OnXv/hDkCgN9U05JcX7l3XnikuLpIb6FoUQQphsVzb9+MOYTCYxKvS+gYGxuVy8dghZEGQ1sLcuWlI+OK67WXLjvua8wUX7Z7rhiwfn++sbpIRiUwONTbIrx8V2fcz9pN3L7j//2FGlJYOHlB5+08pIKBjaX1rHnNPMC0IC1wMGOLvdPmnSJLvdrsvZOY4bMWJEWtwqhhQxfg5IkhSNRtFenSLGTwBIKSQAIAfi190diOeff/66665bu3Ztbw+6du3a66+//tlnn+1DYNA3rC/PJ+6p3kdHKisN1TVRb14uR2yjL3jsza8qG3Z/emvOG3NvfyfADSrIL7p48YYf99uxZ8/bV+QxhBAi1+yqps22cs3OGsY3yNNVi4C59PR7Xv5sS93eb54cs+a3v1tUTXLzvP7qGtr6TeSaXbWm3EHuXt7vMpvNkri/5VdpC7R3M3kC4yrIt1ZXVXdsZWaISjp91uJ8gzz11bX7C1+pZnddTl5uDyu8H5pS+8G732QffdxovqdjBt655cp3ix5Y+ePu3bt2bnxqmhUfASFNqKra3t6OoiWTGT8HIpHIvn37jBxhWjN+AkBKIQEAORC/Losdi8Xyy1/+cvHixZdeeunKlSvjP+KqVauuuOKKBQsWTJ8+3WKxJCNI6D0m77TZx/7w9weX7oyokZ1LH/jbxmNm/ypfqVr15qfb/ILKOXwFOVksx7GmyZde6nr97oeX7w6pRA7v27zyw2/2j7WWNix4bMmOiBrd+dbDz1VOPe8U96FLa6Xuy3f+9UNDRGGzPPleO89yLDdyxsyhn/75kZV1otq+ZdFDCxt+ef5JvWxzM406fMT2VZ/VyoQIVa8+/1Fzd3/QtpMuOy+68I4n1tQJRAnt+e/arW0qYXO8ruaqna0dHmiaPGtG1puP/uUbvyz7v/nLI29Yzpl1VNyrtaiKLElipLV204pnr51xy38m3nXrdHtPx1SFtnbJO2xknpUhkS0vLFqN2cogXdCpoTAvaCZLlxxgWTYvLw/tKkmXLgkAKYIEAORA/Losrbdu3WqxWE455ZTFixdffvnlK1asiOdwK1euvPLKK1944YWTTz7ZYrH88MMPyQsV4sUwhBB28JwFL5+/774pec5BU+6tn/XSgquGsCRcufSOX40c5HL5xl676dTnHjnLScwT7npnwUmb7zi2ODvbV3bsnGe+ad7/l2OZdvGkjy8Y6Rs04c69sxf//aKCrhqthT0rHz3vsHy3yzNs5sej//zUZUUsP/bmV549/r/XjvU4i0/5P/76JX87xxv/KDj6DMrm/vEm9slfjD3ymGk3bxt7YnG3jd7OaX969/ERy+eM8zo9ZSf+/u0dAiHcyAtuOL3yxqFu96Az59ceLLCzjn/ojUfL3p811O0eOuu90kfeePiEuEexiv++abjVnOUuOuLMW5fK5y768t3/qeB7PCbjm3XfPa7npo476oSTZzzaethkG4YEAgAkFcMwdrsdg0IBAEAvTCRysP0stqE/9uuVK1dedtllq1evHjp0aDe7VVZWTp06ddGiRVOnTu1qH/r12rVrGYaZPn16kp9NLwWDwZ53+rm99fuaic3HhPMG5SY9nh937SH2ngfoB5objxpRQgg5xNhL8cubRp1R/+e9L5/dx/4Cat1zp1asuHrvG7Oz+nag3p63ceHpZf93/Ndr7xiFhoeBYuXKlWVlZVlZ3aWSLMuhUKiqquqoo47q8KMVK1bMmDEjGo2mMkbS2tpaWFhYW1ubnZ2d0hOlHVmWt2/fPnz4cDQGZizj50B7e/ujjz66dnv9+adMnjt3rmHjTFPGTwBIKSQA6J4DbW1t33777cSJE+liEMZksViWLl0a1wjTk0466fPPPxdF8ZJLLvH7/Yfcx+1233XXXZ999llZWVlS44R4KWJ77crX/lU35upxcfduNhRVjvg3v/rmN67xvyvFqzfAAY2Nje++++7ZZ5/t8/n6/+wMw5jNZsy9nMmMnwMMw3Acp6pKc3OzoigoAJLL+AkAKYUEAN1zIBKJ/Pjjj2PGjDFyaU3FO3lTeXn5JZdc8um+HPvIcw65Q3DrZ+QPf3jxxReTF1vGyXXaguGe51S32Q/5oSH60a/LZi0rOP0PC+aWp2V3OLnyqZMOf6j2yDn/e88p/dtUDmBoqqqKoqjX9CEsy5aXl+tyajAI4+eA3W4vLCxkt9bqHcjAZPwEgJRCAgByIH69mBfZ7/fbR57jHPvLrnZo2bEwGSFlLq8nx5v4oy2nv9AQSlosTP685S3zkna4eHDDbv4yeHO/nhIAeqKqaiAQcLlcaLLIWMiBDIcEyHBIAEAOxC8tmzcBAKB/KIpSV1eHeUEzmfFzIBgM1tbWGjnCtGb8BICUQgIAciB+KK0BAAAgjamqKssynSEcjSoAAKAXlNYAAACQ9ui61jzfi5FuAAAASYTSGgAAuoSWQEiXHFBVVZIkvSb8G8DSJQEgRZAAgByIH27uAgBAl1iWLSkp0TsK0FO65ICiKLt375YkyWRKzxUojSpdEgBSBAkAuueAyWQqLi5Oi9d2tFoDABiaw+E44YQTHA6HLmdXFKWhoQGTl2Qy4+eA1WrNzc1Fi0qKGD8BIKWQAKB7Drjd7jPOOMPtdusVQPwyvdW6bl9DXVuEIYxKDnYhYwhDyMEuZQxhCCEqURnCCKLo8Dp2N7XvbYto2wkhDDnwn05UVS1wZeXl+lL6RABgoMrKyho1apReZ1dVtampyePx6BUA6M74OcDzvNVqRWmdIsZPAEgpJAAgB+LXi9I6JycnuPWzrn4a3PqZOzcN7iV0IIiS1dWLotdCCCHE6cuP/yGqqopiay/jAgAAgLhEo9FAIIBR1gAAoK94S+sdO3bcc8895KGH/DsWHnIHd677rrvuqqqqKisrS154AAAAAN0RRbG1tRWlNQAA6Cuu0nrVqlWXXXbZ6tWrX375ZUJI7LtX7NeVlZUnnnjiokWLpk6dmvRAAQCg/zEM43K50NU2k6VLDnAcV1ZWhsW3ki5dEgBSBAkAyIH4dTmN2b59++gXK1asuOyyyxYtWjR06NDuj1VeXr5w4cIrrrhi1apVHQ4CAACJURRFEAS9pg9hWbagoIBlMedl5kqjHGAYBh/+ki6NEgBSAQkAuudAS0vLBx980NLSolcA8evyGo0cOTIaja5YseLyyy9fvHjxKaecEs/hTj755BdeeOHKK69cuXJlNBodO3Zs8kIFAMhETU1NCxcubGpq0uXsiqLs3bsXc8NmsnTJARqnJEl6BzLQpEsCQIogAUD3HBBFsaamRhRFvQKIX5eldTQa/de//nX55Ze/9NJLJ598cvxHPOmkkxYtWjR37tyPP/44Go0mI0gAANCHqqqYICrDGT8HeJ632+2EkHA4bOQ405TxEwBSCgkAyIH4ddeyf/XVVz/zzDNTpkzp7UGnTJny9NNPX3PNNX0IDAAAAKBnVqvV4/GgKzgAAOiry9k+3G53S0vLhRdeaLfbd+zY4XA44jxiMBgcOnRoKBSiB0lOmAAAAACHIsuyKIpoUAEAAH11WVrX1tZqX/eqA4Ddbq+rq0OfAQCAAYBhGK/Xi/bATGb8HAiHw3V1daqKsaApYfwEgJRCAgByIH5YowIAALrEsmxubq7eUYCe0iUHGIbNzc3lOE7vQAaadEkASBEkACAH4oeZ9AEAoEuKolRXV2Nu2EyWLjnAsozT6cQSQUmXLgkAKYIEAORA/PAOBAAAXVJVNRgMYoxPJkuXHFBVta2tDR/+ki5dEgBSBAkAuueA1WqtqKiwWq16BRC/TO8QbuL5SEtjqs/icWWl+hQAMFC5XK7TTz/d5XLpHQiAcTEMoyhKQ0ODLMtouAYAGEicTueJJ56odxRxyfTSuiAvtyBP7yAAALpmNptLSkr0jgLAuBwOR3FxMbu9Tu9AAAAgo+HOLgAAdIll2fz8fDQDZjLkQIZDAmQ4JAAgB+KX6a3WAADQDYZh3G633lGAnoyfA6FQqL6+HqOsU8T4CQAphQQA5ED8erj9oKqqoiTyD7MdAAAMAIqi7NixA0VLJjN+DiiKIggCwzBWqxUrryad8RMAUgoJAMiB+HXZai2K0psr13y9aVtUEBM4rtnETxo9/JwTjzaZ0DAOAJA4SZJaW1uzs7N5XoeXU1VVBUHA3dJMli45wLJsYWGhLn8mA1u6JACkCBIAdM+B5ubm5cuXT5s2zePx6BVDnLpstX5r1ZdffLcpsbqaECKI0r/Xb1m6+j+JBgYAAIQQ4vf7lyxZ4vf79Q4EwOhUFZ3mAAAGGlmW/X6/LMt6B9KzLkvrrzdt7fvRv9m8re8HAQAAAOieLMtVVVWSJOkdCAAAZKguS+tIVOj70RNu9AYAACNgWba4uBjzgmYy4+eAxWLxer0YZZ0ixk8ASCkkACAH4tfDkCS2D29UCjplxS3+DwTo6gYA/YlhGIfDoXcUoCfj54DJZLLZbCitU8T4CQAphQQA5ED8uiutGYZ55s7/SewWhaIo1z7yv6gD41RVvac53PP4gWg0Mn5YidVq7YeQAAAIIbIsV1ZWDh06lOM4vWMBfRg/BwRBaGtrw0eOFDF+AkBKIQEAORA/TKRpCLJKbDm+nvcLBjHxPQD0M7zsgMFzQBCElpYWlNapY/AEgFRDAgByIE4JltaqqtI2Vq43nZkBAAAAUoFl2cGDB2PxLQAA0Esi70AfNrXP2VrXLCmEEA/PLhyZ/ysv+t8DAACAbhiG4Xket/sBAAYYm802ceJEm82mdyA960VpLavqN22Rqoh4zbb6sLK/21WzpFy4Ze+zI/LKrKZJTiuHtzQAgKTKycmZPXt2dna2LmdnWbasrAzzgmaydMkBRVH27dsnSRIarpMrXRIAUgQJALrngN1unzRpkl5n75VevP1c8WPd6w1tnbeHFfXyH+sIIefnOl8aVZC00AAAgBCe5z0ej74B6Hh2MAKD5wDHcXSCz/b2doy4TgWDJwCkGhIAkANx6sXthzdi6uo7S30tJ1S0nFBxZ6nvkDsAAMAAoCjK9u3bMX9JJjN+DmRlZeXm5qIreIoYPwEgpZAAgByIXy9Ka+1ynuqxP1w+yMVzLp57uHzQqR57hx0AAAAA+oeqqrIso7kaAAD0lUin+SOzs7R7wwzDHJmdldSQAAAAAOIVDAZra2tVFXf4AQBAT4mU1msDYW0sk6qqawPhpIYEAAAHCYJQXV0tCILegQAYGsOwHo8Hky0BAAwwjY2NCxYsaGxs1DuQniXyDrTKH7z5p/p6QaoXpJt/ql/lDyY9LACAvlizZs2sWbOGDRvmcDjef/99vcPpk0AgsGzZskAgoMvZWZYdPnw4ypVMli45wLKM2+3mOE7vQAaadEkASBEkAOieA6qqiqKYFrNU9uIa2dmDE4Q8ubsp/4ut+V9sfXJ30yF3AADQUSgUGjt27JNPPql3IAOBJEl6hwA6S4scUFU1GAxiop1USIsEgNRBAgByIE69KK0fK8/N5liOkEP+y+bYx8pzUxcoAED8Tj311Pvuu++ss87SO5C0pyhKVVUVypVMli45oChKfX29LMt6BzLQpEsCQIogAQA5EL8u1yjrvIbFvEL3vEJ3asMBAOgv0Wg0Go3Sr9va2gghsizTz+UMw7AsqyiK1vuo8xaWZRmG6bwl9pM97T0V+27UeQvHcaqqalvoiWK30OPTOZC7CiZ14VEdTt054O6D0fHqIbykhEcO5IAxw7NarQUFBcz2OrrdaOEZ/JcbT3jkQAIYMzyDX72BEZ6WAMYMz+BXL93D03JAr/Do8WM/CBnn6nUYhdRlaW01m8NRYUdNXVlxflf7dKOqpk5V1SyLOYHHAgD0gyeeeOLRRx+N3VJZWelwOAghLperoKCgvr5eG+Hs9Xpzc3P37NkTDO6fXSI/P9/tdu/cuVObYKy4uNjhcFRWVmov4mX/z959x0dR5g0Af54pW7Mtu5tN7yEJBFBAujTpNjgpKmIBznbvYefs/dTzBPspKlYEFaTYRbo0KUoPSUhvm7LZ3qY97x+LcQkkmdTdJM/3wx9hdnbm2dnfPjNPT0mhKKqgoKDxFBkZGRzHFRcXB/5LEES/fv3cbndFRUVgi0QiSU1NtdvtZvO5cgJN0wAAr9fbeJzuTF5aWhoAoKioqLnkKZXKhIQEi8VisVi6P3mtXj2cvI4nz2q1NsZAGCYvcPVsNhvHcYHKsjBMXth+uWKSV11d3RgAYZi8ML96vSB5JSUljQEQhskL86vXm5JXUlISwuQpFAqEUBhevaysLBAE+ny+xv8Ejw5f98uv2w8dAx0zYejA2RNHBR828PeBAwcghNOmTevg8Tuo8ZK1A4Tw1z9Oy6X00Oz0jqeksLyKleta3c3jdmcZFQqFIvBfpVLZ8VNjWFfbtm1bSkqKXN7SKn08z3s8nuLi4hEjRjR5aevWrbNmzWpsXm6riIiItWvXXn311Re+1KTVOjMzs7y8XK1WgzCrorZYLBs2bPjb3/6m1+ubS0yXtlqfPXs2JSUlUC8bJjXo4q8eTl7Hk8eybFFRUSAGwjB5giB4PJ4VK1bsPFEydWi/Bx54IPDG8EleOH+5YpIXHABhmLwwv3q9IHkMwxQXFzcGQLglL8yvXu9IHs/zgRiQSCQhSV59ff3GjRuDH4TC5+o1tlpLpdJNmzY122o9a+IoAMCBE2cYtj3D1iU0NXxAv6vHDW/He8Ofz+fLLTMrdQYg8CeKK5u8CgFAzfzRZJ/G7QzHK/Hq4BjWvaRSqVQqDd5CkmRwxx7igskwxWy5cILiVrdACFvYAiG86D7dlrzMzMwmO3RWYrrh6uHkdTx5NE03iYGwSl7gRIFqMpqmA09F4Za8jm8JYfLEBAC+er04eRKJpEkAhFXywvzq9Y7kkSTZGAMhSV7gpQtPdGGCQ3v1QAsdwimSnDN57JzJYxFCwc3O4PzG7eb+7t0EQfBzgkxOAADYdlU9NEU0+0VgGIaFEELI7XYrlUp44QwcWIRL638AACAASURBVN/QU2KAJMmEhASKwvfTTtZTAgDrIjgAsJDHQERExPjx4wND9sJc63eg9l3E3l3MViqVowakHc4vk1DEpRkJHT/g2bJKtuNHwTDsTy6Xq3F4cGlp6fHjx3U6XUJCJ/xau59er1+0aFGoCgyCIFRUVGRkZODlgvssHAN9HA6APg4HABbyGJDL5dnZ2SE5dVvhyt32QAg11jj07koEDOuhfv/995kzZwb+fvjhhwEACxYsWLlyZUgT1U4EQUgkeEpIDGsFz/OlpaUsywZm/sMwDMOwboaL1hiG9ULjxo1zuVyhTgWGYd1BIpFotVoIzXhRawzDMCyEmg7IxrCWsLuWpl361LHOGF7e3ZD1k6u113xqC3U6zunBVxLrWyCEEokED7Hry8I/BiQSiUqlCucU9mjhHwBYl8IBgOEYEA8XrXsX/sSzl6pmfe4MdTraz//1fHX/Rw93SpGT3Xt/v/7/Ohg4FlSMuOPlO0YoOnA8VPXuZBmksx4++OfIeGRZO0dHkNG3b2E6nNrm8WdeGhGz6Idzp0B1q2ZowqeOAOvtCIJITU29cGJMrO8I/xhgWdbtduPxWV0k/AMA61I4ALCQx4AgCAzDNFkZNDzh3wnWV0izrrr9ysyOjlglU1KFjWv2BxZDRrXfrD1oTDfinxHWlbxeb25urtfrDcnZEUI2mw0XWvqy8I8Bv9/f0NAQzins0cI/ALAuhQMAC3kMWCyWDz/80GKxhCoB4uEyQe8lmLc+O3tQjCZCEzNw1jNbqnkAALL8+tLfBsfqtLrIqJQRD/5sBwAAtuz7J64dFKuNUEcPuu7FXXUIAIDMK6eopz7wwsLJl4+6JHPgjMe3mP+sKRIa9rz8t4EmVUTU4AXvnfQBAID7j3dvHpGo1+oijYmXLv6iQgAAMEUbHpyWGaVW6RIuW/DafisCAAD/hht0Y+55+R8zR1+Wk5o26q6vSpofF+ffeGPkxLfKA6dl99yXMfiJ3zkA+JPPD0uc9/Sz108YeWlmyoBrXt5vD/zSPac/v2datkmj0kSlX/7QD/W+75cuWFVyduWs9OTkfjd8Wt0Q1CHcffLjO8el6lUqfcrlt390wi0+bWTynOsTf1iz2wsAECo2rD0zdcFkVeAlVL/lqauHphg1EWpTzlVP/FQltHLYi1xJ/tSqW8dnx0aqVJHJoxd/cNwNALvvqQX/PVa37rZ+ycnp05d/v2LBY7tc2x4YnJycOuKJfaxQsv7eqYMSDBqVNn7o/OX7Gv78+jTTH1mxaMqoodkp/SY9/HMNviX2XC6Xa9euXaEaOi4Igtls7hFVxVgX6SkxQBBEXFwcXnyr0/WUAMC6CA4ADMeAeLho3VsJZR8uvmG19rG9ZmfNvicj1yxY9GGpwOe9/9D/6IcOmG3W+pI97y7IlALAHvvPdYu357x6uMZRs/8509qF/1xXFyiGMbt3OO/asHv/H4c/GPrzbXevMSMAAODy139LPLi9wlry1ZRTD/9rTQ1CNV889pT5xi3lVqul4vDqu4eoIeBOrbjx79sHvHG03l724xLP8rlLNzUEjsoe3lo+65M9h06eWDN+371P/9CO4oJQs/23yKd/OvBH7p5lxKv3v5vPA+D85cGrHzg17t2jtc6Gkp2vXZcmlV75xueLk9Pv2HS2pCR/7c3Rfw0Q8e55cs5DedM+z7daC9bOOPuvOY//6hWdNiJu9g1ZW9dsdQKheN0X5VfeOE7+50tIMWDxB/tLGxxVux5Sf37bss120NJhL7ySACBomvzMd7lmR0PuJ1fkPrz49VM8PfqZzx8abJz7UX5JydmfHrjy/s//PT7iiuXHSkqKfntuNI2AZvj9Xx6ttNqKN9/kfOXm5/YEWtSBf89O511f7zty+sirqV8tfeUAXt4Nw7BeDUIolUrxaEAMwzAsVHDRupdCNT9++euAfz4zN1UOZSnXPbt04N4vfzADWkL7q08fy6/xQUXcpZcmywB7+LNPq6958okrYqWEPOWaJ/6RtW3Dbg8AAAB6+C13jFRDAFWX3XFLvx3rt9oQAICImv3QfaONNGUYe+NV8XnH8jhA0rRQl3f0dKUbSaMGDO2nhnzexnUFE5c9OS1WQqhyFj+9SP/9F9vdAAAAqIEL7ppkJABQXjplDH3qeFnb53OF6mm3L8qSAUDEXDElO//YaQ54tn+6TrbopX+Nj5ECUhE/dGSmqtmnK/bgVxs8cx57YJSeoiJHPvD4XN+GLwMFT1FpI6KuueHS3Wt+rs/94kvrrBtHShuTZRw7b9al0QqSiMhacN98/aE9p9iWPvJFriSg+l9104Q0jQTSpvEP3j2mYM9v1laam8nkKTdOz9ZLCVI39O6lV3r37zvXLC4Zs+jOYWoIYOTEqUNrj59owO3WGIb1ZoIgWCwWPEk41vsghOx2e2Fh4aFDh86cOZObm7t///7S0lK32925J/J6vRUVFaWlpXl5efv379+2bVt+fj7uDY5h4uF+U72UUF9TT8cnmgJ1J4QxMV5aX1OH0u5c+Xrtky/Myr7JkzTxtqdWPDMrtrqiqmH7XZfsPjcImXFKLnG6EAAAEIaoc6OICYPJINTVNCAAABFpNATWi4cyuZSxMAgarn/949LH/7twyO31hlE3PLz8xZsz62osuoR4ZeCYZHxyHPdrjU0ARgCgWqMOlHohTdOsn2l7dg0VGg3VeAieYXhkrzb7E8YnilrInq+vbTANjju37ikVnxhtPVzHASA2bTBy5o2jHv/whTfL+Dmrh9DOL85tR44jHzz82DtbTtexBMk5atA8d+D9zRz2wisJAFf2/fMPvfDlb2VuRBK+el/2ZA8CupY+DarbveKhpz/eXWDjSZKxNhiWes4dvvGsJE1DhmEQALgxB2szCKFSqcQtgX1Z+MdAYO33QPFDEASSFHUvwEQK/wDo3axW6549e/bu33+2pJJBkKSlCCGB9UtJNCAjbfz4cZdddllEREQHz+L1eo8ePbpjx44TufksoHhI8jyPeE4ChZT46OysTL1eHxUV1SmfCOtxcCYgHm617qUIg8nAVpbXBkZFCHXlFX69yUgCRf/rX1q/v7CubMcy3brFD2+0k1Ex0XELPjl+5pyiysoNt5ogAADwFaXlgdp/vqKkAhqiIpv7SUmSr3xi9c5cc/WhFQP2/POej8uB0aS3llcEWr8BX1FaRRujtG2MNolEwrHnujELTrurhREeUBMTLSsvLm/aWAEBAhcUj0lDVGRNedW5Sci5ijKzzmRsSyUT1ExdMPH3Nz9WzLu+/18PcNyJV5c8XXntp4eLyktLzn57dzrR9koD+8aHbtsc98y2M2VlpSUnXp0iC9QTQwCCKowhhH99KGbnMze/A+7YfKK0vLQk/9Pr9fDCD4xhHUAQREJCAp4bti8L/xhQKBQmkymcU9ijhX8A9GJWq3XFq6++//n6w4W1PkCRAJGcj+L9BAQuntqTW/76ex+vXLmSZTs06MtisaxateqVN9/ZcazQyhAsx5KcT4JYCiKWkBwta9j4y6/vr1pls+HFSfoonAmIh69RLwVNM+aNOfn2s5tKfMhXsumZN0+MnjczWijevn5HvpVBZIQhRicnSJKghy9cqPny8ed/KfMgwHtrT2/74dC5sdbc8VUvfVXkQ/6Sr59fWThxzmTtxYvWgnnfxp9O1vkEQh4ZrVdSBEmQmbOuS9ux/IVtZha5cj9+7sO66fMnKdv2Eejswf0Ktu+s4gFgite+/2OLHZoVk26e4//wkVf2mBkgeCp/P5DnRIDQ6TUNxSWOJm+kh8+dJV//4muHrDxvPfTaC+uks+eOoNuUNuXkZ7/fsu2T2zOCmkaQy+lRxPdL1lJAqNv2wfr8tndKRIzTxenTM00yCHy5H328ywcAAIDQ6rXe0qKawAGhMlJHVxaVBNbiElwOryYlK15JALZs3Qff1+EpJrDOJQhCXV0dnrykL8Mx0MfhAAgJhJDT6Vz+6usH86tIWjL1suxld97y7KMPvvqf51e89NwTDy69ff7VI7PiBUq6/ciZt//3jt/vb9+JBEHYsHHjj3uO+KBsYJJxwVUTH7337uUvPvvay/9+/vFlT91/17VjLyEJuOtY0eNPP3vy5EkcCX0QzgTEw0XrXghCAACRuGjV6vm1T400qaJGPlkz97NVS5II4C3c9MjMzCiNxpBz16mpK1+4RgUkQx7buGrS6UfGxKvVhpQxi/53qOHcL0c6ZcFlP1+faYga8mj1vE/evjGmuUZrpnLbi3MGRWs1kenX/dx/+as3xxFUzoNr3r3897tyIlXxk9+hln715my9+G4kgU+QsvjlB4gV43KGjZ7yYH7OhPgWg1U15b+b/9Pvl0UD9arIlAn3bihiACAzr7/vysL707TaqKs/qPqrgC2//Ll1L6Z8OzdNq02b+03yC+ueH9/W5a7p2Esmjul3Xl0DPWLpizNO3D3k0rGTpv79O+OorLaPtoCGuU89oVk5ceCI8VfMetExaLgCAgAAjLpm6RLm5cGRWsOYF0/yskl332dcPTlKq89+YDc59ZGXBn43Z9CwcZNmPng8bVQs/kljnQshZLFY8EC7viz8Y8DlcpWXl+PHvi4S/gHQK1VUVDz/wouHCiojCGbujIkP3n/fuHHjBg4cmJCQkJiYOHTo0NmzZz/71JNzpoylBGbLbyeWL1/e0NDQ1rN4vd5NmzZ9t+sgAGjWhMv+8+/nFi5cOGLEiKSkpPj4+AEDBgwdOvSuu+68auJoDeE7U+1Y8fqb+fn5XfF5sXCGMwHxoM/na/xP8CVr7m+Ru7X894EDByCE06ZN62DqO6gj0z9ACA/nl0koYmBKXMdTUlhexcpbHFELAADA43ZnGRUKxblyoFJ5QTswu++B7Ktqllevvlba9KW2QeaVU7O2/r163Tx56zt3IlT/4ZUp71x+8MAj2XisXG+xbdu2lJQUubylUOJ53uPxFBcXjxgxoslLW7dunTVrVrvr40VyOByxsbFVVVVqtbpLT9QO9fX1mzdvvvbaaw0GQ/efnef5goKCjIwMPH61zwr/GHC5XC+++OKBAvOcicNuv/32sE1nDxX+AdD7MAzz3nvvfffrERkh3HTd1VdddZVMJrvonk6n8/PPP/92xwEI0cLZM+fOnSu+1y5C6Jdffnn34zUeHk4a2n/JksWRkZEX7sbz/JkzZ8rKylZ9ttbOURMvybjnnqUXeQTFeq+QZwIMw9TU1JhMJolEEpIEiCGVSjdt2oSbuHoPgXVVbPviJ/OAYQPb1rs5XCDe13By7fpDmkuHJOPbN4b9yWAwLF68OCTlagzrQQiC0Ov1uPiH9QKlpaW79x8UEBx9ac6MGTOaK1cDAFQq1Zw5c3JSYrwCtevXvXV1deLPYrfbt+/YaefIVGPEjTfecNFydQBJkuPHj588bjQF0eETuWfOnGnb58GwjpFIJAkJCeFcrm6Ei9ZhAQm8vaG+1X8uWwtdffw/3p7S7+ad/f+9YnFqj/xa+cJXJyWMXe6+6a0nJndvUzmGYc2CEGo0GjwvaF/WU2IAIeTz+XC38E7XUwKg10AIHTp0yMWRKoqbPn1aq+3Der1+yuQrlARXUW/Pzc0Vf6KysrL8kgoZwU8Yd3lcXLN9MAMBIJPJpk2bZlCSboE6cOAAXuWuT8GZgHh48a2wkJ6UkN7RY0iv/KjO0xmJAQAAAKPv+MV2R6cdTgwy/cF97ge79ZQYhrWGIIiYmJhQpwILpZ4SA4IgVFVV8TyPp7HtXD0lAHoNhmGKi4sZQE4dcUlWVlar+0MIL7nkknjjd4W1jvz8/PHjx4ssAuXl5fkQqZbA4cOHt/CWxgBISkqaOnH8p9/v9nq9uJTVp+BMQDx8+8EwDMOaJQhCdXU1bgnsy8I/BhQKRXR0NIT4kaZLhH8A9DJSqVSuUJIEkSi6B6zBYMjJ7ocIqk1Niy6XiwdEVkZacnJyC7s1BkBgBSYJSTQ43LgCq0/BmYB4+IeBYRiGNQshZLfb8bygfVlHYsDn89ntdrvd7na7u+6xjCAImqZxK1oXwZlA9/MzDEKC1WoVuT/P84ESdZum/DQajZAgFfJmB3IHBAdAdXW1wLEWS4PL5RJ/IqynC3kmwHFcQ0MDx3GhSoB4uEM4hmFYWHO73adPn+7fvz+ekRXrKXw+H8/zx44d27t3b21dPUJIIZcZjcZJkyYNGDCgK06HF4bBeg2n09nQYEWCIH4hG4ZhamtreZ6vrq5mGEZMWzf6k7m62mazabVaMSfieR5A4PF6XC5XRESEyORhWAdZrdavv/76uuuuMxqNoU5LK3DRGsMwLKx5PJ4jR44kJyfjojUW/gLr9HzzzbenzpY4/IKb4QFCAAKEAJVXuefIyeGDsqZMviIjI6PlBfnahOM4j8eDEMLTg2O9AMdxDNOe9SYRQn6/X3z3kLNnz+LZyDCsc+GiNYZhGNYsCKFer8cz1vRl4mOAZdmdO3d+tuZLswdRBFASbE56gk6nJQhCEITaBltJdf1PB0/vPXx0+oQx8+fP12g0nZhOkiSTkpJoumcuPxnGcCbQx+EAwHAMiIeL1hiGYVizCIII//5XWJcSGQOCIOzdu/f9jz+zcpIEnXzerKvS09NNJpNcLicIgud5iqIOHjz44SefFlv4b7fvRQgtXLhQoVB0w0fAOgJnAn0cDgAMx4B4uGh9DoTQXFsXND4eAoDOfx38OYwLBapteAH5/UxldU1gS+DVwG4QgosN+oI0TUUZ9F36QTAMwzqRIAiVlZVxcXF4Ptg+S2QMeL3eL9dtsLB0XAR46pGHkpKSgl8NvHf48OF6vX7Vhx8dOVv90869iYmJ06dP76yWEJ7ny8vLWZbFDdedC2cCIdHWwkzXjYYIDoDExESKJCUSiUzWyuRnWG+CMwHxcNH6L5U2r1zbhnKvKlIFAHC05RQ+mwUXrTEM60EQQm63G08Q1ZeJjIE9e/aU23wakl14w8L4+PiL7gMhTE9P//uSxfZXX8urcW/Zum3ixIkdf0aXSCRqtRpCM8uyHTwUdiGcCXQzuVyuUquJGpfIqcUCb4mPjydPlhkMhjZVLUEIo6KiVCpVC/sEB4BMJoMEEaGMEJ82rBfAmYB4uO4hCASwi4X6E2IYhmFY56utrd3wzfcMx48aMnDs2LEtN6ClpKRcc+VMFSVUWFyVlZUdP7tEImnTcr4YFs5kMplSoUAItXWBKwhBRESE+Obr5ORkkiRJihL/lsA85LzA94hlkDCs++GiNYZhGIZhHXL06NEqi0NBcBMmTGh16m8I4ZQpU4YOyHAwwr59+zr+jM5xnM/nwy0qWO/AMIzX6+MFobq6WuRbfD5fRUUFzwsOh0PkDwpCSNM0QMBmtYpf5ctmswmCYLfb6+vrRb4Fw/oUXLTGMAwLayRJ6nS6UK0qRBBEdHQ0Hl7Vl4mJgZKSEgYRRq2qyRDr5pAkmdM/Gwnoj6PH2to0dyGfz1dXV4eL1l0EZwLdzO122+020JZ4RgjxPI+Q0NDQILJojRAym828wHu93pZHUuAAwEIeAzqdbt68eTqdLlQJEA+PtcYwDAtrkZGR8+fPD9XZIYR4TF0f12oMCILgdDoFALMy0qKiokQe1mazQcQ7Pb7OGiAdePjDS1t3OpwJ9FZOp1NMhRQOACzkMUBRVGRkZAgTIB6ugsIwDMOaJQhCUVGRIAihTggWMq3GADoHUFTbirUQAEEQOqu1GUKoUChw21qnw5lAH4cDAMMxIB6+A2EYhmHNQggxDIO72vZlrcYAhDAiIgIAYDabnU5nNybtPIIg2Gw2nudDlYDeCmcCoQFhmCxwFRwAWq0W1171QTgTEA//PDAMwzAMaz+CIEwmE0EQPN/mJmiCIDr+pA4hpCgKIdTQ0IDbVbCejiRJmqJJkkxISBD5FolEEhUVRRCEXC5v0w8KAhihUokvw0dFRZEkGanTmUwm8WfBsL4DF60xDMMwDGu/cw0aoD0NGpF6vcFg6GAClEplTEwMbkzDege1Wq036Nu0mBxJkoFCtclkkkgkIt+l1+sJglC1pWjt9XqRIACAF5TFuhXDMOXl5QzDhDohrcP3IQzDsLDmdDp37twZqn62BEHEx8fjQktf1moMCIJQXl4u8G1rLk5ISCDbODYbCwmcCYQEz3Fnz54VuTPLsjabDSEkfvICCGFkZCQkoN/XylSCwQFQXl7O8bzD4bBYLCLThvUCIc8E7Hb7999/b7fbQ5UA8XBGiWEYFtZ8Pt+ZM2d8Pl9Izh4YRosbKPqyLooBuVwOIWywWDq+QK7b7a6qqsJdwbsIzgS6mdVqNZvN5+YGFIfjOLvdzvN8WVmZ3+8X8xaEkNvtFgShoaGh5XWtLwwAjuc6a2J/rEfAmYB4uGiNYRiGNYvn+fz8fDw1VF8mMgYghEajQaFQiDxsXV2dwAsBHUxhYFFf/PDXRXAm0FuJrJDCAYDhGBAPr2sdBAE89x2GYVgTuDEQExkDEomEosQ+V1it1s4NLYIgoqKixCcAEw9nAn0cDgAMx4BI+A70l1iNjOMcXXoKvUbepcfHMAzDsJBAANjtdp/PF6oVgxBCLMsihHDDNYZ1EZLE8yNgWEtw0fochFB0lDHUqcAwDMOwHoYkybS0NPJwnsvlZhgmVEXrwGxqHMfRNB2SBGBYJyJJMikpSfzOSqWyfZVKFEWJn54qLS2NpncqlXK1Wt2Oc2FYr4fHWmMYhmHNIggiJSUFTw7cl3VpDGg0Gq1W28GDyOXyqKgo3FjdRXAm0M3UarXBYIQQil9GSyKRGAyGwFLYUqlU/LsgJIxRUS2Xky8MAKlEKn5WBawXwJmAePgaYRiGYS3Bg1exlmOA5/mKigq+jSPxtFotQRC0RCK+/NAckiSlUikuWncdnAl0J5IkKYoUBKGmpkbkWwRBCEzZTZKkyB8ChDApKYkkRRUEGgOgpKSEY1m/3+/xeESmDesdcCYgUp++TEqlMtRJwDAMawVN0/Hx8aHq4yoIQkFBQUZGBh5i12eJiQG/3w/aOA9oVFQUQRIet9vlckVERHQkhX6/P7Cob0cOgjUHZwLdzO122+12QRAcDrETAPn9/qqqKp7na2trGYYRX12FEKqpqbHb7RqNprl9ggOAYRgEgNvjdjgcuOG67wh5JqDX6xctWtQjivc9IIkYhmF9mVarveqqq0KdCgxrDYRKpUJ8HZDX60UIeTwej8fTwaI1y7JOpxMXrbHegWEYr9fbjjcGflAiZ3JGCBUVFfE8L/A8/u1gYY4giI73b+oeuEM4hmEYhmEdBQHQarVyudiFMMrLy3muM1dJJUkyJSWlRzRrYFg4wMsUY1inw3cgAAAoq6hav+8MJM+7GlMGxORkZYQqSRiGYRjWs7Asy/N8CLsNQwjxiGsMwzAsVHDRGgAAWI4TlEZSct6cin6WC1V6MAzDwgRBEBkZGXhe0L6s1RggCMJkMkF4pra2zu12h2pVHkEQqqqqOI7DDdedC2cCIUEQRGRkZKhTAcD5ARAXF0eSJEVRPaV3LtYpcCYgHr5GGIZhWEs4Dtcz9nUtxwCEMCIion3NxTKZrOPrYFMUFRit7fP58KjRroAzge4kk8mUEREEQej1epFvkcvlSUlJJElGRka2qWoJQqjXG1qd7KAxAJRKJUEQapU6TIr9WLcJbSbg9Xpzc3PbNwdBN8NFawzDsLBms9m+++47m80WkrMLglBcXCxyXhysV2o1BgRBsNvtbS3TkiQJIIhQqTreyi2TyXQ6He4K3kVwJtDN5HK5KiICIdTWggSEUK1Wiy9aJyQkkAQpkUpafktwAFgsFp7nEUA4HvqUkGcCLpdr165dLpcrVAkQDxetMQzDwhrLshUVFYE1SzEsDCGE6uvr2/rUlZaWRlEUz3EdbwzheZ5hGNxejfUOLMv6/QwvCBUVFSLf4vf7q6urBUFwu90iJyeDEAY6jDgcDvFleIvFIgiC1Wqtra0V+RYM61Nw0Tp88YLAcXyTf3g6RwzDMCw8EW1vN7ZarQ0NDR08r9frrampwUVrrHdwuVxWa0ObFooXBIFhGEEQ6urqRNbDIoTq6up4QXC7XH6/v72JxTDsPHiqj/C1bsveQlvTRgAl8tx7w4yQpAfDsL4Jz1yCiYkBCGFMbIz43t0lJSUcx3XicwhBEEajMYTzk/diOBPolWw2G0KiOpvgAMBwDIiEi9bhi6BlCr2myUbKXROSxGAY1jeRJNmvX79QpwILpS6KAYZhQKc2M0MIVSoVfv7rdDgT6ONwAGA4BsTDdyAMwzCsWQghl8uFu9r2Za3GAIRQLpcDAGpr60I4zYwgIIfDgWdX6nQ4EwgVmqZDnQQAzg8AjUZDQMiyLP6h9Sk4ExAPt1pjGIZhzRIEoaKiIiMjA/ez7bNajQGCIGJjYwniaHseuDtpWm8IIUJCfX09z/Od2HDt9XqDpzgJrFHk8XiCP6ZUKqVpmmEYhmEaN5IkKZfLBUHweDzBB1QqlRDCJoeVSCQSiYRl2eAhrwRBKBQKAECT2gqFQkEQhM/nC57+LXCEjn/ei+qeTMDv9wcPEqZpWiqV8jwfPMMWhFCpVAIA3G538FO+TCajKKrJV0BRlEwmu/AraOuXiBByu93BWzrrIzeHIAiSJEmKSk5OFvkWmqYNBgNBlEskkjbFPwRQoVS0HDzBAZCTkyOTfEdSNF58q0/BTwLi4aI1hmEYhmHthxASBKF9zRkGgyEqKqqDCYiIiIiPjycKzB08ThNer3f//v0+ny/wXwjhgAEDAACFhYXB5b2EhASNRlNfX282/5UAtVqdmJjIcdyZM2eCj5mdnU2SZElJSXCBOSYmRq/X22y24BmhFQpFamoqAODkyZPBR8jIyJBKpRUVFcEL8slksrFjx3Zd6bqr2e323Nzc4AntDAZDdHS0x+MpKipq3EhRVFZWFgAgPz8/uAyclJSkUqlqa2uDp63WarXx8fF+ykVV+QAAIABJREFUv7+goCD4XDk5OQCAoqKi4CJ3fHy8Vqu1WCzV1dWNGyMiIpKTk3mez83NDWyRyWSjRo3q6tK1RqMxGo2w2iH+LRRFBVacjo2NFR8GGo0GElCj0QYqccSIiYmRU9DFCLm5uZdccgle8Q7rHhBCmqZ7RLzhojWGYVhYk8lkWVlZgVVSMCwMnVvytI0LWMTFxYV5A4hEIomJiamurm58nisvLwdBHeADrFarw+HgeT54I8dx5eXlCKEmxbDKykoIoSAIwdudTqfH42lyhMbTNdlYU1MDIeQ4rnE7wzA+n49hmB5atOZ53mw2+3y+4E/q9XrLy8ubXCjw5zWhKCo4eBoaGmw2W5MLyLLsRb+CwBHA+RfWZrM5nc4mRxAEIfgILMv6fL5uW6iF57ji4mKRO7Ms63A42tRfF0IYFRVFEATLMBzHiVwN2+/3x0dFHi6q/XzNF7GxsSaTSfwZMazdDAbD4sWLQ50KUXDRGsMwLKypVKoJEyaE6uwQQolE0iOqirEu0kUxoFQqIQFtVmtDQ0MHO5e63W6z2dzpgz9JklQqlVKptMn2i46AJUnywpqCwKW7cGfxRwAAXPQITQpC4tclboeuzgS8Xm9hYaFcLr/wkxIE0cEL2PGvoPEINE3HxsaKb+BtN5vNVltbhxASv+Q7x3FWq5Xn+dLSUp/PJ6YqFiHk9/uRgOrr610ul1arbW7P4ABQq9Vjx4w+Xbwut6xm8+bNN910UzdcECzk8JOAeLhojWEYhjWLIIhAr1SszxIZAxBCvT5SfF9Zi8Ui8ALHceLLD81BCLEsG2hM7sSHP5/PV1lZKb5BL1QIgtBoNF2XSJwJBEAIKYrqhinoWZb1eH0QXrxWpWVtarguKyvjhdYb4YMDAEI4duzYw4eP7Dtd+uOOPVKp9Nprr22hWN5uXq8XIRSoM3K73YFx+G63O9DPnyAIlmXDZJq3vgBnAuKF9d0CwzAMCy2EkN1u12g0uLq6zxIfAzKZTPzDrsVi6dx2ZoIgYmJiOrGEGWgJ7IZpqzqIoqiEhISuGzOCM4EAlmXLysqMRmNXh4TdbrfYHDQkjEZjl55IpCYBoNVqFyy40fLW2/k1rvU/bi8sLFqyZHFiYqLH4wmudwhMqMaybHAXepIkaZoWBCF4qDwAIBC9fr8/UDXg8/mOHDnidrt/3r7b5/NZrVaelLAsW1FanJac9OhD9+l0OovF0q9fv8B3EThs91yNvglnAuLhojWGYRjWLEEQzGazSqUK82GxWNcRGQMIAJfL5ff7L+xB3T3OzaaGUF97+As02guC0EUNqjgTCEAIBQbVd/VZjhw54uWhlBTCZCXhCwMgPT192YMPbP7m2+2/HdufV1nw9IvJsVE0gYIrtmJiYqRSqcVicTqdjRvVanVkZKTf7w+eLo4kyYSEBABAZWVloHWaJEmTySQIQrm5nmX8JOARIlmWY3nk49CpU6fq6+sdDsfPP/8cOIJWq01ISIiOjo6IiEhJSQlMAo91IpwJiIeL1s369Yz5SKm1hR18TqtaIWPI8yov68vOGhLTz9vP7xIEgZCrm7x9fFZMZnpKJyUWwzAMw0KDIIjk5GTicJ7d7ghh0VoQhJKSEo7j+lr7FcuyeXl5JpOph5YoIIT4eb2RzWbbtXsPA+mpIwemp6e3/gYAAAAEQchksvZVKhEE0dY3QggTEhL+cfddA/rvXPPlV2ab50hBBSegwNEAAIIgEKfKAAAIIYRQYGOgRTpwLkEQIIQQQoQAQgJxvDiwMXAEWvAT3B+AVhQXFSKEkpKSKAAgQILAl5aVff3TTofDwXjdSn20AMnAMUnihJREEsQMykq/cubMQYMG9dBZ/bCLcrvdp0+f7t+/f2D5vXCGi9bNMilQYkzT8nAwgVfIpBKGO68/m1+XKZWf962TUI0QEsBf2ZbFas/zqVxeX+cmOJSEordmXF/+zO7/jOyZkxjz+S+PnZD3VMmq6e3OiP3f35b0Qs7eXx9I6/JhWK1A1k+uSfl6bsk3N6vOvnL55acfL/1wJr6/9GQNDQ2//PLLlClT8DqiWHgKFI3a11KsUqvV6pZutWLIZDKDwQA7e/EtrHsolcrs7OzGibv7MoTQjh07ymw+o4KcPWuW+J7nUqnUZDKRZGFcXJz4IiVJkhBCU3S0RqNpR2ohhOPGjcvMzDx16lR+fr7dbg8MygAAlJaWBu8ZGxtL03R9fX3jCuEAAI1Go9VqfT5fTU1N40aKouLi4pxO5869v3n5c2dpfLWxT4pSqZASwmWZ8YGKPEEQPF5fvd1lcRO/niw9eeaNOddeefXVV+Mp1noNj8dz5MiR5ORkXLTuwfolxgwdmN0VRy4pr8w7Vt8VRwZAqNu94t5/vf3j0Sqf3Jjcf9TN//7gX+M1zNfzdXM3pj2y7/C/h0kBANzRJ4dMKX2h4pOrzrUt+Hb8X9aU96NfOLF3Wb9A1TGqendK5udXHd91bwq/85/p1/veK3t/WnO5tWvry2+B27YMlwEAuLLvn73niQ+3n67jI6JTB469c/nHdw/pjS0IqG7VzPTNN5R+c7MWAADonJteuF9v7Eg3RH9rX1NbEcYrHnhhyAD8K+/heJ4PTP0akrNDCJVKZV/rYYsFazUGBEGorq5u68BplUoFCSiVSjs+SJiiqM6dwCyApmmj0Ri8+nHfhDOBbsDzfFlZ2U8//bT1wFGe44ZfNiAuLk782xFCgXsETdMixwVACFNTU8kDJ8Xs2VwAkCQZGxsbGxs7efJklmURQoGybpMp66VSKUEQDMME38goiqJpmuf54HHXEEKZTMay7ODBgwsKCjZtIfw+LwV4BGgAIU1LEhPiZ025XKlUUhQ1YcKEQEEr0DYukUh+/fXXDz7+rNZHfbn5h/j4+DFjxoi5FFircCYgHn7o7lWQdeM9c1fYH/ry2C8jDKw5b/+WU5Jzc0USalXV+8+u/cemW2MvzHPd21dvtGuVtWvWnLr/6UFtDQpk/f6D79LnP5NAACCUfbB4wefG5Zvzbhyodlec/PXH0q7o5SXwPGxvG0kXIZKuWJTU8aO09DW1GdRcet1tHT8M1qcRBBEYBYf1Wa3GAELI4/G0aWpiAEBMTAxJkj6vx+PxdLBxiWGYti7qK0agJTDMW1N5nne73X6/v7a21u/36/V6QRAamwEJglAqlcGDXSUSiV6vRwiZzec18huNxuZmgOvqTMDj8RQUFIT5TFQcx3m9XoVC0dDQwDBMZGQkz/O1tbXgzzHYarU6UPCgadpgMAAAgocTgz+vsM1mCy52SqVSt9tdW1v7xx9/7Dt4qNLOkABclpU0f/78NnWS9/l8FRUVPM9bLJY2TZ2NEKqrrXM6nSqVqrl9xARAk0XOLtreftHmdJIkL9yZpulBgwbxPC+nfyUFkiBJDpIkKVfKZXEmo1arlcvlMplMrVY3Oea4ceP0ev2qjz7OrXa8/8malJSUmJgYXCDsOPwkIB4uWvcqfMnR456Rj915eUIEACDxkplLLvnzJSJ54b39v/7P8v3zl49pkoch20+ffUvMWf5M5T3/W/P7Y4OGt/He5t3zw+6Ecc8bIACAyzt6WjH544VDoyUAyFOHz/7HcAAAAP6NN8a8MfrYtv9LIABg99zX/x8RXx557tL6lVP7fT3oYdOxH8/W17pi5i7/6Nmp0QQAwHtm7SNLn1l7oMKv7n/No++8dddQNTCvnJq5cdhTMb+tOVQe93/bN97sXPvIfc9/caDCJzUNuuWNjS/PNLBFGx6965FP91exmqyZD7zx1j2jdBD4N9wQvTzqkUsKNh0sMzeopr249q15ySQA/rzV/7z14a9yWdOwhXfmBJpbUM170zO3LKpaP18BAPBvWhD32ojfty9NJABT8s0z9zz58a5CJ6XPmvXC2vtq7npsl8t5eHDyk6Rp4eo9j1tvD3QIT60NpDP28LpjFWZnzPw31744zQQBQHU7nrvtrjf21CnTpt03m3nh59EHdt2fen4JutmvSTBvff6u+9/ZXuRRpEy8c/k7j0+NIfmTz4+YefyqJcTOH/Kqa5jMuz747KFRmqA7CN/YIRzuWpr1f+5FMxp+2llUXU+MePizVbfnyAAAQt3u5UsfePOnXCsVN2bxK+89f3Uiheq3PH3bI58eOFvnlyWPu/2V956Z3hkFfaxnEgTBYrHo9fpuWHIGC09iYwACuVwmfoJulmUBAi6X2+VydbxobbfbO71ozXGc2+3uuunBOo5hmFOnTpVXVkJIbPruRwCgMiICAWS32QAALMtWV1YmJCUFFy0oioqIUDXuEwABjI+PS06I1UdG5uTkpKenB3cl6OpMQBAEv98fzjOx+/3+kydPlldW2exOp9tN07RSGSEgwWG3AwB8Pn9FeVl8QqJMJgV/XmEAgM123nw9KpWaJEmPxx3cSEvRtN/vZwXoE0gOQDXBjx857IYbro+KimpHOhFCTqeT53kxRWuEUGlpqcALHMe23CsqJHcBjUYzevTo1NRUiUTCMAzLsgRBEASh0+kCyZBIJBeW1SmKGjRo0B1/X7L89TfKHf433nr7wfvvC9R0YB2BnwTEwxcoZGrqLIUlZS38c7rb3AmNShs9Urf9pTv/s+aXI4UN5y1sAGDk1Y8u1a1+9pPSJn32UP13n/2suPaGedffMN781ep9/jaelC85cYpNy0wkAQCAyhl9GbvhyXteX7/zWJldzFqlzO4dzrs27N7/x+EPhv58291rzAgA186Hr30of9onpyz28i13syuuf3RHYHSOf/eW2kVfHzpZsPkO7bYHr37g1Lh3j9Y6G0p2vnZdmhRwp1bc+PftA944Wm8v+3GJZ/ncpZsaAk9a7OGt5bM+2XPo5Ik14/fd+/QPLgC4E6/cdO/xK9aera/+7SXjj2uOtphY5si/Zy/6Jf35vVUOW8XBVYsGavrf+/m/x0dcsfxYSUnRb8+NPv8u5t+z03nX1/uOnD7yaupXS185wAKALBvuuekT3VMHzfUF3ywoXPej86IPgRf/moSyDxffsFr72F6zs2bfk5FrFiz6MPC6ULP9t8infzrwR+6eZcSr97+b3/zdkcv75fSQ17cfPHb6x5vKH394TQ0CQCh+76Z5n+ke2VHhsBx/d9ivdy5eWSwAgBQDFn+wv7TBUbXrIfXnty3bbBfxRWK9FELIYrF0eqEF60FExgAEMDIyUnwhubi4uOMrWgcjCCIpKakTF9/y+XydnshOJAhCbm5uXlGZ0w+qGtxWP6j3cKW1trJau52BTo50CaTV7a91+u0MDPzzAqmTI8vq7IF9Av/cAu0GklOlNRt2/f7xxi1Pv/Dye++9V15e3viN9/FMgOf5U6dO5ZdUeDgIJHKPQNe7udJaW3mdw85AJ096EeXhgAfRgetp8fCltbayOrsXSL1A6mCJwPYKi7O01mb1Iy+Qunjq3M5uzs2TkKQS9Mr5k0fe/4/b//73Je0rV7cDwzAItP61hioAJBJJYmJidHR0YmJiWlpaSkpKUlKSWq2OiIiIiIhobkg5hDA7O/vqGdNlgDtdVHH06NFuTnav1MczgTbBrdYhc9qnPX26pSILpA1trvlQz3hzx+dv/vfd1+9+5USJP2bC7f9d+cLfUs/lPmTWHU9c879/vrzjptf0f71FqNr42Xbd7O/HyA3u66+496HVO14cP70tcwQIDptLHqEMpJWIWfjpDumrr3z4wq1Pn66CqTPvef2dJ6bEtPBB6OG33DFSDQFQXXbHLf0Grd9qW3Ddno++oG754Z4RBgqA/gsfu+WtazYeYidmASAZsej/xuggAMC7/dN1skU//mt8DAUAiB86EgD+1MZ1BROX/TQtVgJAzuKnF/1v4hfb3bPn0ABQAxfcNclIAKC8dMoY+uHjZfxMYfOG4iseXna5gQJg2D0Pzn5jYQtPT+zB1Z/XzX7ruauSFQCAqIGjowBAdc3vLxmz6M5haghA5MSpQ2tXnWhAoyJ2rt8Ss2j3vFQZBHEzHr5jxMdrLv7ei3xNqObHL38d8M+Tc1PlEKRc9+zSd/p/+YN5ye0AQPW02xdlyQAAMVdMyb5/72kOZDYzIp5I+Nud1yXSAIC0yRPj3jmWxwFD8bqPD4361xez05QQKCc+fP+41Hd/Nt99Z+zYebMCb8pacN/8/9605xQ7e3T49tPDMCxM8Dwfwjbec7Op9YHOn4GFcArOFlbW2wECqXH6IUOGIITq6/+axiUiIsJkMh0+fFitVjd2LQ4s5lRaWur3/1WLHhMTo1QqS0pKTp48dTLvrJ2jftj7R3FJ6ZLFi/r3798XrmdzApMI5BecrW5wAgBSYw0mkykyMrLJalImk6mmpkapVAb3AKdpOiUlBQBQWFgY3CacmJgok8lqa2ttf/YaoGk6NTV10KBBuD2wsxAEMWXKlMO/Hz2YX3H06NHx48eH81gDrJfBReuQIekumbVZnnblsnevXAaAv3rf27fPvWVJ8pCt/xcTeA2qpz18X/yoZz/454rG/YXSdav3xlz3zEgpgNKZN0xbevfqLa9Mn92GuSIJtSbCW+X+s5EVqnPmP/Xx/KeA4C7Z+tLN199wT1bel/ObXxCEMEQZzxXLDSaDUFfTINgqqxylW67P+frcjGp+B3eFUwjsYtQTAACA7NVmf8L4xOCRSEJdjUWXEH+uWoCMT47jfq2xCcAIAFRr1IGnA0jTNOtnkGCraTAkJZzrf0YnJMeRZ5v/jHxNdX3M4CTRs4kRjecjaRoyDIOQva6OjY6P+fOjxsU2e7ALvyahvqaejk80nXuzMTFeWl9TxwMSQIVGQzV+MJ5hmm+1JoKugYRiGAYBobq82rPr0VHZzwV24dx0wlC7gCKOfvDwY+9sOV3HEiTnqEHz3LieEsOwZkEIDQYDhNBsrnG5XB2f8bt9AgVOjuM6seE6DLEsW1hYeDL3jJcnJFBITYrLzMwkSXL48OEX9qkeNmzYhUe49NJLL9w4ZMiQGTNmlJWVfffd978ePn6ywrp6zdpnn36qz5ZJWJY9e/bsqdw8j0DIoJCWnJCRkYEQGjduXJsWObvo1ca6WkRExJBLBh0qqKo217rdbq1WG+oUYX1Fb779hDmdt8JkaGkpnbNmB6eKbvfxpTGj/7Fk4nN3nSzgQMyfG4m0xU/Oe+O2F3689lxZic/74vMDnrIT1yZ8CgEAvMfeQK7+3jLrRvGL/JDJA/vT3+eX8yDjvCk3CGXylH/ePPzV106W8fP7SyQSjmUDrwhOu0v488bEV5SW8yCLBICvKKmAhqhIQhNtUmVP/ObAo9nBB0TBM65ATUy0rLy4nAdRjTFMGE1669EKDwCawHGraGOUtplKYMJgirQXWjgAaACAYKm1BOoGJFIasOy5Bmyv08kiAAAgTbEGc3EZA7KDnlsghCI6UjUm2GikzRXVAtCRAAj1ldUtdLxv+jURBpOB3VFeK4AEAgChrrzCrx9lJEGD2LM3d56oWJN25iOHV88Ofkzgjj+z5OnK238+/F2OluJ+f/zSa1pon8d6PQihRqPpyy1XWKsxQBBEu4PkogMm24okSYVCgRAIDI3u4NFCxeFwsH/eJi9E07RarfZ4PKdOnSoqq/QjQkEIOf2z0tLSEEJut7uDiwhACBUKRVZWVnx8fML332/6afuJ0rpNmzbNmTOnqzMBmUyWmJhotVpb37UziLnObrf7xImTpZXVDCIiKJTTv39qaqogCE0mvg4fMpksPj6ezK3QaDRtmv8MAqhrbRxHD70LsCxLAMHucrfwdWMihTwGSJLU6XRtiu1QwUXrkBmY0sriXl9tO1TZxicEvvDL59ewE+dOGpxiJMz73lm5DQ16Nvu8L1k5YdlDWZc+uoYHVwIAuONr1pwe/OT2zXemBIqgzIEnx92yelPNDYuaHprx+XyB5ECCkkiov35e8rEzLi9ds9fyYEYU5I6uenKL5pq/XZ6TpOOKt7760W8Rl9yRQgI6e3C/gu07q5amxvPFa9//sQEtOfdu7viql76a9+71ceavn19ZOPGpyVqouOK2ecsWLXtjwvt3j44mXVUnfz1Jjp2Sc156FJNunuO/+ZFXZnx6/9gorvLocVfGiMxZ16W9uvyFbROenxRx9uPnPqyb/tokJQDnjzk/h8y6Znb8lR98WTrt1iTC/O17G8r4yQAAqBowKP6pbTsbFlwT6T3x/oe7/GAyAIAefuONhhlPPT07++npCUT9qd9rokfmKCN1dGVRCQMuEfNYqJjwtyn/fOTFdTe+Pz/O8uNLKw+wqiub3/v8rwmaZswb8/iKZzdd9ebsaPOmZ948Mfrej6Jhh4vWZL95tw68/LlHNgz497X9IriGwoN7a5OvHO5yehTx/ZK1FBDqtn2wPp8fDwAAAFl//d9/j+bc/3/jDRDV73prxfFBF/zdwQRhF6VQKIYOHRqq9Tkb1ynF+qxWYwAh5HK52jcMT63RdLxBSS6Xx8TEEMeLWbaVCZnaBELYbW22Dodj7fpNnmYGJiGEIOudfsX48oqKersHEFS0Wjagf3ZMTExgTaNOTElERMS8efM0Gs3/Pt+47tufEhMTR4wY0aWZAEVRarXabu+OWT1avc6A9U6fNK68oqLe4SUIKlojzxnQPzo6GkLYude5czWOhtDpdOKDNjY2liBPy2Sylqu3euhdwO/3A4RUahVusu64kMdAZGTk/PnzQ5gA8fC4jl6FUEcpiz9bOnWAKUJhGnLbBvXdX3xwW3yTOagTb37yVoPdgQAAzMHPv6yYcOftI2Ojz0m86h+3puxZvb7s/EI9qv/oGo38HGXOo78F1wDCyKuXXFXwxYYKAQBCF0Ude3vJuH56eUT82Pv29Xvq69dmaSEgUha//ACxYlzOsNFTHszPmfBXoqRTFlz28/WZhqghj1bP++TtG2MgAKor/vvdf7O23jHEpNKYMq9Y+tkpF2haalNN+e/m//T7ZdFAvSoyZcK9G4oYQOU8uObdy3+/KydSFT/5HWrpV2/O1jdb2KMGPfjp8/r3ZlwycvykRT8nTx4YuBVRQ+9dsaB82WUDh4+5+j/ciLGKwAEkwx7f8P6EUw8NN6m1cZct/vCECwHZpLvvM66eHKXVZz+wu9UqUai/7rVPF1ieHGYyZFzzeep10yJarHwL/poAIBIXrVo9v/apkSZV1Mgna+Z+tmpJUmf8dsm0O9d8eZN9+fRUnToyYej8l3ZUs4gesfTFGSfuHnLp2ElT//6dcVTWuboZZDvyxRtrDtkEAIBgO7Tm9Yv8jXUFpVJ52WWXBVbv7H7tW7IY601ajYHAak9tDZKMjAyKpgSe75To0ul0JAS1dRa3293xowUolcrMzMyON6qLwbKshwOarNHRl8248B+dOLjEXL/vyPFah4+iiJz0xEkTJ8TFxXXR6FwI4bhx40xKqsGHdu/e7fP5ujQTYBimrq6uE+tEWtDydSbjB5WY6w/8fqLO6ZdQ5KDMlEkTJzSu3gQhlMvl4TkiOrD6miAIPp9P5JVsXKnY5XL6fL4W9uzRdwGaovrsoIZO1KNjoJvB4J9TcJVzc3+L3K3lvw8cOAAhnDZtWgdT31kKS8o2nrKTkvNGv46L5ltuVW63kvLKdcfqJyVJRLRaNx3zTLtr/m/W5V2Rqg4RCt+cfmPlc7teGiFrfee/IPPKqVlb/169bl74rrnRJYTyt64YvPOOkvXXh2ZIYvfatm1bSkpKywur8Dzv8XiKi4tHjBjR5KWtW7fOmjUreNKdruBwOGJjY6uqqkI1TDRs8TxfUFCQkZHRIzpiYV2h1Rjgef7VV1/98bfTQ1OjHn/sUZE/or179774zicmJfXic093fErk77777t3V6ymIHr7nrpEjR3bwaI3Kysq6Z11ri8Xy6dffq/uNUGguMh6rodZcuPfbIVmpapUqOSlh0KBBwa8yDOP1ets6BrhlLMu+9dZbP+w/kWqQP/PkEzabresyAZfLtXv3brlc3g21GC1fZ0tNddHeb4dkp6nVqtTkpJycnCY7JCQkJCYmdnUi28Hr9b788su7TpZOGJi8bNmy4LXTmoMQ+uyzz1b/sDsnTvvkE4+30LTbQ+8Cn3zyydofdqWaNC89/wy+s3dQD42BbiaVSjdt2oQ7hDcrv6zax4qtQ7U1NGh0OpGDECxWO4CqDiQt/BBp/9zyW6gTEe74szu3+AZPydGyxRufffvU2H+N67SHIAwLVxzHBapFaJqWSCSN/w0IDO8MXeqwzgQhjI2NUanE3t3Ky8t5ju+sgWmDBw/Wb9xc6eR/+nlLdna2RtOGyTib43a78/LyKIpqrsjX8qjdNvF6vQTnqzuxu5nXUWK0/oqJ41Uq1YXVlCRJxsXFSaWiZ9oUgabpjIwMyW/H6m2u4AmxQ6I7r3O8qdnr3PtYLJZe3w7p9fm8Xm/vLlojhLxeL0JIJpORJOn3+4OXDAzcfHme53m+e/rg9HG4aN0sTgA+VmyO4/b5pAwPxXUTYgXAuh0Q6FvfFetFkO3gihvnL6hngTRm1G0fvntTbDj2KsOwTpWXl1dWVgYAMBqNJpPJ5XKVlJQ0vkrTdGZmJkLI4/EkJibiYnZPByEUP89N4Fmws04dExMzYezo9T/v+iOv+NixY+PGjev4MRFCLMs2N994y6N224FDJBKaPRxBERqN5qK/kcAEP53e61Wn0xEA8Qh4PJ7OLbe3STdfZ0pCKpXKi7b/Mwxz6tSpyMjITuwdgHUdg8FAkoTA9/K6g8BU9mVlZYIgpKeny2SyysrK4EkBDQZDdHS0zWZzOBxjxowJYVL7CFy0blb/5FamGTvfoNZ3+RPH8Q6XKwI/RAIYfccvtjtCnYruQg1b9kv+slCnAsPaBkKo1+vbPS9ooJlao9G43e7Avb9JT8WysjKO46xWa3V1dXZ2tslk6nHz0PZ6YmIgUPSqr7e43e6QzAtAUdTMmTNzz+QdLalb+clav98/cODAjoS9otl2AAAgAElEQVSTIAhutzu48aeJxlG7clUntJC3zGNvcOT/5vV6L1q05nnearXGxMR0bumaJEkIgEwmCww2DtUPszuvs9dpt5/Z10ILeSdWBvUgHbwLhEpiYiJNkh6ft6sHlIWQ3W4/ffp0bW2tXC6XyWRms5kgCI7jgu+zHo+nrKzM4XB0pMk65DHgdDqPHDkydOhQ8R2jQgUXrUOAoshIbZffITAM6x3q6+s3b9587bXXGgyG7j87QRBGo7GDR2i8owf/3YiiKL1eb7fbjxw5kpKSkpGRgWedCSutxgBJkvHx8QRx3O/3d89kVBdlMplu//uS1Wu/OHSm/I2Pvkg2fn/Z0CGDBw/W6XRqtVqr1Qamy2rcv3HO25qaGo7jgvuQUxRVUlJy5MgRn8/X8hhguUqjvNio3U7naP4lnucrKyvT0tI694djNBppmiYIQqFQdGk7LUVRGo2mhVoM0I3XuTumKe8CFEXpdDoIyymKamv5RyaXtRw5Hb8LhERkZCQFeD8Pi4qKwnOEfMeZzeba2lqtVhvcueaiHW0IguhIwTjkMeDz+c6cOTNgwABctMYwDMM6JNArNVStJYIgVFZWtnsu4szMTIejhULBOQRB6HQ6r9dbUFCgVqvj4+PbcS6si3QwBlqm1+s7sc4oPT39kWUPrf3ii00/7zhd5Thr3rHhx20UBDKpRCaX8Tzvcrkad4YQBkZgOh1Ok1E39JJLGh9JKYr6Zecep9M5atglfbOhEgCg0WhIimIZpq6uzmq1dt2E5DKZLCEhoXumi+utAstxkyQRHx8vvve+SqUiCEKni2y5p0mX5gBdhyAItYwsZ1BhYeGECRNCnZzO5PF4zGaz3+9vaGjonsbkHhoDIYGL1hiG9U4rV658/fXXa2pqBg4c+MorrwwbNizUKeqREEJut7vdpQuKooLvxD6f76Ilba1WK5FIAs2DFRUVJEkGmrLxXTwctBoDPM8XFRW1tb06OjqaJElIEJ37LUul0nlz50abTHv27M0rKvUJkEUExxEuJ8OwDEB/TW9L03S9k+E4zudnig4dLa62kuS5lEBIMIiUQEoqlfbZGXGdTifP8zzH2e12mUzWdVUMgiAEag97XJfj8MFxnNvtbtNXBCGMjo4mCILneUEQWvgZdvAuECoGg2HYpYOrdh3a/dvvV155Ze8YbYQQqqmpyc3NtdvtkZGRgcXMA0NCLpyRTi6Xd1Ybbw+NgZDARWsMw3qh9evXP/LII6+//vqwYcPefvvtWbNm/f777x1f4Adrq9LSUqfT2Xh337lr9+lS84W7KWgiMcaYEBcbFxen0+ny8vIcDkdcXFx2djae26xHaMcjl1qthgR02O02m62FhX/aQaFQTJs2bcyYMXV1deXl5V6vNy4uDgBQUFAQnM6kpCSpVFpTU1NZWfnNT1s5CBtfJQhoUCsVFFCpVH22aF1TU8MyDNH1hRGPx5OXl9c9i291BEVR6enp4TlzOMuy9fX1PM9VVFT4/X4xDdeN/aFqa2ocDkfn/gbDAU3TU6dOPfT70QoH89wLLy257ZbBgwf36OrawIxlRUVFCCG9Xu/xeCorKysqK4srzE4fd2EWHCmnbrnphkCFgkKhyMjI6P4090G4aI1hWC/01ltv3XrrrQsXLgQAvPHGGz///PNnn332wAMPhDpdfY7L5WIYpvG/DMshXUJ8v8HB+yAkuKz1Z+qqTpSdkME/DGpFSkJsVFSUz+ez2+3Z2dnR0dG9oLWh14MQarUaMQvqBtjtdkEQGIYJjpBOTIxKpVKpVKmpqY0bBw4ceOGegY3Tp08P7itOEIRcLkcIFRcXd9ayT10HQqhUKvtsFUC3IQgisLhRqBPSEo7jxNdzlZSUhHByhG6Qmpo677rZq1Z/kV+DXn3z7euuvXrMmDGRkZE9tIBdWlqam5vr8XgsFktJeWW1xe7hIakyqmJyTHoTSZ43YN5qLvNUHG/8L0EQIZznv0/BRWsMw3obhmH++OOPxoI0QRATJ048ePBgaFPVQxEEEeg02FkHpGjphTP9KtS6qKQMQeBdDXXW2qrKgkp44mwEDY2aiD/++GPkyJEej4fjuOjo6Nra2sZ3yeXyhIQEhFBBQUHw0VJSUmiarq6uDl6P12AwREZGut3uysrKxo1SqTQpKQkAUFhYGPyImZiYKJPJamtrbTZb40adTmc0Gr1eb/CgUJIk09LSAAClpaWN89BKpVKNRmMymfR6fS94mhEfA0qlUnzDY01NTfisi2MymUwmU5ONLpfLZrO13ErpdbZt3iueY5EgQIIgqTZMOdbyWWiaTkhI6LrW1Maew110fDE89oY27S/wnMDznXudOY6rrKyMiooSX3/UO3T6XaDbEAQxadIko9H4vw8+Lrf5V63dsH3nruzMfpdffrlcLi8pKQmuOAv8iCwWi8ViadwYyMn9fn9paWnjRghhoAW4rKzM5/M1bo+JiVGpVDabLfg+FRERERsby/N8YWFhcNrS09OJ/2fvvOOkqq4Hfu97b96bslN3Zme2V1h6ERFBQBABCwgI2EsSuxg1lsREEzUxaozRaDSW/BIT9ac/DURRQcAChI6IAgtsYQvby8xOb6/d3x8PxmEbb3ZndmZ37/fD58PM21fO3Hfefffcc+45BNHU1OT3+yMbMzIyDAaD1+ttaWmRttjtdpVKZTQaeZ7ft3//iZNN3rDAkSqlKUs/dlKOOZPsJQWdz6WOToweCoVqa2uNRmNMDRhh6OrA4INNawwGM9xwOByCIESHf2dkZFRWVnbZLRwOR2whyQYTBEGyryCEBEGIohiZ/u++Rcq32X1LtIUmvYeiV0B130KSJEIoskW6UPQW6fwIociZB1k8vV7fh3h9CyPtJq2iRAghhACQ/kle6GjvCiQIUme26sxWAKaGAz53R8vJ9qbj3xzfdajC5/OFPJ1Ga3a0/BBCmqYRQl3cnrSCBhDwHC+iM34ISZKiKJ6ZiBjStAIA0OUMUmJkjuO6NIV0s7q4MSVjkmU56ecgBCBAJAQ6lWJ86aj58+dNnjxZSsAba+v1tmXQdC+yRavVSht7FA8AIN1av98fDAaVSqVM8RBAACDpueu3eIlrPUEQ0GkiPzOyj0KhUFPAXb4ryhrrQaujv4qC4Pb6RUgSiNdr04hT/k/Y7ZDuJwEqClAUJQnQRRipTaJ/UVxaT9qToijJ/y99TYTuRR8b/ROkTkOpVKoo4Knc6+mhrXpuPVEU3B6vCCkC8XqtliBJOS0sbVFRMJLNroswoih2dnaGw2GFQpFqT260tJG1030LcwoEEAKRZ7BH8URRjFaAfoiXxH6Ppulp06Y9lZX10l9eOVbbVNboKm/+ZvPu7yDo+uKgKAVBwMgwIHJC6bnr3u1DCFmWjW5PKbdI1zNAglJQAAGWk/OeoiiKFAQh8p7yd7YDRpOWlgZIBaE16zInZGVkqbTGqEAuSYDujwYAUVrB87zL5RqI7kk6kKybGxlL9C1wUsTrEsmCTWsMBjNCef7555955pnoLdXV1VKNGb1en5mZ2dbW5nafGjanp6dbLJboCWabzWYwGOrq6iKv55ycnLS0tOrq6kgnXlhYSFFUtE911KhRPM/X1tZKXwmCGD16tN/vb2xslLbQNF1UVOR2u1tbT61JlqwyKXu2tGUwxSspKampqYm85ruLp9FocnNzo2f6o8Vrb2/3er0qlUqtVnu9Xr/fH4bqQCBI0zRFUaFQOCKMlC8qGAwhhHgu7LW3hTwO3uugECeGBUJgIRJIkSMh4DiOoiiFQiGKIuJCSBQhzwMAIIRSbU+v1ysNgyAABEFIA4JgKMiFeQAASRDS0FmytMMBL0mSDEnyPC+V3YYQCgILBAA4DiIk3QK1Wi2Kos/nQwhJQxqlUqlWqwOBQCjgla4l2fkESQBIBMJck1tsOVhx4PCxSy6ac8EFF0gTATG1XtJ1TxLPbrd3dnb2IZ7ZbNbpdILAt7S0lpeXFxYWyhEPAMBzfDjM1tfXu93ufouXuNarra31eDyhUEhKqieKotPplHaQqrwumj8ncmmSJLVaLcuygUBA2qJQKDQaTSgUivi1IITNnV4W0py3M89qomlaqVQqlUq/3x8ZuKvVapqmpRRi0pa0tDSKonie5zhOupzRaCQIInJpnuc9Ho9Op4v4yuLSek6nEyFE03Q4HI4oUiJ0LxQK+Xw+lUolfYgIrNPpgsFgMBi8cOZ0v9/PMIxKpQoGfyhT3FvrORyO2uYOoNSBkGdccZ5arXa73ZExsU6ngxBGNET64QghKcMiwzBSd8eybCTnIkmSRqMxHA57PJ7a2lqlUplqT25WVhZBEDzPBwKBmpoag8FwVvFMJhMAQBCFUDhUV1en0WgGuWMZzNZzu91XLl829vjxiooKu9PtcHs5jpd6cgihwWCQundOspQIgqIoyV4SBIENBAmCYChKUgwpEIkNB0iSJAReujTDMEqlkuO4YPCHd4RarVIqVX6/P3z6HSFN2koGYSjkk54vBBDHcQzDEATB8WHAEfD0S42iKLWSBhSRpqQ8vgDLtwuIEBG0UDShYCJqT1EUTdMsy0Xe1NKwgWNZh8MhzbxACBUKxZC+uUVFRakp3pgxY0AUMDqSIXrqpbfPMnfr+/PevXshhIsXLwapQXVd/UdH3SR9RtjeXJswbeLYZIkEAPjwq2+axK5hkwp/2z3L5yRFHgymf3z11VeFhYV9BysKghAIBGpra2fMmNHlT19++eXy5csjYyk5sCxrsVjefffdpUuXSltuv/12t9v9wQcfRO/WxWtdWlra0NAgFeNJKc9hKBSqr6+XUi71JkzixAMAVFVVFRUVSeOMWOeA29vbjx49KtmrCKFPPttYJxgKJ83o7jhCCATcne6OZm9Hs+izayhgS9cX5GbPmTPHZrP5fD6PxyONBRFCaWlpJpMpHA53dHScdoYDCGFeXp4UYhd5/0EIpQBOl8slvTghhBBCpVJpNpt5nm9ubpa2SOfJzc2FEHZ0dITD4ciZTSaTTqfz+Xx2uz0isEqlslqtPM9H3uLSCEYqlXzo0KEDBw58c/BQq19QKqgFMyauWL5cqlwy5LzWHMdVV1dLOtCbeP/5z39e+/DzqXmmX/7yEYPBIEe8f/7zn/+36b+l2eanf/u4SqVKEf9DdFt5PJ6dO3dK6bW6e629Xu/7az8OnJE8KBavtU5LnIqulOu1vvHqlVJWvy7CsCwbCoXmzJkTnfNv4K3ncDju+fmvCQheeva3TqdTUoBE6B5CqKGhoampCUZlkgOnvdYej+f9deuDfG+uuZ681gLv9vr67bW+duUyqch5F2GkeZPZs2enpaWl4JP71ltvvbd5z8p559xyyy3SnTqr1/rll1/euLfs4mljHnzgZ9IOPYrHsmxNTU1EAfonXor0e6FQyO/3h0KhiF0KIczNzSUIorW1VbKJpDeCXq/XarV+v1+yTiVtVCgUNpsNANDQ0AB/CMUCmZmZDMM4HA5p+lU6s8FgMBqNfr8/MucFISRJMisrCwDQ2NgoimLkWY68p6QzSCfRaDQQwkAgYDAYtm3bVlFR0el0t9idXg6JjE5jztJbMrXpGQRBdn80Ohpqwif23vnjG6RL+P1+hUIxb968/rWeVAaiqKhImkFOzZsLku21Zhjm448/xl7r1GVGabbT1bVKjZLJToowGMwQgqbpqVOnbtu2TTKtRVHctm3bHXfc0WU3hmG6rIOVIn4jX08PfEFMW7onuTnrFul129sWjUYzdmwP03yDI17kzRG9sbvAvV3aYrFEZlWk0UnI5+loqIneE4mCz2kPO1sUQsigVpybk5mddb7RaIQQFhYWTpo0CfSCQqGQogy6kJub232j2WzuXj9ZoVBIK6W7IA19uqDX66UBdzSRtdZdOP/8888999xLT57897/X7vz+2MadB31e35o1d+v1evmtF+uWuOte9IWidaDLpUVR7OjoQKIIIJTMbzniqdVqAhIqtTr6JvZbvIFv6d5WDMOYTCaWZeHp4EsYFYXJcVyAB/oxF3TPHdAHVp5DokiQJEHGMAALuDs9lftCoVB0/eEuUslpq5hajyAIAGEoFGxpaZHSd/WmAGDAuhdRG3hmwkIIIc/zwf62cz/WWrvLd0dcf7Bb9kRJ5i7tIA3cpTE3QRARY0AKXpW8lJHpP5DIJxdCoFarFacX3/at9gihkpIS8pvjBElGYuD7EC/6h8exYxn4lphaT6PR9FbEW6oj0AWDwdBj7vTo/IgRMjIyuhcikdZTdN9ZitzpgsVisVgsPYp32WWXFRcX19fXcxwXCASam5trG+rtLeXNiFIYbGnp1i6q7nW00adfu5GNA2y9yAx7at7c3oQZTPEADghPZfJzsvJzehjeYTCYs3LPPffccccd55xzzrRp01599dVAIHDDDTckW6iRiNvtjq4Eo9dptW214RMd0ftACPLTjflTS7KysqRRhdvtlurc9Dj4GCpQFFVcXHznnXdk/Oc/n2zdu/dw+bitW5ctW9Z9vD7UQQh5vd4uAW5nJScnh6TIcCgUDAZTs6CRUqnMzs6OzlrXHZVWr9GbBkGYHsrBJ5gTJ06EgsFBKL4VDAZra2sll2Bv+wxaO/eRx4wgCIPB0NbW1tbWJplnwWBQFMWtW7c2NzdLMdgAAKk2UuSonJwchmHsdjvP87m5uePGjcvKyop7IrRgMFhfXy/VN+Y4ro+WjHDK6EJA8rX2OE2JSR0Yhpk4cWJ6enp5ebnBYCgoKJiJkNPpbGpqamhqbqj/LnxmuBkNgMXwg0lP07Tkb8ckGmxaYzCYYciqVavsdvtTTz3V1tY2adKkjz76qHv6X4wcCILIycnpPlkrk8bGRp/PFzGt586ZPXfO7O67RaxNhJDT6TQYDGPHju3uZB6KGAyGH//4x45O15YDx7/etv2CCy7ozSmRssjVAQgYmpZfmkgURYCA1+uVVuMPVMoEIAhCKBQSRbHf+j88gBAOpBM4K4Ig+P3+1NSBaBBCNTU1L732t5AADAYjAMDr9QoC39Fh57mw3pxJ0GUIIYGPKmcFIfltJQRAEAUCIAXYp6HEcydPXL16VdznDaUoYrfbLQiCHNMaIdTY2CiIAhsOn5ncsSsDfAtg4gWEMDs7WyoeduLEiba2NqPRaDKZJk6c2NvMZuTdOkDTGuuAfLBpjcFghid33nnnnXfemWwphjwQwjh6M/pw2ErjwszMzPHjx6vV6mFQsyoChHDO7Fk7vj18sq2zqqpqyJnWMnUAAmi2mHsLtuzOiRMneJ5P5XFIMBg8ceKEtNY62bL0hUKhyMvLi15oHV/i2wkMXQRB8Hq9IQGyiPT5vAgBaa0sw9AMIV507ji1WtXpcARDP2QGMeh1mrS0YDBodzi8vkCHy+vmyS8OHD9R98ID964pLS1NbgxLMBiUE2yCFSClkCahCgoKGhoafD6fwWCIrKfoAymXW7+Lb2EdkE/qvtIwGAwGAwAQRZHneamqx+BfXarGWVxcLN8b2Q84jnO5XNnZ2SUlJYm7ShLJzc3VMZSThV3KvQwJYtABdKrQ2qDIhTkFhFDKPJyQkwMAAKisrEx0J5D6iKLIcZxZp1YqlXNnTo94ekVRNBqNCxcuZBgmEAhEe4CVSiVN0yzLBoNBlmVbWlp27Nixc9+Bk67w757788oli5YsWZLiEzdgsN4CmJjQaDTTp08/duyYw+HQarVnXWIQCoVqamoKCgr6d7mk64DL5dq5c+fs2bN7XPqeUmDTGgAAFBRF+DtA6IzWYHIzkyUPBoPBRHA4HOvWrVu5cmWyvJ1dEobHipTUh+O46IIUBEFI7k2fzycIAsdxWVlZPWZrGx5otdpsm7mtzhGdY3wI0bcOQAiNRiOAsKm52ev1Smn2MYMGz/MNDQ1ShuG4n1ylVufn57e0tMT9zEMOiqIKCgquuOIKrVar0WiiTWiapiULucfYgchfLRbLmDFjVq1a9dobb+45Xv/e2o8tFsvs2bNTfzZqgG8BTCJIT0+fMWNGRUXFyZMnRVFUq9XBYLCLWkrF5KSZHTnLBPoguTrAcVxjY2OX6uKpCTatAQAgLyfrgatwwjAMBoOJMxqNZsyYMZmZmS6XK1J8EgCgUqmkUpCHDh1CCOXk5BQWFg5jlwhFUQxNi6JYX1/Psmzq+6ligiAIk8lE9Ms8UCgU0dmJhxxBbx95rwbpKqIout3uvpfL9oOCggKlUokQiK5/kwgYhsnOzo4uNN2dgLszoTJInPVuUhRlNpsHEhlL07TVav3pmrvrH3n0pFux/pNPx40bl56e3u8TSjAMk5WVRR5v1Gq1MXWkEEC9Xp/6C90xPULT9IQJE6RKHAihuro6p9MZ+WtmZqbNZnO73TU1NQzDdK9wgUkEQ/h9hsFgMJgUJxLgLWU07b7DhRdeOKgCJQ+pCGogEIg1k3bqgxCSuWizO3qDQSpXnpp0dyfyPO90Onme9/l8gA20ffdVH4dTFEUNzFMUQU2BATqdYsVkMilpyukNHDhwIC8vL3EXUigURqPR5/NFb5QWiUgON8gG2g9t6+MMQ66djUbj0ssW/+ODjysb2r777ruLL754gCckCEKhUEAI09PT5ctvtVoJ8phaoxlOuS1GGhDCSH6yKVOmdN/BYDDk5+cPrlAjGmxaYzAYDKZXCIIoLCzEeUEHCE3T6enpEDYmW5D+cFYdEEWxubk51nDBoqIiitozYOkSSFpa2vjx47sU36qsrDxaXikgEAqFXG63StfXvAANhZWXLY6LS1ChUAxypD3P81qlosUFy8rKZs2albhOgOM4p9MpCEK0u7W8vPx4VbWIQDgU8vi8tKavBZaD1s6S+TrwqxAEMWvWrC1ffl3V4T98+PD8+fMHGLPDsqzD4UAIcRwnM6c9hFCn00EIA35/36E0+C2AwTognxFtWgeDwd2Hqxtc0UllIACn5t3PKzQU5NikPjQFnQwIIfmJWDEYDKbfDOl43RSBoiiNRpP6Kyp7IxE6QFEUgMDe0dHe3p6RkRH388edYDBYUVlZWdvAioRSQUIUBApVxuT5Km3PYZYBd6encp9KpRp4uG9SoCiqKD+3svXot4fKGhsbS0tLE3ShcDjc1NSkUqkk8zIYDB4/fryqvplDhJIiwygokspUaGeapouLi+M19EpPTy8pKjhhPxZm+YGvhREEQcpb0dTUxLKsnFX3UqUuaTVBIBDoe5UKfgtgsA7IZERPPyCEWERwCm3Uv7RTHygtJ4jgdD2YZEvaA0N3iIbBYIYQoihWVVXhHDYDhOM4r9crvU1S853SBzJ1AEJotVrlL0NtaWk5owJw6hEIBKqrqyU3YGNj49bt249V1/O8kGPWzZ99/sKLLjQY+lq7CIlByh1AkqTVao37An6apufNm6en+I4geuedd6PXcCYIKRnB11u3Ha9tEgUh12xYMHfWwosu7HuN6KC1c3yhKMpisZAAtXcmvGF7o729XU7fjt8CGKwD8sEzEBgMBoPBJBbJpwQAUKvVw3jun6JI+RGDkbmGlEUUxWAwSFFUQ0ND2bHygEgwUCwuyBk3bpxarfZ4PGoKust395H5anAW7pIkabFYEpEbb/z48fNmnbdhx4Fvy2s3btx4/fXXJ25an+O4qqqqYxVVQZFQQrGkMG/s2LGSK1tNgVRoZ5ZlKyoq0tPT4+W4pigKQtDe3tHW1ma1WuNyTgwGk1yG7Qseg8FgMJgUgaZpnU5HQuD2+YelaS39KKfTGQgEeqw/lGhEUZQSWVMUBSHkeT7abidJkiCIyD4SEEKKohBCgiBQFNX9rwAAlmXLy8vrGltYRGpIceL4cYWFhZIVp9Pprl21vO9iMIOzQFoURY/Hw/N83FVLqVSuXr26rb1j7/G6dVt2dDpds2aen5eXp9VqpWtJrS0IQrQ7iyAIkiQRQtFJy6XWBlF3KrIzACAcDh87duxkcxuHSK0CTRw/oaCgQNo/ddoZAMBxXBzngywWC0kQoihiZyAG0zdKpXLMmDGJqC8Yd4bhCx6DwWCGE3q9/vLLL8dlM4Y0BEGUlJTQuw9W1DYePXp0/PjxyZYonpAkmZ+fT+w9GggE414Cqm9Ylj18+PDx48c7Ojok60vKtdPc3BwMBiO7WSwWnU7n8Xg6OjoiG1UqVVZWliiKu3btGjNmDMMwXf6akZHR2NhY32oXIVVoM44dM8ZoNEa75VOkgjfP8/X19QUFBQMpCtUbVqv1l4/84rXXX9/zffmnuw59tesbmykty5rBMAyEMCcnh2EYu90eXTpLr9ebzeZQKNTU1BTZSJIkz/PZ2dksy0ZXd1er1RkZGSfrGxpaOxBBFdvSx5SWGo3GaPd4irRz3DGZTARJAhAHu5qiKCknWT8STSloZljO92GGE1qtdt68ecmWQhb4WcJgMJiUhqbp3NzcZF2dIIhRo0bhvKADZ+LEiemaj5q83Np163Jzc4eQtZBQHTAajf0rvsWybGNj438+Wr/rwPchkeAQRAACAInvqgFAoihK3kUIIQCQIE4AABBCEfcghASEAMIjAICT1VW7y2r15gxRFKX9pYSmSkYxKtsCASgtyJoyZcqIfQpUKtX99903bsuW9Z9uaOpka+zBqo6T0p+Ig1UQEtENDgA4beBBURQROm03ItDcUGswGDV6E0JntDNBEIY0lU3HjCvOnzRp0shJJePz+ZAogngsFZcKmJEkmZeXJ9+zp1KpCIIwm9P7npTBbwEM1gH5YNO6/0hFSpMtBQaDwSQWnucTsYxzpJGZmXn54oXvffrlwWPVGzZsWLFiRW8jYJZlWfaH0hVSavFgMBgJoyVJMi51huTTtw4IgnDy5ElBjC0nmcViIUiCpKh+eMxaWlreffd/DxytcgQFJYRaSsi2ZmTaMrRardvt9ng8kT01Go3JZOI4rrW1NbJRcrcCANra2liW1XGdmZmZ0mSH2WxWqVQej0dyw9I0abHYxmUmWWUAACAASURBVIwZM8LHlDzPL1q0aOrUqUeOHDl+/LjT6ZSyuwEATCaTRqPx+XzRec6USqXFYhFFMdpxXS66cnJyJCvOYrEolcpIOysUCpvNVlpaOnLsagBAc3Mzz/OAjMNPFgQhOkxDDtJTQBAHpYS9fbc8fgtgsA7IBJvW/Qfb1RgMZtgjimJtbe2oUaMGXh5mhENR1OrVqxuamrfsP/rvTz4Ph8PLli0zGo1ddmNZdufOnaFQCAAQDAa9Xu+4ceNIkmxpaQmHwzzPa7VapVI5c+bMQbOu5egAz/MgxleiFFzt9XrcbndM6x3a29tfe/2Nb8pPQgjH2nQL5s8755xzdDqdWq2maTocDkevy6UoSqlUiqIYCASiTyIZeIFAQBRFlmUj9YoZhlEoFJHZjaampvb29hFuV0cUwGaz2Wy2BQsWhMPhyERPlxaTkGZ/EEJ+vz+yMbqdlUolRVHSzfL7/fv27dNoNAzDDPJPiwmKogoLC+O+2lOblta/wI1oWJZtb2/neb65ubnvItUR0GlaWlrcbrfB0GvlcPwWwGAdkA82rVMRCOG/t37vExU9TiEqxPB1F08dbJkwGAwGMzAghD+66cbq6ier2oW1m7a1tXcsXXK51WrlOC7aeAuFQtLC1A8++rTiRHVBUYlCQZMiKwoCzTArl16q0Wg6OzsZhjGbzQAAh8MRnRdKr9czDOP3+6OtGqVSqdPpeJ7v7OyMFslisUgRWHHwFkKo02nlW0c+nw+JKBwKh8Nh+RcJBoMffvjhgfI6tZJZfen8uXPnZmZmRgvPMEx3GQiC6DHktbeMazRN0zTNsqzf70/9aXQIIcMwg2b/EwTRfVpHarHugvUdaRy5WRRFpf78BUEQGo0m7suSKQUVxzkFlmXlJ0U7ceJEdNeBwWAGDjatUxVKqaB6eSGFPT1vx2AwwxGe5z0ej06nw5lmhgEmk+mna+5693/fO3yi/otvK/aVVSkQr6QVCsWpm5umVmfaLCXFxT6fzxsICwhyHCeKCPJBTiQYQLW3t2/fvsMXCpEkaTKlAwCcTueZprVOoaADAX8g8EOAKMMwWq1WEIQu1YltNqvFZGBoety4cRMnTjQYDP22sSEAWm0MpnVzc7MgCADEVjOprKxs6+79CCpWLr7w6quvTmj8MMuyra2tUv2nxF1l4CgUiqKioqQkZh9R8DwvVcmKr3c9GAxKPXwcz4nBDDM6Ozu/+OKLhQsXDjzEI9HggRoGg8GkNE6nc926dStXrrRYLEkRIPW9SUMICOHYsWMf+Nn927dv37V7b0O7g4UKf0jkfafMYNjp/+5ImelwJc/zDU1NmVYrJbIiJDiRqK+vBwAEgkGnvc1ozaaVqkZXkyj84KGCBCRJssXrEM5M000pKODjhDZ3tAOWIEmCgM2VDaxQD5H4+bY9Ywqyrli6ZPr06d3djzJ1IBwKcRyX0ArDlZWVHoHO1MCZM2eOqHW5yQV3AgAAURSlRPTxMq2lLOuhUNjv96e4aY0VAJNcHZDmhYdEkAU2rTEYDAbTKyRJjh49OtlSDDfS09NXrFgxf/58lmVdLtfJkycbGxulP3k8ni+27YwYjQLPhwVBJE+tioQQUhRpNhpuvPLSvLw8h8PhcDgip9XpdDabjeO42tra6MtJd7ChoSE60VFmZqZWq21vb6+srDxRe7LVHTxQ017z6htXX9mycuXKaKv1rDpAEERWVhYkyu2OzmAwmDjTGiG099vvCSDOmDYtiWnzUw2WZY8fP56enq7RaBJxftwJJIj8/HxKoQCgr5LdMoEQJi62AisABuuAfLBpjcFgMJhekRIRSUmqky3LsAJCKOUws1qtpaWlke2hUGjMmDGBQMDj8WzYItacqCwpzFcoFDzPa4vyFQrF4gUXpqWlXX755X2sYp0/f758SViWdTgcO3fu/HTj5ma/4j8btkyYMGHMmDGRHc6qAxBClUoFQX80RK1WyzcIjx071tzeyUBh8uTJeH1ENIIgJG5NeKI7AbVaXVpa2tLSkoiTjxCUSmVOTg5ZVm+xWGKa24IQZlitffvM8VsAg3VAPvjNhMFgMJheEUWxsbER5wUdNKTs3+3t7VVVVauWXe5yXSDlu546dapWq62rq5NSXsfxdtA0nZmZeeWVV44bN+7/Plz7TVXTn15+9Te//HnELXxWHRBF0eFwiDGadlI0rFqjkW9aNzU1hXmBJoDNZovpWv2DJEkp99sgXCuVSXQnQBBEJHP4iOLEiRMcx4H41TOCEGg0Gvm3qbCwkPzmOEmSfcf64rcABuuAfLBpnbrEJ2UrBoPBYIYUer1eq9VmZmZGb5R81CUlJaIoJqKuNUmS48ePf/Bn2Q898lidI/DOO+/ef/99MjNjIYRcLheSnZdYIj8/n6RINhyWWSsIAMBxHAKApqiYinX1G5VKlZeX19DQMAjXGsmEQqGGhgae51M8EoEgCKPRGEchRVEECDEMM/DHORgMNjY2CoLo8XhktiSEkKIogIDT2Sk5JAcoAwaDAQDgtARdQQgghBBIYbM21euAYDAYDGZASMWiopG2q9XqtLS0xFW01uv1M6dPJYF4sOx4zCYlBIrYSyh5PB6XyxXTISqVanBS+omiyPN86hffGurwPO92u+WXjEoWFEVlZ2fHva61Wq3uo6a0fARBQEjs7OyUGWchVbQWRCEUDEWXgsdgMAMhpScIB4GI9YwQAgACgCCEqfAajTbspfd66pr6GIwMovMndWdIZH0cmUAIaZrG/c8IAUI4c+bMjV9tDwhEWVmZtAhcpg5AADOsGX3XMY6mqqqK5/h+jENEhAbHwxkIBMrLyymKkhavSkHLHMdFG9skSZIkKQhCdCcGIZQOYVk2+oTSGXiejzYjpTNIZvxZzyDVf+5yBghhcXFx4iZcBqcTiP6lBEFQFIUQ6mLySdEN8m9B9zMM5CZKy00FQYhvTKzAC4nOq98bPp9PzswRfgtgsA7IZ6Sb1pEeBUIYCcCGEAIEztrdSIckSrCoaPCuqowVGzOkUCqVzc3Nka+Rp6bLBzxrnpoQBFFUVJRsKTCDh9VqzbKkl7e4GxoaRFEkCCJBOtCPF6jFYqFIyu/zNTc35+XlxV2kLpAkqVQqQ6GQZPQWFBSQJFlXV+fz+SL7ZGZmpqenu1yuSI53AIBarZZWqpeVlUWfMCcnh2GYxsbGaEd9RkZGRkaG1+s9efJkZCNN01Kbl5eXR5vcUv3q1tZWu90e2WgymWw2W+LWQCa6E6BpWmrnyAyswWDIyckJh8NVVVXRexYXFwMAampqoudqc3JyDAaDw+GIToSWlpaWm5srCMLx48ejz5Cfn09RVH19vcfjiWy02Wxms9ntdkdHaqhUKukmHj16NKKrLS0tBQUF8Z3F8Hi9dru9ywKQlAK/BTBJ1wG1Wj1t2jSZa5SSy0g3rQcCDhLDYGQya9YsqXQQAAD1BABgz549yRYzRTEajVdddVWyqp4ihNxut16vx9PVIwSGYbQatQg8BEFIpvVZdQBCKOlnS0urx+NJnK6aTCaSJAE/SNNwKpVq5syZEU+mlB3XYrFE+zZpmpbywEWPOwmCkIaAJpMp+oRqtZogiIyMjGhrWToDz/P5+fmRjRBCae1renp69GBDpVKRJGm1WqPnIhUKRbwqLfdIojsBmqZnz54d7bWWcvWJothjxgGz2RzttGcYRqFQ2Gy2UaNGRTZK+QgQQl3WDki3oMebyHFcYWFhZGOPNzG+aQ5OF99K9Tx5+C2ASboOaDSa6dOnJ+XSsYJN6x9ItS4DpzHDDD96tKuxqvcNRVFdBuiDiSiKra2tWq0W5wUdIUilsyUbUgq6PqsOSLaKZIrHejnJKy5zZ6/XK4jCYCaJ6W5H9WhZKRSKHgN6ewyP73GxLkVRPe7cY3IphmESakt3YRA6Acm47bJRyjjQfecePVc9ngFC2OMZBn4T44KUJD8up6Jp2mq1EscapLkD+QdCALU6bd8LyPFbAIN1QD44jRkGg8FgMJhTqNXqnJwcgiDkl0pGCIXDYdSvHJum9HSz2Sxz5+bmZlwKCzNsaG9vF+Kkz9LiBSkmQma+fQCA2WwmCCIt7SymNQaDkQ82rTEYDAaDwZwCQgghFEWxvr5eZgYEqeSpKMRcfCvFiy1hMAnF5XKJohgXtzXLsk6nEyEkf0YMQmg0GiEBQ6FQl2x5GAym3+C32g90iUo91ug+6egrpzEAAAIYmacfY9Mcb/bCXuJwsgzKcUXZMUqEQ2Qxw4r169evX78enJnAbMmSJUuWLEmqXJi+kNZ84oj9kUMwGGxra4senSdIBxiGARB0OhwdHR0xFdNKS0vLycmJrzCYPsCdQEIxGA0Dz2EmCILH4xEEoaGhIRwOy/FCI4R8Pp8ois7OzkAg0IevGysABuuAfLBpHQ2M/uiBaZ5QnzN/6IcjEAIZLk9TgCJ7mIOHBEEw/pjTrmAFxgwzli1bBgCQrGuJpUuXLlmyBGcE7BuWZdva2qxWq/wwvzhCEISUJhczQhBFkWXZ6KdSpg5ACC0ZFvkZXNva2kRBFEUx1h4AQhhr9WzMQMCdwHClpaVFTn4ErACYpOuA3W5fv379smXL5C8gShbYtI4m2lYGY7SBPFu6zN0BAnq91Wr2dvc0t3Y4j7gTVW0SgxlaXHHFFQghybru264+cODArFmzcJQaAMDtdm/YsGHlypUxefbihSiKDocjPT0dGzMjFvk6QCsU8mO83W53rGnPlEolhDAQCLS3t2dkZMR0LKbf4E4goXi83lgDNwYZrACYpOuAVKN+SHhisGn9A13iHAw6rdUS29SIMqOHjJ0sJwI3Ng8wmFNI1jUAoG9/9axZswZRKEyvIIQcDkcSU5Rjko4cHYAQIgDcLlcwGIxv1d9o8vPzaQUt8KFQKJSgS2C6gzuBBGE0GgmSFHghxZPzYQXAYB2QD55/wnRFOPH8rMyfbOw6G8DteXjM+F9+k9LdP2aIcMUVVyxdurTvfViWxS5rDCZZEASRl5fXYy2i7pAkWVRURBKEzx+QmfkMg8FYLJZ4lTIiSTItLa1/S2EpSoHd0RhMvMDP0jBD7Pjv89fPLDSpGLUpZ9zs1c9ud8caPEFYFjz49A3j5QU0hNddrRv3qwPY4MbEmwMHDiRlaTEGM8KhadpkMkFIkCQ5CElrDAa9wWCQuXNVVVWYDSdUHgxm0AgGgyj2UvA9QtN0eno6SZJ5eXnya54zDAMhYcmw6HS6uIiBwWBwQPgPdMkQnnT6IQ9yfnTf6hfcD39w6IsZZq61Ys+Wo3TMyxKgfurKH8d6EAYTZ3BAeIoAIdTr9SnVN2ISikKh0Gg0CIltbW0cxykUirPqgJSXWIjRSDAajQRBUApa/iRarGuzMXEBdwIJoqGhged5QMahYaXVVQiBcDgs805BCM1mMyQIJXOWBxArAAbrgHyw13pYIdR9fzhw/g13zslNo1XGvCmX3Xr9TAMEAPANnzy8oMhkyCi+4I6/PLpQv+xdNwDhj64zzX+lQRqpcDt/Nmryrw/yZwSEI++3r1w7xaY3Zk244ukdDrlWur/sn3fOLUrXatML59z+1hE/AAAA5Nz70g3n5Rq1WkvpogfX1bAAANT6xkLdogefvvHiOTOnlE689LEtrXjcNLyBvdB9TxwQniIQBJGZmYnDBUcOHMf5fD6EUCAQkMbrcnSAZVkQY4IZi8VCkITf5/N6vTEdyCgZo9EY0yGYgYA7gYSi0WjkB270QSAQgIj//sixjo4OOftzHFff0CSKYjBwlnUcWAEwWAfkg73WP5DQyZjyk22eIIekpOJnDj8gBAhEbYQAABBC/YnEo4pnnW/8y7N3/gHdfPGMaROLTdJMpHjyH7fdumnsP8s2XWqo+edPFv+TnXyznNMFd/z62qc679rUcN+E0LZHly6vF9fIOWrnb1Y9XHHjJ5VfT4cHnlt1xarHSr5/cXbwk/tX/9H3q89P3lHq/+pXS6655vnRu381AQDA/ner9+tt/52l9e379UVX3j3m23U32PCs2LDCbrdv27at73265zPDGcJTBFEUpdJf+J06QmBZ1ul0Rj+ScnUAwjSNWr4LOhgMIhEFg8FgMKjVauVLSCvomPbHDBDcCSQUmlYMPPMfhHDs2LGb/ruv2e46fPjwggULznpIWVnZnoOHGAAKCwv7zqqAFQCDdUA+MDrNZvSrtLfPMnfr+/PevXtxUAEGg+nOjBkzpA/SAJ1l2S+//HL58uXhcGIXWHo8nqysrObm5hRcctbR0bFu3bpkFd8SBKGqqmrUqFHxSreDSXH8fv8f/vCHnccbL55a8tBDD9E0fVYdEAThxRdf3LT/+NLZk++7916ZF3rrrbc+2LQj26B85ndPyKyktWHDhr++82+jmnn95T+lpaXJ/UmYgYE7gQTx/vvvv/3xlkyD6uU/PTdwfW5tbX3it09V2QPnFlkffujBvsv/er3el1/+y7ZDJ8yM+Psnf1NUVNTHzlgBMEnXgWAwWFdXV1BQkLgKFAOHYZiPP/44aV7ryAAag8FguoP91RHS09N/8pOfyC8XjMEkC5bjeJ5PnK4WFBQoFIpQKNTZ2YlNa8xQhyRJAGEwGPJ6vQPX54yMjHlzZzd+/HlZXcvatWuvv/763oI7gsHgp59+uv9IBQWJebNn5ubmDvDSGEyiUalUY8eOTbYUskjaWO3bb79N1qVHOE6nc/HixSMtGQxBEJs3b8bL81KfadOmSR9wQHgEgiBwsnTMIAMhISUclrMzQRBWqxXC8o72jkAgkLjQD4ZhCIjDETHDhOLiYoVCAUB86tURBLFo0aLGxsat31Vu3LZHr9dfddVV3R9hQRDee++9z77cHobMlKKMpUuXyqyxh8Fg5JBMN0hhYSHP46pNgwpFUU6nM9lSJI2hrnIURdXW1g71X9Eb0q+LfMUZwlMECGF6ejpewjNyIAiCYRiCgDqdThqXn1UHIIT9rqmrVCrlB/i1trbyPI/nmQYZ3AkMFUwm07333ku9/sYXew+9v2HrkWPHx5WOHjVqVEZGBkEQdrvd7/f/d+eu/UerWcDMGl/wwP33aTSas54WKwAG64B8kmla4/jGwWeEt/lQ//mS/EP9V/RGl9+F/dUpAkEQSVnjjUkWKpUqIyMDgDqfzycIAkmSZ9UBURRdLpcYY4ZwiqIABGlarfycZG63WxAFXNtkkMGdQIKora3lOQ7Eda6Ipuk7bruVC7+6++Dh/ZVNBysbGPgFRQAAgIAADxUhAWgIbvaE0p/Js6sBVgAM1oFYGDIvKIZhAAA0TZMkSZKkFB4pbcTESm8FkIY3sbbStm3bfvbAQ3v27JG+7tmz52cPPHTWTNeYeHHgwAEcBZ0KiKLY0NAw0paQjGTQaTo6OgRBADJ0ACHkcDhQjEpSVFREURTPcX0X/ulOZAyAGRxwJ5AgOI5DCCkUiviOZlUq1d133/Wzu269cEKhnhIBhIhSIkopAqiC7KzS7Lt/dN1P71kj064GWAEwKaADoiiyLDsklHCIub8iNlI/LCVMXNi3b9/TTz+zfv3HDz308OTJk2688cZkS5QoKioqVq68qrLy+MyZMwEAlZWVK1dedbTsu3nz5iVbtBEBDgiPkNzEmAghv9/fvToaZrgSCAQaGxujRzDydYAgiFjfzi6Xy+l0yswQLqFWq202W0xXwQwE3AkklLQ0jclkiu85NRrN3Llzzz33XLvd7nQ609LSpPmvjIwMi8Wi0Whiek6xAmCSrgMOhyOJpVJiYgiY1h6P59ChQ8XFxdXV1YWFhS6XCwBgMBhqa2vz8/MrKyvPP/98nCl00Hj88adoBQ0AEATxzTffXrhw4fAe4uBJnGSBA8Ij+Hy+7du3m83mVK45gcFACDMzbfKju2tqanie78c4BAGEEMKdM2Z4IIpIWnkR9zOr1eq8vLy8vDzpa0lJSdwvgcFgujAETOu9e/du3LgxNze3oaEhNzfX7XYDAPR6fXV1tdlsrqurQwgtXLgw2WIOJfoXIA0ACAQCTqcn3WS69ZY7WlraTIb0ioqKzMzMuEsYd/r3exUKRd/FHoch/HfPXvYI/OPGX0zupYBt5atX3tL2yNe/nZnglKI4QzgGM7zheR7E6AKRkpb7vL6GhoaIwYDBDGncbnd7e/uQGEphMJizktJrrRmGYRhm9OjRkl0NAGhoaPB4PB6Pp76+PhAISEGSo0ePlvbs8SRCx76//+Lai6dPGDtu6pzlP/3zlw1RQ/VQ7eYX7l01f/rEsWMnzbzkpkf/sbdNGJzfNjRRq9UWi8lotIoCbc3I9fpcEyZMkH+4UPPyxaPu3sICAMKf/CR3yZtNKb5mguO46urqWI7g9z05e3RxcXFJyehx58xb9dC/vvcMfvQMd/DpBdPu+cwVdWWh+vUVk697W0aDE7mL7lmzKCf57iAcEJ4iEARhs9kIIqVfFpi4AyFkGEaalDyrDkAIpXWb7W3tPp8vcVKZzWaKHAIugWEG7gQSRHZ29pDIS4oVAIN1QD4p3UbhcDgcDldUVEh2dTShUEhaCVZXV1dRUSHt2f0MyLP7mRvv/IBb+uz6fYcOfvnmbZk7H7nu5xvaRAAAEOrev/vqX+02X//CRzsOHtyx9rlrsw5t2uMaGWtJ+p0M7Nlnf9fWXs9xfEtrw33332WxWHrZkd/58PiSn37FRW0iLfPufeKasYqoFfODRUyN43a7m5qa3G633d7hcrmampqamppcLpfd3hH5Ux9Na1z997KKirLdHz5ceuS5B179ftDrZCkmL7s8Y9f6rxwRXRYqP/u0btryS7LO8sALgkCYz7niiqnpye8ZWJbFLutUAEJoMBhifYgwQx0IYVZWllTw9qw6EBl1cTwfa5qZmLpov98/JNLYDDNwJ5AgNBoNHAq2ClYADNYB+aT0I03TNMMwpaWlubm5AACVSvX4448//vjjv/rVr+666y5p7qSgoKC0tJRhmJ7yhQq17//pA/LGF5697twsDa1MH3v5Iy8/WLz9+f/5jgPAt/WVlw5P+/Vrv14xNcegUumzJ1265qUnlqVDgNzf/fOBFRdMnTjx3ItvfnpTAwcAAELlq8tmP/i3v9+35PxJE2cs+9UnJ9t2/+XWxdMnTpy1+vdb20UAALv5vqmrfvOXX/34mlVLFy669smNDaeMqkDluseumTdt4sRpF1796NqKADi18+rfvvn4T1Ytv2TevFW/3tB4yl/ON2994Y7LZk6ZOGnGZXe/tq8zlUx9p9P54Tuvh0NeBIBKTa9YsSKmw6Fu8rIbLsyTp3QCn7QAgteffXbPzTdP2bZN/carYzdvPnrbbUdvu23s5s3qN16dsm3b0dtu+9sf/tDX8QRBkSRtKLnk6ouz2yurBt9vTY65Ymn+/vWb204NQfmyTze0zly+KO37v9+z6qLzpkycOG3BDb/dWM8BAAC/78l5lz7y6rO3r1yycNkz+4LfPbto8R8OCQCIjZ//7ubLLpg2eeKUWVfc+z8Hf5h1Qt7v/r7m8lnnTDnvkrv/9l0S/PKYQUQUxZqaGmzPjBxUKlVWVla0d+KsOoAQ4nm+fz1ButksPy1NY2Mjx8eWThwzcHAnkCDsdrsoDIFYSawAGKwD8klp01oQBEEQ7HZ7tJMQIdTZ2Rn56nK5pAIhQvfuCTn376nMumjR2B+MbiLz4ksm2ffurhH4Y7v3B869dIHlzBkYCJDrq9/d86Z/1T92HDq48Ykxex+9928V0qlF59Yv3de+tePg189M2PfkzXd8oLnvg73ffv5Qxobf/+OQZEXzh3e0LPzTe2s//eyvi0787ufv1AkAhA78ec0zNXNe/Or777/684V1f1jzwjchaecju1oWPv/hx5s2/vm8g0+9tC0AAOCPv3H3I3tGP/bx/kP71z5g/vTBJzamkHH9+eefr7lmdn6mEQIQCoZiPVyoeWlByV2bT3si+fr1P79kclFOzrjFD39UxwEAhGPPzR170++fuXnh7GlT1nwSiK/0siFpuoFlG0KhHv/VhcOEnLovoufwV3tajAX5aYM/yUcWLV029tAnGxtFAABgD36y0X3h8vkGqMi95NH/3fbt4QOf/LJ4z68fX9cqdZJCzfajJY+9/9kXnz0664cV1AhoJ9/y8oY9332//c3lvv958JUDp+6c2PnlJvs1b/33293v3Sy8dd8fdvgH/QdiBg+EEMuyODfsyIEgCJIkRVGsra2VIkfOqgOiKJ48eTJWIyEnJ4ek4p+6CRN3cCeQIDo7O3sYu6YeWAEwWAfkk9KmdY8Eg8FQSJ5RJ7qcLmBMN0ZbNlBvMhKdDicSPS6PKt2o7H6BfZ9t1a++75rSNEKRMfueO2bVf77lhAAAAFA5++Y7zrcoKMucS86n+cnX3DjRQNI5ixdPspdXuBEAABCFS66bayIAYEpWXzOt8vOvm0X+0IbNwUvX3DrVQJKGqbfec1l48wYpRpgsXXb9TBMBgHr87GlUVXmzAPgjH/+n/eKf3jPLShPK3AX33Fi8e/P+4IAbrQv9DqsO2k+MKswSRAQgDAZDZw/C7mFj5AN/4N87x7y4p6buu79O23P/nX+rESGEQGz/erf64fW7Dpa9eaWm34L2Io5MHnrySSor62ar9fbMzNszMwUkBgT+cpNJ+iparQ888UTvRyPX+vtnz5hx7pTzb16vvfn3d09PRvlVIvuy5VMrPvmsRgAguG/9Zm7B8tlaoJqwaMnUTDUJmeyLbl1dUnbgqDQjRFgW3rgsjwYARLcSmTP7irklBpog9RNu+NH88HcHT0dWUDNuWjM7g4LqkqvWLFd+9fm3OG4bgxk2hEIhu90+CEMotVoNIXQ5Ox0OR0wHatI0OOcTZtigN+itVmuypcBgMPEhpdMnkCQJITSbzXq93uPxAAAQQk6nM3ofg8FgsVhIkkQIdZ38I/RGPXA6nAhkR7Yhd6dTSMtNgwRh0AVbnCEAzkx07scKTgAAIABJREFUjLwOB2edaTs16aDKzDI4OzpFAACAaoNRMpMgTTNGvUHaR8EwkOM4ybQ2mU9b8owpXe2ydyLB6XCZx1pPtTRpy7K4jzh4AACAadpTDk1IURTPsgAI7S3t7j2/WbL/lFCcnx7rDyCgTvbqBoTQ0YM777thLgAgzAoKFbhg1tyBnRLmrP7Zj8ZpINDM+/k9Myf8/fP6u+4CAKrn33LrRE1chB4YNEGoFYrmcNi2Qu20c7YapZqiAACTzlJ/FeoX/fajR89X0ml6nTJpHhkiY9GyGX/826cVd9ze8snX1KI/z1ADEKr+7M/P/eOLY21hSAK/IzAjyANAAwANJkP3WTbUuf/vz7y07ps6j0CSnNtlvPnUlBaRZrWd1tyMTEvghCMEQDImEEYKEEKFQhHT9BAG028EQQgGg/0wrSGE5nST/BJxdrtdFESe7ynorE8ISEiLwDGYYQABIc4OhcH0jV6vv/zyy/V6fbIFOTspbVpLoWiRNGbBYPB3v/tdMHjKiRudxiw/P7+H46Fp+sySpz7dcvynEyacGviLrV9tOqSbdksxSQmzzlM/sulr+9Jl5qgRK4LadJOirblVBCYSABBqbXEZR5nkdnpCS2OrCLIJAJCvuclrmGmCZCjdYG9pF0AJCQAQWps79Ob03tqdTM8wW5c9t/Gx6ckeNtz+8GPrPKfKk6odJ6/NBXdfNQOAPADAykWT2h1uCFtefHKNpei8G268uV9XIDNzbJLlCTVZmbrOdocI1ABqzempZKUd9vncXm7PftfFWsMYigIAlLe09J26Gqp0FrM52TcQGhesmP3M8+t3FrVs0176t2kMEFvWPvHEofP/Z+0rU4wKsemf1122P7JzDyfg9r380P+CX7y98dJcNcHtfPTCp0/XyRF9bW0+BIwQANTealenp3cP/sDEEbPZfMsttyTr6gRB5OTk4JHfSEa+DjBKpXyjt7OzM9aVewqFAgIYDAXtdrvZbI7pWEy/wZ1AQvF6fQ6HIz09PdmC9ApWAEzSdYCmaSnxVuqT0s+JVFIrksZMEASHwxE4jbRPJI1ZT8W3yKJrH1jNvf3gL9//tiXAhR3lG5/96R+PTLjjlgtUAKTNX/PTiQeeuvvp9d83e0IhT/ORz1+974n1DuWMy+e51r269kQA8R27X3lzV+4lC0vkOh9R64Y336vwI75j16tvfVe6+KIsgpp82ULl56+9ddgtCO7Db/11I73o0sm9mdbU5BUrtBteeGVncxABIeQ4sXvb4U4EAHts3fNvbG0WAQAgdOTfPX6Wj5zw6bBKH172YHjp/WFbqXP8wruvmjF+9KkionfdeNnj9197+3WLCrLT7R1tfYZgd9nwwyYAhbbGNlH6HGhp8aRbzCSEEABIxBjpLZOYmgjRtOS0WWwylZf7S2l10WlXDBd7qQzk/uad59/e50QAAOTc93ZPn+MO1M1dcVFg/a//uN16+RUTKQBQyB+gLAUFBgVAvsMfrj/Sd+py0e8LaXOLbGoC8M0bP9za+YOW8fveeX13B48C1Wtf/zhw0SXn0PFWUUzqACFMS0uL9QnCDCdk6gACwOv19litI14UFRXRNM1zfGQMgBkEcCeQIHQ6HUEQPM+neDkMrAAYrAPySWnTOrr4Vm5ubjAYLCgoMBgMBoOhoKAAAFBQUNB38S2on/PYO6+tgh8/vGT6+PEzlvxsi/W+t169qZgEAACy8Pq//t/vzm99+/6lM6dOX3Ddo+sc519xgYEwLPz1Kz+m3rtp1qSplzx29NzfvnTbGNlhvdSUK2bWPrHkvHMXPnJ42m+fu6mQBEA5/YFXHs796p75U6bMv+fLnIdeeXBG7/FyivFrXn925ok/rr5g0qRzL1z9i3cPu0QAAFe15V8f7msTAQAoXLXlnz18TgwECaYtBgAQRNfHyaDVTJtYEgr2uRJcYMOhU4TZLvljUf3aP79d7kN8x3+ff3XP+Csu7Z46XGza8ufH3z4YBAAAsWHTCz19TgzaWbNOBAIIoYZwmKwGjxoLaIIAANQFg8oZM2I9G/KUffavTw57RACA6Dn8yT97+JwA1DOXL6Jb7SVLl44mAQBkweoHr/G+fOXlq6678b4P6Unj+54iYObc+fPSr9dctuya63/yTHneOdbI7SFMFy8yvvejudNmXvMP8aYXfz43DSRRRTEJRhCEysrKIZFrBxNHJB+F5II+qw4QBFFYWEgQhMeTWNMakxRwJ5AgbDYbORTqWmMFwGAdkM8QeKSnTZuGEBo9enRhYeGECRPa29sBABkZGWVlZRMnTiwrK5s+fXofh5PWmbc9P/M2AJDv4Is33b29ORhl4KmKLnngL5c80PUYwzm3vLS+S/wlOXrN+h2nv1AzHt/26ekv9OKXvlsMAAAsAIDMvuTXj/zo8TOPVY+56ukPr3r6jG2RowAAgDrvN1s/O32hzAvvffXCe888g2bZG0eWSR+h/sq/lV3Z/XPCoHqO7mu1O//3422MeXzvRyLHO6tt75z6Qpbcv+XD6FXK1PRVFxy9b1bhYTsz/uoX37hzFAm6PrJi2663Xztq+dmN56ig0LLjX3+tsHb7PJCf1js33Xbb37zeTV9+qReE0Wr1m01NAICAQgEWLLjlttt6P46a8fiOfd22Erk//uDIj6XPZP5ta8tu6/45ETDnP/HfyiciX6Fp1v1vbb4/8n2N9F+0MgNATX1ky2ZJupJVf/x4VeQPv3hI+n/0mvU7AQDg7jvPuFYyVRSTYHC9jRGFQqHQ6/UQtkav8O9bByCERPf5V3nodDr5y+dqampYllWmtFNgeII7gURwKuXyUHAEYgXAYB2QyRAwrc1m8yWXXMIwTFFREcMw0pQJSZKjR49mGGbUqFEyzwPTzrnvtRcsayuOtJ47J3NEF/yIS0RHti39rhsue+2jI72cjZ77QqX3ha5bt9ZK/yuX/6tlOQAA3PNi1F+p8Y/sqoj6rpj22+86Tp9vxtOH7T18lkesP5kkyTsffrj+6qtvvOnHOr0RAOBxO9/5xxt5eXkxnQeDwWCGFjRN63Q6AFBHRwfP85QMr5ogCM3NzbEOvKRoWLrn9Vw9w3EcArj6C2aYUFdXx3McYIaCbY3BJBWe5z0ej06nk/NKSi5DbO4XRdGPw0nrrBvX3DTC7Wq5iCLhDUj/YCjc1ngy7HOF/Z7Iv4aTDWvXf6FSp0Iy70SRl5f37w/fNxp0RoPu3x++j+1qTFLw+/3ffPON34/rh2MGA57ng8GgKIo+n0++tdyPpOI2m40giWAgEKtu0ww9JPLEYjByUKtVOp0u2VJgMCmN0+n88MMPu1SJSk1S3fSPIC3fimR6kHzXKbam64wY76EO6etEn5zyKKe5Gz+jTF/uXQvRGZmvEAJZE1M3p2VcyMjIePtf/0i2FJgRTSAQ+PbbbwsKCjSaJMxkRZbRDv6lMUkhHA53qWstVwcgVKtV8l0KLMsCBPx+v9/vj0m3GZrBpvVggjuBhMIwTFL6dvlgBcBgHZDPkDGtMXFETnT0v/7y/L8GQZTBAmc1xGD6TerHX2ESjRwdgAAYjUa1Wi3znHV1dTzP92McIvnV5RfQxgwc3AkkjnCYDQQC8h+cpIAVAIN1QCZJm37QaDSiKOL5j8GEIIgRnoRgSKucdPtomh7Sv6I3pF+X4jP3IxNRFKuqqkZ41zHCka8DPC8kVFVyc3MVlCIYDHZ0dJx9b0ycwJ1AQgkEAm63O9lS9AVWAAzWAfkkbQYiLS2tvb09IyMDz4IMGqIoSvnV+7dSfUgj/eQhrXLS7aNpekj/it6Qfh02rTGYFAFCaDAYZM7iQQjNZjOEsK2tzefzJW7hqFqtJggC4NEdZlgwevRoBb0NAC7ZgmAwmLiRtNF5Tk5OQ0NDa2srzs0zaGg0Go1GYzKZRmZ0tMlkUqlUQ1flpNuXm5s7LB+cyK9LtiAYzEgHQkhRFEEQRqNR5hQeQRB6vb5/b5aYMoR3dHTwAk/34zIYDAaDwSSeZDq+8DA6KbAse+jQocmTJ4ORsQJZ8lcfOnRIr9cPD5UbHr8Cg8GkJmq1OjMzExxrlPKEy3Fci6Lo9Xr7Fw8VU13rzs5OQRCGWm0TDKZn6uvreY4DeK4IgxlGDKuYUowciouLq6urd+3a1dnZmWxZBgmTyaTX64uLi5MtCAYz9CAIYtSoUcNveT+mbxASW1tbeZ6nafqsOoAQam9vj3UZ3ujRo6mv9oiCIAgCScZQFJMgiGG2IibFwZ1AggiFQgghiqIUCkWyZekLrAAYrAPywS+nkQg2MjGYIQRJkkajMSbbI75I9lWyro4ZZPx+f2NjYxc7OXE60NnZ6XA4MjIy5B+i0WiysrISIQymN3AnkDi0Wq3ZbE62FGcBKwAmuTpgNBqvuuqqIVEBHk8/YDAYTEpjMpmuvvpqk8mUlKuLolhbW4vzgo4ouoR2y9QBCGF2VpZWq5V5lfr6eoEX+ikiZhDBnUBCQSmfXBYrACbpOkBRlMlkGhLxSti0xmAwGAwGEw9gDCk8pGjYmE6v1+tJgvT5fI2NjbELh8GkIi6nq7W1NdlSYDCY+IBNa8ygcuDAASmeJKYPGAwGgxlkYvIPKJVKAIC9w57Q4gU2m42iKIQQdqBhhgE2m40cCl44DAYjH2xaYwaVWbNm9eMDBoNJIjhzyQiEIIjc3NzI5GbfOkCSZHZ2NkEQYZYVhATGeIfDYRFhozoJ4E4gEeh0uqHSsENFTkziwDogEzxbhhlUWJbtxwcMBpMsSJIcPXp0sqXADB5KpdJqtRJVP0SonlUHEEL9Xiyabk63WCwyd66rq+M4jsIDvMEFdwIJwuVyiYKQ+iNxrACYpOsAy7JtbW1WqzX1o1nxCwqDwWBSGq/Xu23bNq/Xm5SrI4R8Pl+KZ9nBxBGSJGmaFkWxvr5emt88qw6IolhTUxOrvzozM5MkSQgJ+cuzMUkBdwIJor29PaFRHvECKwAm6Trgdrs3bNjgdruTJYB8sGmNwWAwKU0oFCovLw+FQkm5uiiK3UsxYYYx4XDY6XQihCKD/gTpgFarhQR0u1xOpzOmA9VqtdVqja8wmD7AnUBC0el08gM3kgJWAAzWAflg0xqDwWAwGMwpRFGUcnezLMvzvJxDBEFgWRZAaM2wSPnM5BAMBpEouj1e+dNGoVAIIUgrKIZhZB6CwaQ4JEkMiZJCGAxGDkPmYUbO966e8tmqI+9dpTrw+AVr4Gu7npg2ZITHYIYBLpezob6+ezAQBCA3L89gMPZ2oM/rPX78WI9/Gjt2XJrsKrgYDGYQYBhGp9ORELTbO/1+v1qtPushfr+/w+4gCaKgoCCmhXCEyIc41NHRkZmZedadEUKtra0cgro0dTAYVKlU8i+EwaQsfn/A5XIZDIZkC4LBYOJAanithWPPzc0wRmG79v96jaYn8y7/+cOX5xEAIPs7q/L62HOIwu179NwZT3wry1eAwQwaDfX1wVDQoDdo1Golo5T+qVWqUCjUUF/fx4FVVZUKhSIrKzs7OyfyLysrW6FQVFVVDpr8mP4BIaRpGq+GHTkQBDF69GiGEJsd7pqaGiBDB5qamk62dDCEmJWVJf9CRUVFSlIMicT+/ftlrji1dzoRhBaTAeeqHUxwJ5AgNBoNQRAsywaDwWTL0hdYATBYB+STOi8n+pJXq1pOc/Ltq/S97UhknLdq1XRL6kiOwYwIEAA2a2Zefn5ra4vd3uFw2Ds62ux2u9FoOmteC7PZsmnTxpqa6qzs7Kzs7Nra6k2bNprNiVpd9txzzy1YsMBisWRnZyfoEiMHgiCKioqwJTOimDRpklmj8IuKsrIyIEMHDh8+HABMGinYbDb5V7FarZPGjGYR3LV3f2NjY9878zz/zTffHDnRoIb89OnTcUD4YII7gQSRnZ09JOpaYwXAYB2QTwq1EaFQRmAUBAAA8E2f/2bZlML8kimLH1hbzUvjd+7A4+fNeOJbXqh87fandvm3PzZ70qQpF/9+P3fG2dj6z3933Zyx+Tm5xZMW3Lu2XgRAbN/23A0XlOZl55XOuv4PX7cJAADh+PPzJ9zx8qu3zCnNzSmdf9/ampbtz109Y1ROztjFj25pEwEAqP2tFblXPvbCHcsvXThn+qxVT33dLq3iR64Dr9++YHxBTk7J9Csf+6SOPbVz3qonX71nxcJ5500+94onv2o/JXXjlt9fd8GY/Ozc0gtuenGXHfW2c3jLL257p772revPmTTp3Fv+rx0nZMSkDizL7tyxva62tra2prr6RG1NjSiKgijL3TR27LjNmzb+d/vW3bt2bvp846hRCaziwLLsihUrbr311sRdYuSAEHK5XDg37IjCZrOtWrEsjSE27/xm+/btfr+/Nx0IhUI7duz49MsdUOTnzZ6Zk5Mj/yoajWbBgosMFN/qF7/eurXvDOQHDhx48S9/dQX5sQVZ55xzTn9+Faa/4E4gQYiiCIZCq2IFwGAdkE8KmdbdEBveve+nW4r/sLuqavcrU3d/uOvMIsfk6LvefOwCzYVP7Tx8+PsvHz1PEfU39vs/3XDPtqLHNpc3nDz61V9uGKeBYuO799z6of7BzZWNlVt+blx7+5p3G0QAABAdWz53/mjdkZqDL0/e9Ysrr3877ZFNx6t3/8b20aOvHjxlr3O7d3hveWfjF//d+vKUr9c8uLYdAeT8/Jc3v+y//t9H6mt2PTd+5/23/D979x0fRZn/AfyZmW3JbnY32VSSQAKEJIAgTaqAAiJVIBQ5RAXsyllOEfDkfmcBQRSxoHjYCxaQIkVp0kEBEZSaBALp2ZTdJLvZNjO/PyasIXWBbGaS+bxf9zqT2TLfnf2w2WeeMu+eFsZwuw7tL5/5+bbdv+5eGLdu7rtH3YS4/1p+7+y9yQt3n89M3fZC2JqHn19fyNd+Z/Udi/83vXX8jK9+P3ny6Ed3h2PsBUgHx7E0zdw1bsL4CRNHjR5DURRN+5rQfv0HjBg5evu2n7du2TTsjjsHDhrsvzr//e9/P/HEE506dfLfLuSD47i8vDysCyorCoVi1KhRt/XqUupgl6/8+Msvvzx79my1Mds8zxcUFKxevXrZiv8V2lxJ0SGjR49WKpV1PWetunXrNnXC2AAls2HXoQ8++OD8+fM1F04rLy/fsWPHig9XmV2KUJVnwvhxwcF1ruwA/oAPAT9JS0tzu90N309sCAAgA76TzkAU185nurabK/zMtH1kzdZnI3Zs+LXTg+8Mi1JRJOEfT01Zsa32lZBqcv/+3feFo19/YXjrAEJIWMdbwgif/8m6Q0kPHRoXp6FIm7HzH/6497rtBffeRwgVcNvDT90ariLktrEDlDsU9z3QLVhBgseM6T539SkL3zOUEKLoPnVGzyCKEF33GVPb99+w2zpp9P4fthnvWXdvxyCaBN323JODui3bdP7J5FBClH3umdEtiCIk+NbbbjZ/eaqEv/nSt9/kjVj83KBINSFtRsx5YFX/TQcrxo8htd25F74zgIRRFKVSq1UqFU1TNE036zOYTqfT6XQKPwtXjWZZVmg/UBRF0zTHcd5ztDW30DRNUVTNLVVbIMLoqap/jWpuYRiG53nvFmFHVbcwDBMTE6NQKLzP3JTlCartumbB9Rcj4tFDeddd3uRJE1PT0s7lsht2HTr2+x+pqaldunTp1KmTQqE4e/bsH3/88etvR89nF7K0ukNE4P333RsRESEU4Ht5KpVq3LhxHo9n9Y/b1+0+cvC3Y317de/evXtkZKTJZAoICDhy5Mi2bduPn0kr51WtjernnvpX+/btm8XRa2HlkSsfAtIsT+JHr67yhP8P0GgCAwO9FUqnvGpHzxsAaZYn8aPX3MvzZkCs8oxG44wZM6p+EZLO0WMYhlQhnaa1csCLP709WksIIYRSBIUquFRzcUB0bIjQsc5Et45hfG1aswX5RZGdY6suU8oVmYuUrWIqp2jTpphW6qKCQpYwhNIGhwj3pNTqgBBj5Q6VajXldrv5yvuHhV55ZJiJKywo4UoLC11RA1tVHs2A6Jjg4vxCloQSQgfp9UJPHq1UUC63m2fzcnJL9v5r4MHKilxlypvKy3lCarvzNR43gCbFcZzb5aIoyuNhhU9bhmYafhghBw/sF/qrFQrF1i2bCCFt27bzc7ENWLp06aJFi6puSU9P1+l0hBCDwRAVFZWfn2+1Vq6TaDKZwsLCsrOzbTabsCUyMtJoNGZkZLhclSNqYmJidDpdenq690M8Pj5eoVCkpqZ6d5GQkODxeC5evCj8StN0hw4dbDabd66pSqVq27at1WrNy8sTtmi12tGjR5vNZu/zNGV57dq1I4QIy1nVVV5sbGxRUVFRUVHTl+fL0UN511cez/NTp0zevmPHkb/Ony2wXVz/s3rD1mBDkFqtKiy22lyck6cNKjKsT+f777uvtLQ0LS3t+srr1KnTgMzMw7//mVvOrNlxaNOu/UqKV6tUgTqdpdxRYncrKa5NKPPgjOnJyckWi6VZHL2WVF5ubi658iEgwfIkfvTqKc9qtfI8rwnQ5OfnCwdZUuV5j15GRoY3ABIsT5pvbossLyMjQ9zyCCESPHpJSUmkCuk0rSl1cFSrVrq/N/CmsBDnsUIbT9QUIVxxUXGNYQgURZHamqJMeKQp/1Kmm3TwfuenTWEm977sQo5E04RwRdk5zpBbQhlS4lNxbE5mNksSGELY7MvZlGlEMK0PNSlzs3JZEqoghDhysktCkkLraGIwYRERrSa9t39h36suSsIX1LE7ivC1vi4AcVE0TQj544/jDENzHNe6TdyAWwdevnTJXmFv8LFnzpy+c8TI/gMGEkJ4njt79ux1N60XLFjw5ptv1nrTsWPHEhMTfXyeZ599dvbs2cLPZWVliYmJ7dq10+v1RPhsISQiIiI8PFy4g7AlOjq66jlLQkhcXFy1LUJbtOqWhISEqltUKlXVLYQQrVbr3SLsyGAwBF25LJmwxWQyhYSEVN3SNOVRFBUYGBgVFSXcWWrlSfzotYDyOnXqtG/fvl2/7M4rsrp4yuJg2fJymlBBarp7m+ihQ24fMmQITdN6vf5Gymvbtu2gU6d27Nh58vTZCpahFCoHT2wWm4rmO0Ubu9/cZejQobGxsc3u6LWM8lq1apWTkyN8CEiwPIkfvXrK+/333ymKcrvd0dHR3gvCS6c875b4+Pjc3NyqAZBUedJ8c1tYeRzHCRmQZnlSOHpe0mlaE87tcDgq66FopUoVNnRszyVff3luwj+T1NbDn373lyf+6kdQ2mCjMjfjsovcdPV1NJXdJ040TXrttdGJc4dEU0VnT5rDeyYOG9f71feWbB6+eHR4webFK0/3fvS9cMrXpvWpL5atH/fmhKiCjUs/zbj1+cEGKmDA+GElc5d+PXHF9Hjb3qVv724z9l8dGFJc28OV3adM0U95ZemwlU8PjlE5zWm//mHrNLS7qfad0cYQveXPy2V8z2BMtAbpoAixlJSEh4d7zyDSDHP50qW8/NwATQMXmC0sNN9550iKonKyswkh8fHt4uLams11nVxqwOzZs6dNm1brTcJJTR+p1epq6wwzDFN1YA9dYzFMX7ZUGxrkyxaKohrccn3FNEp5rVu3rnYHSZUn8aPX3MszGAyjRo0aOHAgTdMlJSXp6elFRUUGg6F169YRERF6vV74FnKD5Wm12ltuuaVLly5ms7m4uFihULhcrpycnNjY2KioqJCQEO89m9fRaxnlKRSKah8CkipP4kevni3Cv53ycltpaalWq5VaeV5KpbJaACRVnjTf3BZWHsMw3gxIsLz6i2nK8oiUmtaunx5PiHq88hflzS/u3/FMh+lvLU17/P5B60PCjK1vvbWXqvrFOdQDH3js40fHJbzPR0z/8sAr/bzrp6i6PfvFcvecF4ck3F+hCukw+j8f90hufc+7K3OfeXFYh0fsAa0H3LfynXtjaeLT0saEqAZP6r5zVq/nUsuNPWe8v2JiBEVI8KjXPr307PPjOs4rVUT2nPLGqtmdFHX1Nau6/uvLd8n8l4Z3nF7Ka8MTek+eu3RoXTtj2k947I6NL3Rr87Sy38v7Vt8biQY2SEFs69aZly9XOBxVN1ZUVARoAmJrNL2qSkjocObM6Zyc7Jo3JSd3vI5KwsLCwsL8deEuqIbjuKKiIpPJVPOPCsgEz/NOp9NkMul0OqHr2E80Gk1sbKx3F926dfPfvsB3+BDwk3bt2imVSkKqL90nNQgAIAO+oxxVvih7u7nr+dnHu9X/8+HDh/v06XODpTcVvuCTCb323Hvu0/EasUsBkKEdO3aMGzfOu96YLzIzM0tKSjZv3rx8+fJt27YRQtq2bStMoq5LaWmpMOhRGBAOXizLpqamJiQk1Hp2FuQAGZA5BMBPjh079t+3PtRS7jcXvyoMtZUmBABEz0BFRUVGRkZcXFxAQAPDJEWkVqvXr18vnV5rAIDG8corr3z11VfCz/369SOEbNmyZeDAgaIWdf0sFsv+/fsHDBhgNBrFrgUAABpHTk4O6/GQa7tiHYAclZeX79mzJzQ0VMpNawGa1gDQ0qxcuXLlypViV9Fo3G53VlZWs7j8KQAA+Ki8vJzjOPrqNT4AoFlD07pBVPiMdZdmiF0FAIAYKIoyGAzCcjsgT8iAzCEAfmXQ671LE0sTAgDIgO+ad9P66NGjP/7446ZNm9Rq9YMPPujX5VUAoAkMHVrnAn8gCpqmpTwJEJoAMiBzCIDMIQCADPiuWTat9+7du2nTph9//LFNmzYpKSnbtm0TLizm8Uh9lUUAqMf69evFLgGq4zguPz8/IiIC64LKFjIgcwiAX1msVuHwil1InRAAQAZ815wO0M8//zx79uw2bdq89dZbXbt2/f333w8fPvzcc8/VesHupnfu3DnZ7h0AWiqe561Wa7XrRICsIAMyhwD4SXh4OMMwPMdzHCd2LfVBAAAZ8J3Ue63dbvePP/64ZcuWjRtGHFNlAAAgAElEQVQ33nHHHSkpKe+++67BYKj1ziJ+NlEUxXGcWAWIu3eARoFToQAAIB9Go5FmGELw5Q2g5ZBo07q0tFSYRL1z587x48dPmjRp9erVSiUuUAAAAAAAzV5ZWRnHcQSrgwM0hKIopVLZLNZRk1bTOj8/X2hR//HHH+PHj3/88ce3bt3q+8PFHajA8/z1FsCeeW3ALf93wjBjw4X3h6maeu+Nyrp6YpsZm/jb3jm75cGoG/gHwBd8NCbh8R3UmE8yv58a1Hj1ATQ/Go0mKSlJo9GIsneKokwmU7P4ewZ+ggzIHALgJ7m5uazHQxipH1gEAETPQGho6KxZs8Ta+zWRxAjMixcvvv3228OHDx88eHBGRsaCBQssFssnn3wyevToa3oeoXnJuw4+l6TVaAJ0XeYequB5nncfmX+TThPY+rHtTt7v2Esr7jAGaHS9Xj7pvrLN9dv8m3SagNC7Psnjan8U4au+ghtS+fI1Go1GExCoD4vrMfrpT09Y69hx4yON9UpIoz0TNAuN+JHSwgQFBQ0ePDgoSJxzTDRNh4WFYbi+nCEDMocA+FVQUJDJZBK7ivogAIAM+E7kXuuDBw8+//zzdrs9JSVl+fLlffr0uZFnq/x2Xvkdnfekfrzw68fWzYi5url3gyXXt3ee5wkVfdfkAf/et+PMDz/8+XznmxWEENeRNesveKig2yaPDqu1a/3KNp5cd4F/v7bKJ2Aiut7eLcKVdeLwqR3vP3aqNOTYh6ONvpxt4liWYq7/HGrVg329z1HtadDoAhAPx3HZ2dnR0dH4mypbyIDMIQB+pVAwKtX1DVhsIggAIAO+E/MAHThwYN68eV999VV6evqSJUtusF1dHUXTVNmuN5btt129nT2z6r5BXdtFhxv1xoi2PcY8/cUpGyGEONfdExIYaBw8d8XcEcmRoTE9pr5z5OLexZO6RZtC4/s/8tV5FyGEEK741w8eG3FzmzBjcGSH/ve8tjO3+gW/6MgxkwfrKE/quh9OeAghxPnrmg2XWNp4x5QRzI4XR/RKbhNpMhhCYzoOmr54Zx5bvXDnjzMitIGGQcvSWELYs0v6GwJ1sY9udxFC+LK/vnpuXK+2EUZjWHyvlBc3pDvrfP3KXk9++cP6rYd3v9JPRbj8nduO239dOrZv5/ioUIMhJCqhz8QXN1x0CUdkcT9DoC72nkXL7u0dZwqf9HX+ma+fHHFz6zCDISQyrmPvMS/vLqvvtTtTv3tySIeIkMikYU+vveCqpSHMF3w+IVyrjZj0dSFPCOELv5wYodVGTPzSzLvrqKoq98E5yUGBQTfN/81NCG/+dGxwYGDIpNWlhBDiSN/4f1MHJEYFG02xN496+ou/ytEQB2hEPM/bbDac4ZIzZEDmEAC/stsrrFar2FXUBwEAZMB3Yjat58+fv3z58i5dujTWE141spSOunNc34CMLxZ+fsFTdXAxa05N9XS866FnX3h+Zp+Ai7s+fGL6kmPuK49yH3v/tZ1M+9Yay9mN80f3m/q/3DadW1EFx7/413/WFXG85/yKqXc9+9lBW8d/PPPEiNAL61+eMun1E84rO63cu2nEpCF6ypO2fs1RF8/bD675MYulTXdOHqZ3XD6XGzpw2pPz5v9zTFzpsbUvT3/q+wLu76RWeQEUVbXjl+d5ns359uHRD6/Ymd963JNPT25n3vbmveNf2FPGX733qrnnWXvmidP5HCGUVhvI5Z7N0PSc/PjzLzwztTN3euubMx9amc56e8xL1r+27FzM4DFDOyo2LXhq1b7C+An/euHZh1J6h5ovZDvrfu3uv96a8ciqQzmkTe++kSdee2OXi9QYxU1CR989zEDK9/zwk5njOfNP6/aWU4ZhU0aaCFtXVVe9dkIIIRS5+gXyPGfZ/tzYe17/Mc0w9LFn7r/ZceDDR+96Yl1Bkw1+h8bUWJ8DAAAA0qfRaCiKcjqddrtd7FoAoHGINiD84MGDy5YtGzx4cCM+Z+W388qJy0yb6fPu/WPcyreW7Vqh/3sqs/KW/9u1V60iHmeFc3pczk0Pbbyw/2AW2y2S8IQQKnj829s+GnnhpT63vX62Iuze9dtfvXnfE8kTPi0+dTLNM17x1ceHSolx3H/e+/fwIHaQ58SY5Sc+/uTQ028OUlcduhw8fPLw4I3fXdz4w5EFnW3fb8ph6YjRUwZriWbaZ7/NUCs4V0WFYxD95x2vnz209w/X5NtqDn+mqMq2tfcW9vLaVVsKeM3AZ997eWo4SdGeHbjg8Jf/2/bireP1Vw0IF16+Y9PMCO1M4cG0vu/Mf9ysTXjnt7FqFed2OJzjg1N7zT3w+/7fyh+PDxQa5nT7x77Z+WqfQMLnf3yXm1CG4Og2iX2Sxz0wb0momiLu47W/9tn3711z0kECh7y66YeZUa7D83vf+XYaIdXbSsbhU+40bfh2z7qfzFOGb1u310aZpk4ZbiQ8ubPWquKuOiTCe0PR1Q4JZ9m66psMj6LzI2+9/ngCUxZzuc/sLRv+tz5n7AOtMGAFAAAAJKt169YKpZIQt9iFAEidzWY7ffp0x44dtVqt2LU0QLSm9ahRoxq9n8rj8Qj/4YROXPUts58e8tWTXy36aAhLCCEc6/F4nGdXz332jQ3HLlmdnLB/pa20zO0JZXlCCBPXOVHDcjqDgSYUnXRTEuPhAoN0FClyOZxuZ35mLku4kvUPdV7v3WthRobVPcDEeDyeygIICRw04c7QNV9f3vD9jlvKt+RzTPSYCX1UHlfOL0ue+c9nu8/m2z2VHbG2sjK3p7JioUC+sizO4/F4eDfLVd7izM7MYwnv2ju3V9zcKzvnL1/Idnq0SvrvvVc+GRPR5babWymUgWHteo26597hCaTw1w+enf/+zyezy92VR15dXlrq9qg8HE8IpevR9yaVx+MhJGT0U0+uOff29sWPbnuNUIwh+R9vfbd8ZHbtr92Sm2vmCB2akGBkPR66XVI7hqQRnmM9Hk/VCdsBgyaNCv/u870/bPrLs3mvnYqYNHGgxuNxW47UXhVV5ZAwbNXj4/ZceTddeVm5Lp64/1wytO2SKztiMtMznJ5wXKit2VGr1WKXIFHFxcXbt28fNmxYSEhI0++dpunIyEhMr5IzZEDmEACZQwBA9AzY7fZjx47FxcWhaS0uutWkOTPe+2X5xq3UlZ7O/O9efPbzQ2zClNc/mNYx4LfFU1/Z7SbE28anFEoFRUjl8vJK1dXHh4loFcmQMsOd/3n/ka6Va06wHl2SrsaiX9pBE0dGfPNpzrp5/3EVckzcXSm9NcR9ePkzS7dkGm59etUzQyILvn3y8c/SaxZNKZRKQhylljKOkIq081nslZ1HhzPkoqrvM6uev7UyV5xbE9eq9isiKns88enXk3Xe39lzHz3/f2tOKLs98N4L4+LdP79w79u/kyrnNiiFUln5MqiQgfM3/vWM+cK586c3L3ly6Z5vP9jwzKh+tb/2IHVGKE3MJbm5FYQoubzsXK72eQYBAyaPi/ly5d6PnjWfs9Otp0/pH0AIe77+qqoeEq7MWsYRwqafu+ghREkIoUOjIlQUIR0fXPHKqDChet7NRCW27FiD3LAsW1JSwrI1FmZoEhRFGY1GUXYNEoEMyBwC4Cepqalul4uopX5RKwQAkAHfSbYNwmb/ti0n5o5erRgu75e3Xv30BNPzgfmP3xpe3/kS74ho76+8uvujz9z5+SM/FvOEJoTneU7oBSaEdxT++dOPvzt4wlQddVx1unO1Yc08z3SeMr3Xhy8e3v3pypjJ/SM85oy/Duxm79t+Ww/+ymznK3fW9EsZE/3Fh5kXLhKi6HDXhO5KnnezHEcIIRTPll3a98OuDLbmLnhekdg5SUUdPv/5vH9VtD2/5odijlCEEJ6KGXvfsDd+23L0q3d/IEPb0CWXzx7enTvsq629+av2fuUkQrVFuvnKKd0U4ey5v23YetpDiEpYk7zq3nlCSOmah4Z97BnQq0O0vuJUjo2nFAaDVlHXa793zLhOy8+c3Pp/M1+8dEvJpvf/chOirm2FcFWviRParnrrj0PHiSJh3KQeKp7n66mK//tV0PGdkwOp9IJ1L83W/li46ctLLCEKQniiH3ZfSszPX5z5YcXX2lHJmtKs88f2/NVp2b4BiZi5C9A4OI7LyMiIi4tDl4VsIQMyhwD4ifBNR61WBwYGil1LfRAAQAZ8J9UD5PljxUOvH3RRhLdseG76+5ciovP/N+2xbwrqbTBVXxCJ53mehN317IOdVBQhhOcJTyImLXhpfCfj5bX/fm7J3ohB/fQ0uaplfdXVnnhS7emYhEc++37RPbcoT3795qI3Plp3uDh25JjueqqWNZlUvSaMbcMQQogiafyEmxie5xW9Zy9+9NZY9+F3//XC53ndb++kqLZDnvA8T7W+55UFI9vrLUfXb77cddbdycrKO1FRk1esffvhwaaMde++tmTlt3sygweN7x9B19x39cPB8zxPd7j/1TnD26lPffb8nPdOdRzaU1X9BV75hVfFdkpw/7n5s7dfW/T+7tK2tz/yzqspoaSu1850mv3h8nt7hhbuXfXBZsfwyX3VNXcuUHSZMCFRQQhRJqVM7KKovypS5f0gwWP+vXBKF5Pjr83r/mx1//29VZW3EMPQ13746OlRrQu3vb948btfbz+n6DlhSDxT295B6m7sIwP8hed5l8uFN0jOkAGZQwD8KjAwwGAwiF1FfRAAQAZ8RzkcDu8vVQ9ZXT/7eLf6fz58+PCIESPqe4ec66Z1+HFa6scjXRumJ60YcOSnh3XfTe788/2pH4+s++J/JSUldd7mZzRNnzt3LiEhQYZ7B2gUQUFB69evHzp0aLXtO3bsGDdunNNZ95XmGkNpaWmrVq1ycnL0er1fd3QdzGbz2rVrU1JSwsLCmn7vLMumpqYmJCQwTO2TT6DFQwZkDgHwk9WrV3++fltsqP7dZa9L+dLWCACIngFxvwj5SK1Wr1+/XqoDwqmgIDrrspmzn9p6oM3QBeEUcalVvMfTUK91U9VXy65F7HlDvx8AAABAs1NWVlZUVBQVFSV2IQDQCKTatFb1/cfE3JQBPd525HZ85dd2DHGdOHg8KumFeutF01qUvQNAC0bTdExMDKZXyRkyIHMIgJ/ExcUplEpCPGIX0gAEAJAB30m1aU20A5cc/C3l14uq5L7do2nCO/RDX327c2K9wxDcbtGuDcgwjMfjEasAcfcOAC0YRVE6na7h+0HLhQzIHALgJyqVqvKCNNKGAIDoGWAYJjg4uFlMSZBs05oQlyXz3NHtx9d//z9PZXes8lLrHg92r7vk/fv3N1VxtcvLy5Pt3gFu0Lhx48QuQaICAwN79Ogh1hKyLMump6e3a9euWfxJA39ABmQOAfCTvLw81uMhSrHraAgCAKJnICQkZMqUKaLs+lpJtWnN538/Y8AzlwZOHtIhWnXllJ7CFFDv2b2JEyc2QWkA4A8ej9QHxYlFq9X26tVLxAI4jhNx7yAFyIDMIQD+UFpaynEcRdHS77tGAAAZ8JFUm9auXzefuuuLX98erBG7EgAAAAAAPzAaDZGRkWJXAQCNQ6rz0Zmo6AgKq3IBAAAAAACA9Em115pJuGtoxnMPvln6yG1tDcrKgTK0oXVyrF7qo2YAAFoOmqbj4+OxLqicIQMyhwD4lbW01Gw2S/lqvQgAIAO+k2rT2rVv1Ypfzzl+fXj7kr83asb+79yHY9TiVQUAID8KhVT/UkBTQQZkDgHwh5CQEJphOJaT/lIjCACIm4GysrJjx4716NEjKChIxDJ8IdXTD+oxH2bkVZeBdjUAyE5hYeFHH31UWFgoyt45jktNTcX6JXKGDMgcAuAnoaGhzWLNbQQARM+Aw+E4e/asw+EQqwDfSbVpDQAAhBBCeJ53u908j9UnAABaDpvNxqO9CtCySHmAhzvnwJcffbvnVJ5DE9lp0JRZ9/RvJfmL/wEAAAAANCA7O9vj8RAGawgBtByS7bVm0z9K6TlmyWFX9E3db4p2//r6mF4pH6WzYpcFAAAAANAodDptcHCw2FUAQOOQaq+1+7f3X788fcvRxX20wob59z3ff8YHv937el/0XAMANBWaphMSErAuqJwhAzKHAPiVUqnUaDRiV1EfBACQAd9J9RhxBfllyb27ar0btF17J5fmFWBOCgBA05L+6rXgb8iAzCEA/uOocJSXl4tdRQMQAEAGfCTVprWyc4+EfSve+rVYGALOFv/61op9CT1vQpc1AEAT4jju4sWLWBtWzpABmUMA/EShUFAUVeFwlJWViV1LfRAAQAZ8J9UB4XS7h5bP3zvh9phlrdpHB1Rkp+UEjXh93YNtpXoqAAAAAADAR23btlUolYS4xS4EQOqUSmVMTIxS2Qy6WKXatCYksOtjP5yZfPa3I2fyHZqI5F69k0KbwfEEAGhkOp1u0KBBOp1O7EIAAAAAmprRaBw9erTYVfhEYk1rvuTC8Yue2Js6hJZfOJ5ezBGiCYttQwgpzzhxNIMOadetbTAuUgAAchIQEJCcnCxiAVi5BJABmUMA/OHChQset5uoxK7DBwgAIAM+kljT2nXojalPFb+w9+spv78x9bHNzqtvVY9acfK9kWpxSgMAkCGGYTp06CB2FSAmZEDmEAA/8Xg8PM8rVSqJrxCOAAAy4DuJNa3VI987N5IQQsjI985lvCdyNQAAcsfzvM1m02q1FIUhQzKFDMgcAuBXOq3Ur2uNAAAy4Dupdu6z6du+OZhfdSE6LvOXz39OZ0WrCABAhjiOy8rKwrqgcoYMyBwC4Fcsy0n8skYIACADvpNq09pz/JOF6y9WbUh7Tn/94ifHJf3hAwDQ+DiOc7lc+JMGANDylJaWms1msasAkDSLxbJp0yaLxSJ2IQ2T2IBwIqxkll7sSC+uyDv7+1EFI2zl7GkbjyrCpkn1VAAAgJ8UFRWtXbs2JSUlLCxM7FoAAKBxxMTEKBQKQjAgE6ABbrc7KyvL7W4GV6qTXtPadXDJ5Ic3ORwlBa4/UnYJ19uiKEZj6nDHS+/1x/W3AACaEEVRKpUK06vkDBmQOQTATwIDAymaln7TGgEAZMB30mtaq0d9kJZF3Ifemps2asn0BEbsegAAZIym6bZt24pdBYgJGZA5BMBPzGYzy7IS/CZeDQIAyIDvpDrAWtn3qZdvObFgUt+ESKPeGJnQd9K/15yzi10VAIDM8DxvsVh4nhe7EBANMiBzCICflJSUcKzUu6wJAgDIwLWQatOaL1z/6OgXz3aZvXLTvoN7flh2X9Tu2Xc8tqEI7ykAQBPiOC4vLw+LqMkZMiBzCIBfGYONkZGRYldRHwQAkAHfSXUYiuvwxtN3rdr/Yn81IYSQzl373qK52H3dIdddo9UilwYAAAAAcOMoQjCFFaDFkGqvNW0KC6Gu7qLmqYioCEy9BgAAAIAWoaysvKioSOwqAKBxSLXXWpF014CTz0x7afbDwztFKMsyDn+37CPF9KWas3/9RQihDa2TY/U4xwcA4GcURWm1WnSqyBkyIHMIgJ8YDAaapj0ej8vlEruW+iAAIHoGNBpNUlKSRqMRqwDfSbVp7dr/6UcnMhwn/rV/xd8bz0wd9hohhBDN2P+d+3AMRoYDgAwYDIZRo0YZDAZR9k7TdGxsrCi7BolABmQOAfCTiIgIRqEgROqX6kUAQPQMBAUFDR48WMQCfCfVprV6zIcZeR+KXQUAgOhUKpWIf9I4jisqKjKZTDQt1QlE4GfIgMwhAH7icrl4nieS7wxGAAAZ8B0OEAAA1Inn+aKiIlxyQ86QAZlDAPwkIyPD45Z6lzVBAAAZuBZS7bWuuPTLl6tWb//9QqFTHRrfbejUB6bfHhcgdlUAAAAAAI0kMDBQrPk+ANDopNhrzZu3PXvrzWMX7i2P7Drw9oFdI237Ft1184B//WzGyRIAAAAAaCHUalVgYKDYVQBA45Bgr3XZthce+j7ujaP7ZiZ6u6kXn/t4+h0Pv3DHnx8ODxKzNgCApubxeEpLS/V6vUIhwic2RVEGgwFrw8oZMiBzCIBfOZ1Om82m1WrFLqROCACInoHi4uLt27cPGzYsJCRErBp8JL1ea9fRjdujZ792X2LV4d8Bife9Njt6x49Hm8GcFACAxlRSUvLdd9+VlJSIsneapqOiorByiZwhAzKHAPgJTdOEouz2itLSUrFrqQ8CAKJngGXZkpISlmXFKsB30vt3wpeV2sIiw5lqm5nwyDCbtYwTpSYAAJniOC43N5fj8OErX8iAzCEAftK+fXulUil2FQ1DAAAZ8J30mtaK9kltjm//peTqedV8yS/bj7dOai/BAewAAC0Xz/NWqxXrgsoZMiBzCIDMIQCADPhOek1rJun+J3tuf/iuOV8fza3gCSF8Re7Rr+fc9fD2nk/en1S9MxsAAAAAoJm5fPlys7j4FgD4TnpNa0JHT/tk03/its/uG63XGoINWn1039nb2iz48eNp0RIsFwAAAADgmjgcDp7nFQqFSqUSuxYAaBySHGBN6bs/+vnx+5ecPnbiQpFLZYrv2qNTZABWJgQAaGoURZlMJqwNK2fIgMwhAH4VFKQzmUxiV1EfBACQAd+J1rTevHnzL7/8ctttt9V1ByogstOAyE5NWRMAAFyNpumwsDCxqwAxIQMyhwD4Fc/zPM9LudGCAAAy4DvRRlj369fvqaee2r17t1gFAABAgziOy8zMxLqgcoYMyBwC4FcWizUvL0/sKuqDAIDoGQgMDOzRo0dgYKBYBfhOzMnLCxcufOqpp06ePCliDQAAEhccHDx58uTg4GBR9s7zvM1mw7qgcoYMyBwC4CetWrViFJKcmHk1BABEz4BWq+3Vq5dWqxWrAN+J2bTu37//q6++Om3atHbt2s2ZM+fw4cMiFgMAIE0KhSIkJETRHL6BAQCAj3Q6HU1jfV6AFkXkf9L9+/fft2/fmjVrdDrdP//5z6ioqCeeeGLXrl3iVgUAAAAA4D8lJSUcy4pdBQA0JkmcLYuPj//nP/+5bdu23bt3x8XFvfTSS0ajccaMGZs2bRK7NAAAWaNpOjIyEl0rcoYMyBwC4Cdms5ltDk1rBACQAd9J6xhFREQ88MAD69evP3nyZM+ePd977z2VSjVlypRvv/3W7XaLXR0AgOxQFGU0GqW8ei34GzIgcwiAX+n1+vDwcLGrqA8CAMiA76TVtPbS6/XTpk1bvXp1dnb28OHDv//+e61WO3r06E8++cRqtYpdHQBA03G5XJmZmS6XS5S9cxx34cIFrA0rZ8iAzCEAfsUwNMMwYldRHwQARM9AYWHhRx99VFhYKFYBvpNo09pLqVROmDBh1apVBQUF06dP37VrV+vWrW+//fb33nsvNzdX7OoAAPzOarVu3rxZrLOKPM+7XC6sDStnyIDMIQB+VV5uKykpEbuK+iAAIHoGeJ53u93NIoRSb1pXNXz48HfeeefSpUtPPfXUiRMnunfv3qdPn9dffz09PV3s0gAAAAAAfCWsEO52ux0Oh9i1AEDjaE5Na6+BAwcuWbLkzJkzr7zySlZW1h133NGxY8fly5eLXRcAAAAAQMOay3WtAcB3zbJp7dWzZ8///ve/x44dmzVrFiaBAAA0OpqmY2JisC6onCEDMocA+InH4yHNYoArAiB7yIDvWsIxSktLO3XqlMViEbsQAICWhqIonU6HdUHlDBmQOQTATy5cuNAsLn+DAAAy4Ltm3LTOzc195513Bg8ePHXq1Pbt23s8HrErAgBoaViWPX/+fLO4+Cr4CTIgcwiAXwUEaIKCgsSuoj4IACADvmt+czysVuvatWvXrFmTkZExceLE5cuXd+3alRCiUqnELg0AxHfp0qXFixfv2bMnPz8/KipqypQpc+bMwefDjcB0G0AGZA4B8B+NRqPT6cSuogEIACADPmo2TWu32y20qI8ePZqSkvLiiy/27dtX7KIAQHLOnz/Pcdzbb7/dtm3b06dPP/HEE3a7feHChWLXBQAAUJ3L5XY4HBqNRuxCAKRLp9MNGjRI+iehSLNoWm/cuHHt2rVbt25NSUl59NFHhwwZInZFACBdw4YNGzZsmPBzfHx8amrqqlWrmnXT2mQyzZw5U4GFZAEAWhybzVZSUhIVFSV2IQDSFRAQkJycLHYVPpHud7WdO3euWbNm7dq1I0aMSElJ+eyzz8SuCACaH6vVGhwcLHYVN4SmaREHtNM0HR8fj3VB5QwZkDkEwE/at2+vVP5CiNSXCkIAABnwneSa1ocOHRJa1D179pw4ceLbb7+tVCrFLgoAmqX09PSVK1e++uqrtd7qdDqdTqfwc1lZGSGEZVlhlQ6Komia5jiOv3JllJpbaJqmKKrmlqrrfAh/h6rOUKq5hWEYnue9W4Qd1dxSfzH+K4+maYZhWJYVlgaVWnkSP3otpjzhySmKkmZ5Ej96LaA8bwCkWZ7Ej1495RGKEnbtrVBS5XmPXtUASLC8eopBeY1SHs/zwp0VCoUEyxP36DEMQ6qQStP6xIkTwlTquLi4iRMnLliwwGAwiF0UAEjRggUL3nzzzVpvOnbsWGJiovBzTk7O+PHjx48fP2PGjFrvvHTp0kWLFlXdkp6eLszkMRgMUVFR+fn5VqtVuMlkMoWFhWVnZ9tsNmFLZGSk0WjMyMhwuVzClpiYGJ1Ol56e7v0Qj4+PVygUqamp3l0kJCR4PJ6LFy8Kv9I03aFDB5vNlpWVJWxRqVRt27a1Wq15eXnCFq1WGxsbW1RUVFRUJGxpyvLatWuXlpbmvYPUypP40Wsx5RUXF0u5PIkfvRZQnt1ul3J5Ej96dZV38uRJt8tF1FRWVpZweldS5XmP3oULFyR49CT+5qI8+ZSXlJREqqAcDof3l7/PotX9s493q//nw4cP9+nTR9hy4MCBp556SqvVpqSkTJw48bpnm6hUqsDAwOt7LACIzuPxrF+/fujQodW279ixY9y4cZhfEIkAACAASURBVN7uZUKI2Wz2ftGvJj4+Xhg7nZubO2LEiF69eq1cuZKuYwhTtV7rxMTEzMxMvV5PZHCS1ffyCCGpqalt27YVzstKrTyJH72WUZ7b7U5PTxcyIMHyJH70WkB5VQMgwfIkfvTqKe/bb7/9YsN2UyDzxmuvhoaGSq087xaXy3XhwgVvAKRWnjTf3BZWHsuyQgZUKpUo5XEcx3FctUVnJHL0vL3WarV6/fr1IvdaHzhwYO7cuatXr27fvr24lQBAcxEWFhYWFlbPHXJyckaOHHnzzTd/8MEHdN1Tg9RqtVqtrrqFYZiqA3tqPtaXLdWGBvmyhaKoerZUVFRkZGTExcUFBARcazE3Xp73L0fVjTULvr5imuDoobzGKq9qBiRY3o1vQXn1b6k/AKKXV/8WaZZHURQhRK/XR0RESLC8aruuGgAJlndNW1De9ZXnPcPe9OUVFxevXbs2JSWl5tc/SR09IvqA8Hnz5i1atAjtagBoLDk5OSNGjIiNjV24cGFhYaGwseYXl2akvLx8z549oaGh1ZrWAAAAACAdYjatDxw4sHDhwgEDBohYAwC0MLt27UpPT09PT+/QoYN3Y3l5uYglNWs0TSckJNTT+Q8tHjIgcwiAX1ks1ry8vMjISLELqRMCAMiA78Q8RqNHj0a7GgAa1z333FNeg9hFNW8ej9SvDQP+hgzIHALgD+Hh4cLU0GpLGkkQAgDIgI+kffqhInXDy/cP65HYJrZNYo9h97+yIbVC7JIAAOSE47iLFy9WW9gMZAUZkDkEwE+MRiNd21xNqUEAABnwnVQuvlULvmjzM3e/art7zrInO4Ypyy8fWbtszoQc5Z73RoZQYtcGAAAAAHC9rFYrx3GkGTSuAcBXEm5au45uPTfyna3P9RZW8E3u3Ku7+tKgTUdcI4erG3goAAAAAIBk5efnsx4PYdBfBNBySHhAOB0SGkxdPfuEp8IjwnB6DwCgCWHlEkAGZA4B8B99UJDJZBK7igYgAIAM+EjCvdaKhJF9Tr3w0JKH7r89KVxZfvnouhVfKKa8rEk9c4YQQutjOkQH4UwfALR0FEUplUrhCqhNj2GYqmutgwwhAzKHAPgVo2BUKpXYVdQHAQDRM2AwGEaNGmUwGESswUcSblq7Dn/9xV+XnX/9+/BHf288/8D4twghhKhHLD/y1p0YGQ4ALV1oaOisWbPE2jvP8zabTavVitW2B9EhAzKHAPiV3W63Wq1SbjMgACB6BlQqVWxsrCi7vlYSblqr73zr5Lm3xK4CAEDOOI7LyspKSEhgmsNKtuAPyIDMIQB+EhAQQNG00+my2+1SblojAIAM+E56TWveknHyYkldq7vTwfFd4ow4awYAAAAAzVZsbKxCoSDELXYhANBopNe0dh1594F/bXPWcav6jjf2Lx2GceAAAAAA0GzxPE94nqC7CKAFkV7TWj1s6ZGTYhcBAACEEEJRlEqlwhQ7OUMGZA4B8JPU1FS3203UUj+wCACIngGPx1NaWqrX6xUK6TVdr4aF1AEAJM1msx05csRms4myd5qm27Zti6tuyBkyIHMIgF9pNGqtVit2FfVBAED0DJSUlHz33XclJSViFeA7af874cvPblz23Ky7U2a8f8JDbMe/+9/OrLpmYQMAtEh2u/3YsWN2u12UvfM8b7FYeJ4XZe8gBciAzCEAfhUQEKDX68Wuoj4IACADvpNy09p54s3x4xf/Gdw7mTqbbuWIJjDzu9fXXmDFrgsAQDY4jsvLy+M4nNWUL2RA5hAAv/K4PS6XS+wq6oMAADLgOwk3rV2/fvFl4DPfrpp//23xARQhhGmTHJ91Js0jdmEAAAAAADesrLy8qKhI7CoAoHFIuGnNV9hdIaHBVSp0ltsplRLLKAAAAABAcxYXF6dQKsWuAgAak4Sb1qqbb+16ZOXyQ4UsTwjhOctv76462WvwzfgUAgBoKhRFabVarA0rZ8iAzCEAftJclt1GAAAZ8J2EVzCnIiYtfvXItKk3rVKqHPzW7t/aw6a8u3qcCW8rAEBToWk6NjZW7CpATMiAzCEAfpKbm8t6PETyPUYIACADvpNw05oQRexdb+6+/cnjR07l2NWRnXp2jzcwYtcEACAnHMcVFRWZTCZceUW2kAGZQwD8pKysjOM4iqYlfmARAEAGfCfppjUhhNBBbXrc3qaH2GUAAIiEYZjg4GCGEefMIs/zRUVFISEhouwdpAAZkDkEwK+MBkNERITYVdQHAQDRMxAcHDx58mSJX6ZOIMmmtWvnvNvn7az1SgSqIYt2LRqiauqKAADEEhISMmXKFLGrAAAAABCBQqFoLid3JNm0ViSNf3ZuL+HaaZ4zX76wMeK5OcNCKUIIoWOSJFkyAAAAAMA1sZaWFhQUhIeHi10IADQCSbZT6ehbxk24RfjZtffAqwfbDx8/oQ0G9wMANDWKogwGA9YFlTNkQOYQAD8xmUwMw3Asx7Ks2LXUBwEAZMB3kmxaAwCANNA0HRUVJXYVICZkQOYQAD8xmUw0wxDCiV1IAxAAQAZ8h65gAACoE8dxubm5HCf1L3/gP8iAzCEAfmKz2fjmcFQRABA9Ay6XKzMz0+WqdSEuaZFkrzWXdWjdwUxhdAx7Nt1mLt/8/XfCXGsmtt/4vjE4IQAAslFWVnbs2LEePXoEBQU1/d55nrdarZgHKGfIgMwhAH6SnZ3t8XgII/VBtggAiJ4Bq9W6efPmlJSUsLAwsWrwkSSb1p7zm99ZttN55dcwJv+Lt04KP6uHhI7pG4MVwgFANhwOx9mzZzt16iRK0xoAAPxHp9M1l6WPAaBBkmxaq25/ZfehV8SuAgAAAADAf5RKhVqtFrsKAGgcGFoNAAB1oijKZDJhXVA5QwZkDgHwq4qKirKyMrGrqA8CAMiA79C0BgCAOtE0HRYWRtP4YyFfyIDMIQB+olQqKYpyOJzl5eVi11IfBACQAd/hGAEAQJ04jsvMzMTasHKGDMgcAuAn8fHxCqVS7CoahgAAMuA7NK0BAKBOPM/bbDae58UuBESDDMgcAiBzCAAgA76T5DJmtdm5iSg5WukhSg+t9FAKF93pfmfDDwMAAAAAkJj09HS3201w1RuAFqR5NK1t5USjIzGteJojNMeFRvOXdjSDITQAADdOqVTGxMQom8O4QQAA8BHLsoTnVSp1QECA2LUASJrJZJo5c6ZC0Qzarc2gRELImdN8YkcSWnmhcv7CdmVUb7e4JQEANA2j0Th69Gix9k7TdGRkJBYvkTNkQOYQAD/heZ5wnFqtNhqNYtdSHwQARM8ATdMqVfMY4CG5pnWqld1x0RHAMSqWZtyUnqaG9lCkXyBlhRTjoZQeSumhKy4ybYehaQ0A4HcURUn8ax/4GzIgcwiAn2RlZVmL8hhnqdlsDgsLE7ucOiEAgAz4TlqnoHbmO48UuW4KVfE8z/O8SkF1jGN++JnlCD/4TtL5Fl4XTHpO9hhacZhIDwDQBDiOu3DhAtYFlTNkQOYQAD+Jjo4OMISqAwM9Ho/YtdQHAQBkwHcSalrnOthUm8fl4RiGD9fRLEdahZBDf7G39aVVCursaer33bTBRDKOMoY2XOllCVUOANBS8TzvcrmwLqicIQMyhwD4A8dxOp1Oq9XqgoICAwPFLqc+CAAgA76TUAP1h3x77xCVUUP3jVLllXERekqrpiYOZg4cY1MmUvZSniN86y5c0QU6vCNn/pMRu14AAAAAgGt26dKl48ePV3h4h4cvLCxEowWgHhUVFWfOnKmoqBC7kIZJqGnt5Pjjxa5hrTSEEJqmzGU8IUShoFQMTQjp0Y8kdiVnD9GEkIBQzlmIpjUAyILFYtm0aZPFYhG7EAAAaAQlJSXnzp3LzjcHxyRUsMz27dtPnDiB0bYAdSkvL9+zZ095ebnYhTRMQk1rSuwCAAAkyO12Z2Vlud3irN1I03RMTAzWhpUzZEDmEIDG5XK5Tp06ZTabrRWe4KjWCkN4Zmb25cuXL126JHZptUMAABnwnYSOkZqmuoWotuc4CCHhWorjCSHE4+HdHE8IOXaQnDtBkvpyPCH2QkoTxopbLQCAHFAUpdPpKAonP+ULGZA5BKBxFRQUFBYW2mw2B8cEmcKDwlplFhRSFHX+/PmSkhKxq6sFAgDIgO8k1LQeHxH4a7HL4uAP57mFudY2J//9L55+3em1a/jAIEIR6vJJOrQtW3CKCeuMYTMAAH7Hsuz58+dZFmcz5QsZkDkEoHG5XK7g4ODsnFyFIYJhFMbw6DInV15e7nQ6T58+7XK5xC6wOgQAkAHfSahpHaVhErQKlYLysIShqSIbySkm/W5S/HKIc3tIYifSYzBnLSJxPbnSy7S+Dd5dAICmgBmAgAzIHALQKGw224ULF/Ly8jiOu5RToA9vRQjRaIM4lS4nJ8doNFqtVrHm/tQPAQBkwEcSaloTQoZEqHuZVH+aK8/YuTz86Qx2/B00Rcien8hfv1HlJeTodwprjrTKBgAAAACoC8dxZ86cOXnyJMdxeXl5ZS7OENZKuCnA1OrC5Wyapg0GQ0FBAVYLB2i+FGIXUF2CgUm4WVttY/t2pE0bEhrOE8ITwl3cTpVl0UExOH0CAAAAAFJ36dKlnJwcg8FAUVROTg6n0Wu0QcJNhvBo85/nrVar0WjMzMzMy8vr0qWLVlv9yzAASF/z6P5NSibnThNCSNZZOvs0TQdwub8pxS4KAKDlo2k6Pj4e64LKGTIgcwjAjSspKTl//rxKpVKpVDzPp1/OCgiJ8t4aZAp3cEx2djYhhKKo/Pz8M2fOSGf8LQIAomeAoiilUtks1lET89/Jjz/+eODAAV/uqdURRznZu5HOPE1y/6Itl2l3mb+rAwCQBI1Gk5SUpNFoxCpAoZDc+CZoYsiAzCEAN+jixYsOh0On0xFCLBZLodVuDI/23sowCoUhIjMrmxAiDAvPycm5fPmyaOXWgACAuBkIDQ2dNWtWaGioiDX4SMym9YABA+bNm7d//35f7jxkNBk4lus7ges52dP1H+5O9zn9XR4AgBQEBQUNHjw4KChIlL1zHJeamiqd/hNoesiAzCEANy4oKCg4OFjoc7NarU6WaI2mqncINJrMJVbhZ5VKRdN0Tk6OCIXWBgEAZMB3Io/uWLRo0bx589LS0sQtAwAAAADAHyiKYhjG+ytPCEWoaneounaZQqFQqVRNVR0ANBqRm9b9+/dftGjR1KlTBw8e/M477+Tm5opbDwAAAABAYxEmWl/TNYGDgoKSk5P9VxIA+In4cycGDBhw5MiREydOrF27dsiQIXFxcRMnTkxJSTEYDGKXBgAAAABw/TiOu6Z2dTPFcVxFRYXL5WJZlud5mqaVSmVAQIBSWX3h4dLSUqfTaTAYfO+ZN5vNNE2bTKZ67lNUVMRxXFhY2HW+AEIIISzLFhcXq9VqvV5f/z0dDofT6fR4PBzHURSlUCjUarVGo2kWS201MeGoCj+HhIRUHcEhsNvtNpuNEKJUKo1GY1PWZrPZ7HZ7UFBQY61oI5Xl/rp27frSSy+dPn36xRdfPHXqVNeuXSdOnPjNN9+43W6xSwMAkC+aphMSErA2rJwhAzKHADS90tLS06dPi11FJV8C4HK5iouL7XY7y7JKpVKtVlMU5XQ6LRZLeXl5k5VaFcuyZrPZYrFc06NcLhchRK1W13MfjuMsFktZWZnL5WIYRq1WKxQKt9tdXl5eUlIihdMoDofDbDYLjdVG0YgfAk5nLatlORyO+h9ls9mOHDlS6yuy2Wxms7nBZ2gykvug7Nu37xtvvJGRkfHoo4/u2bMnOjr6vvvu27hxo9h1AQCIo7i4+Ntvv/We8W16Ho9HrF2DRCADMocANDGe5yXVt1R/ANxut9Vq5Xk+MDDQZDIZDAa9Xh8SEmIwGGiarqioqNa61mq1wcHBNXuzb5DRaAwODr7BJxEafvV0p/M8b7Va3W63UqkMCQkxGo16vd5oNJpMJpVKxbKsxWJpkct93fiHAE3TFEXVbAN7PB6WZetfgdxutx87dsxut99gDU1Ack1rryFDhrz//vsFBQUpKSlr164NDw9/9NFHd+7cKXZdAABNimVZEU+Ecxx38eLFFvlFAXyEDMgcAuAP6cf3px7Z4/1fYXaG2BXVqcEAlJWVEUICAwO1Wm3V4dAqlUqY3VlRUVH1TAHDMAqFotEHTgtPeyPPIJzRqP/6yXa73ePxMAxjMBiqDmymaVqv1yuVSo7jGrG7WCIa60NArVazLFutlS40tusfKdCMiD/XukFjx44dO3as2+1eu3bt+++/P2vWrJSUlIkTJ/bt21fs0gAAAAAA6qTX6+Pi4qzWymtrhYaG9k5qzVZrpYTpQ+udSCxZwuRqmqYDAwNr3qpQKAICAioqKioqKrzd1DXnWnMcZ7fbhaeiKIqmaeGB9fRsezweq9XKcZx3lmzVudbCBFpCiNvtNpvNwkManEHd4GhwnueFdmC1kwgCiqJ0Ol1JSYnD4dBqtcLw6bqm8tacGe5wOFwul9CFKxw6jUYTEBBQ9VHeZ1MoFDabze128zyvVCq1Wq33WFksFuFEht1u93bzCgU4HI6ysjLhJEjVpxUe4p0FLUyNViqVBoPBZrMJU8qF4yPU43Q6hVMMFEVpNJpaj0at1Gq1MEe96kkQp9MpzMyv9SFut9tutwsD+61Wq0ajCQwM9J7UEA4jIaSsrEw4xUMIEaIlvFnCIfXOh9doNI01p7ouzaBpLVAqlXfffffdd99ttVrXrl378ssvZ2RkCAuede3aVezqJMZ9eE6XmdRnJxff0mzeXyCEEOI+9NxNDyi+OLGoF5W29NZbT//70scjcfUNAACAZktYzau0tFT4VafTDR48WNSKGpMwglqYXF3rHdRqtbC8WV3PwPN8SUkJx3E0TQuNbY7jnE4nwzD1NLeEIeh1rYUmXL3M5XJRFOW9Q4ND0BscDe5tpNV1H4VCwTAMy7Jut/uaumF5ni8rKxOafwqFguM4YfK22+2ueTrA7XaXlZUxDKNSqTwej9vttlgswcHBQntVeJlut7tqN/51TJPmed5isQiT54U3t7S0lGEYj8djs9mUSqVKpXK73RUVFRzHNbjqm0C4Zrtw6kHY4nK5OI6r9bwMIcQ7m0CoXxhP7nQ6jUaj8NLUarVwRkapVHpfo/ADy7Ll5eU0TQtBEg6p2+1mWbbamYXGJd0B4XUxGAwzZ87csmXLzp07w8LCnnzyyV69eq1YsWLhwoVil+ZXnHnv0ml940MC1IEhMR0HTHptj5Vv+FG+c26cHtr/jfTrHephO/3NnHHdWxsDNLrIjkMfef/Xor+rYwsOvPvonV1ijAHqQFPbPpPmr/6rrFFr9686jwx79rXeUTO31PnH4obQYUP+tfCeTgpC3Aee6dDx+d8wzw1Eg+WLABmQOQTgRthstry8vGsaTKvVatu3b++/kq5VPQHwdrHWdQfhJp7n65rW5HQ6OY5TqVTCPG2DwRAcHGwymepqmrpcLqEPs541xtVqtU6nE/auv6JaD3Ctz8wwTM31q72Eztv6R7MLr/c6Zibr9XqTyVR18rZCoXA6nTXPSghN05CQEGFOu/C6vB3UWq1W6JgVeukF13GZdKFT2vumCC+5rKzMbrcbjUaj0Si8U8J6db7PWdNoNBzHeV+U99RMzXsKJxdomhaOiXCIdDqdcBpCuI9OpxMeq9FovC9WeAtomjYYDFUPaUhICE3TwmJ713o0fNeMPyujoqJmz569e/fu1atXp6WltezPfb5k3ZOT3rSkfH7CXFaUfvDr+cNjVdJpnXpSV0667akjnf+zNa3EmnNoxejCN0aOevmonRBCeMvOZ28f9VbB7a/9fNZckn968ytDyzeuP4mGYgMoQ7eUGbe3acmhhuaBYZgOHTrU81UDWjxkQOYQgBvkcDhKSkp4/hq+tikUCr92rF2T+gMgnDKo50s4RVFCq6yukwvC9mpdysKY8Jp3djgcVquVoiij0XgdzcV6CIOr6+9qFt7E+lscwq3XOi2ZoqhqPf80TQsZqNm0ViqVVbt5hZ/9se6dTqcTSmIYJjExkWEYjuOqDtRnGEZoxvu+d+EICy1qnueFweG1vtfCyQKdTlc1GwEBAd6++vp35B0E4cUwjHCsal2lvLG0hG/u7du3Hzly5IIFC8QuxI/YjD9O2vvc88itsTpVQHDrm0c+MK2vkSKEEM689/WpvVoH64LCEu+c++Pl6m1WvvTYygdubRcapAuJH/jYV2eFdfnY/F8WT+3dJiQoKCSma8qKk1mf3/fYOsvvCwe1jYtLfGzzVYmzH35zUt+ECGNQUGiHIU9+n16jl7Z066v/PTLgzW9fvqtTqEatjx/81GcfTS9+88WvczjCnls5/3/046u/mjOyU6QuMCgieejDb3/9Qj8lIa4LPzw7PDFcHxQc22vaW4dKeEIIcf4wNXjQ3BXP3N4uRGdsP2LhvtzTnz3Yp41RF5I88b0TFYQQ9q9XesamzJs/8bYBvW9K7DZx6SGL8NeKy9vx0vguUQadIeqmcf/dlstW3rn15P976e7Bfbolxncau+RQZV9/bYeltjvzeXUdGffB/0x7/YT5+xkd4uLa37nsDFtrAVer9c3iy469O/XmSENwq85jF+670tvPpi3tFzVzi8u5+Z/TPspIWzmufVxch6mf50nnhArIBM/z5eXl1/SlEFoYZEDmEIAbR1FUcXFxYWEhTdM0TVsslsIqXC6XMErWuyUvLy87O1vsqiv5OwBCs6qiosLhcNS/l4qKirKyMpqmvYOfG5HQgm3c5vq1EuYVl5WVlZaWlpaWVlRUkCvjAqqqeRqCoqhGX2iw6tkNIQPCWYNqe/dOz/bxaYWGtNPp5Hne5XLVczqjrndEKMDHcQEul8tms3kPqdCo9uuijC2haZ2WlrZly5aXX35Z7EL8SNGuX5/gXa89svjr7cfSi/9u3HIXP7xn8hfB837JKi06+UHPfY/MWnmxalx489pHx71um7kh3WJJ+27cxXnTFh93EzZtxdTJnwU8+VOGpTT/5HfP9DG1uvezFeON3efvuZCRcW7FqKszroxPWbYrvbDUfGxp0q5HH/8s6+pAuk/s3FM+IGVU+N+n2rT9JowwHt5xsIIv3LvrVJsxE7pdtWQARVHEc+rNfzy4q9PbfxRaL299wP7GpH+uLxY+T92/rjnY5b0T5vz9j7qW/uO2Z34f9c3ZoqzN4y8ueOGbfJ4QQriCHYdN/926/9cTBxaFrvrHnJ/LCOEufzxr6pfGFw7kleUfXBDy9bSZH1/iCCGEy9/1a8j//XT4+Jn9c+hlz3xwnq3rsNR2Zy6yriOj7Pffr57rGjbpk/MZGWk/PZ1M1VVAA29Wxb4Xp75SfN9PmUWXdz1lX7vm8tUPUo96+6tZce0fXp+WkXF+9b2RjbyeJkBDOI7LysrC4sByhgzIHAJwg4KCghITE5OTk5OSkvr27du3b9/OnTsnVdGjR4++fft269bNu6Vz587CeGYpqD8ADXbS8jwvNJjrGkStUqkCAgI4jisrKyssLCwpKbHZbDWbahzHCdNujUajP8ZQ1L+YlqD+7ndBg934teJ5vrS01GKx2Gw2YTqxdyh4zdMNNZ+80ZdbJ1fazAIhAzW3X9+u1Wq10K4W1oSrdV0x70EuLCw0m82lpaVBQUGlpaXe63U3+IkkXH7carXa7XbvIRX6utG0rl1ubu4777wzePDgqVOntm/fvoVfdFE/4p1fvpqm27/8sTtvightN+zZHy64CGHTvv/0SN/nXx3fTkurom6b+8zAE+t/rtKxyRdu+mRz/BOL7utsYBQhvZ6YO86ycdM51/nvv/ij//ylU5P1DKUMSezfPbq+HAT2GH93n1idgtK0Gf3szOTf9x2/eggGV1xYEhgWetUEFjo03OQ2m61scVExMYWH1nh+9ty671Nvm7NgeCsVHdR51v/NNG3+ZpdwrQJF0j1z7knWMdqOE8d2tkSPnTM2LoAx9po4IvrsifPCmxxw+4OzOmkIoUOHPjJFv2nNASefv/XbfZ1m/3dS2wBKE5/y0j9vOvDtljyeEELphz80M0lDCB01ZFjy+ROnPXUcFrb2O/uo7gK8L7nWN8v125oNrpS5j92spxXht897cqh/1y2E5igwMLBHjx51LfIBAABSplKpEhISEhMTExMThS1t2rRJrEKYRxoSElJ1Y6tWrUSt2ldCQ6ueL+Hem+ppD+t0uuDgYGGZa4/HY7fbi4uLq43a9bZ7/dGFzrIsy7INdln7Mo9auPVaG/92u11YuU2YHhwaGhoWFlbXZbr90ZBuSkI3tbC4XdXlx+q6s1qtDgsLGzNmTFhYmPqKBoctCCuoC+ucew+pcDU4v2p+K0gLK4SvWbNGWCF8+fLlwgrh4g7haAIB7UbN+WDUHEKcuQffe2jSfQ/Edd/xcG5mrn3P/L7JlT32Hpsytof171MxXH5WbsWJpcM7fiD8I2QrKgKnWj0FOeaQ+LggH/9hVpz9ZsHcZRuO5zgohpQXlA+yeQip0q9NB5uM9szCCkL+fge4woIiymDQMcGmYFJUUMiRarOGOXN+UXBsTOVEIiYmLtqzL9/CkTBCqJCwEJoQQii1Rs2EhFb+otKo3MVunhBC6KDwsMpWBh0WYbIdN1dwhfmFypjWEXTl1tYx6sJ8M0sYQgUaDELMKaVSybpcbB2HhSOG2u5MfPtwrKuAVt5/Ylytb5a7sKAkqkdM5b20rf+fvfuOa+J8AwD+3F0GEFYIhK0sQYbiFsGNe6I4q61VW1uto2qH/qyj1bqqVq111Vm1LlRcVasoDtwLJwoICLJJWAmZd78/TtMIBEGrifJ8P/30Ey83nnvvvSPPvXfvW7fibQhU2wkEgubNmxs7CoQQQqg8Ho/HNgkaGoFJ1+121Qkh+5ywhYUFwzBlZWXsQ7zlHhW2sbEpKipSqVQlJSVWVlb/YYb5CrrgkQAAIABJREFUyr7BdUESBMG2uFY6s27orHLflrsXwDBMuYZTtoFa1wUX6y01rla8MfFWu/WqiO2ym21ANjQUFntwCYKoZt/jFbHH1NraWj91fwd7+t6k1uy41lFRUdevX4+MjJw5c2atHdea7xz61Wcd5o69l6gh3VwcbXtMv76930uPDakvP/9AOjiLLVqNPXfsc0e9y4824ZyD5GpaCdPa7qWLEgGV3AWk0zePn3C1/ZGLe1ra8+i0Fe0bnis3B7dRx3aCz/YdzR360YtnwuWX9h/LazS2pSXh0DY8cMpfB+JnNW367+WRYRjSwVEkvZ0hB7ABAG1GWibXQWxbzaySLnr6tJABMQGgTU99ZiF2MCftHe3VZ9JzaXAnAei89AylqJUDBZLKlq+8WEB7z9AGKy0ZAAL+nW4oAL2tiis7WOqzYjtpXgEN9SgAUOfnFdLulWwe8B03ZCTsKCPv+z1y9CawDtRyWAFquaorAJ/PZ4ebksvlFbte02g07AvDr+ydW39zFhYW7JBO7Ihc+l+x2TWbNf2H2XU1X7QmCMLc3Fwul5eWlrKdY5ebgX1knc/n68Ku9FXkil1wVdpBGvu89H9INyqV/kR2RLGqF6xivLHXY2ZmxvZAbuhFa4Ig2PTb0F2McireL2AYhh0jXX/iW+3AjPUetJAdOnRoxIgRrq6uZ8+eHTt2bGpq6tKlS2tbXq1N3v3j3O3nEjKLlOqStLMr1sUwDZv6cyjfQZ82OD13+v7HxVqglZLE84fj0vVOD0Lce1S3B0umbrldoAFGVZR2Jfp0opaqN+DjRhcWTNvzqIRm1NLHcTef0UAKRdbZT9LKym+ZKSsp5Tj5+oh4wBRf27jjesUO+ax7/O+HxuenDJ558EGBUlWSenbFiJEbYMSMT71IoPzGzP9Mu2ro8KXHH+TIykpzEmLWTxr280XaLyLS+8zS+THZaqb04Za5m/K6De5Y7c4w1edX/XwiS83IHmyYv0PRKzKUTzh2HxR27/efolMVjCI1+sff7oYO6mHoxeTKi8XQxgyVDJC2ItuytCc5WgCAVwdQ+cHiNo/sTR/YcCKPBlAnb//jeIUx1UihyEaSklqMyTUyCpIkvby8PuwhGFDVsA7UclgBarlXVgArKysAkMvlMplMP8NhR5+GF706G1pc9was/oJsslcxd2Wzay6Xq1QqdSMwGQobADQazSufHmdHPK7m/SMLCwsOh6PVaouKivRzVJqmi4qK2NGk9d+TZx9iVygUupnZ8ZbLrZbNwPVzafbd4FfGUyndvpebzgajUql0Ba57g/2VK/xvLwJmZmb29vYikaiKMmffgyspKSnXRzr7XIPusBrqR42iKIZh9ItULpe/jX7UyzHdC2VMTMzYsWPFYvG+ffsiIyNzc3PXrFkTHh5u7LiMg7QWC1K2TewS6Ghp4dhk5H7rcbs2jHQjgfL+8q/dw4uWdvMSWtu5Nx288EyWWv8KQjgOXHfwW6vtgwNE1kLXBr1nHEpRMEDVG/fXrqHSxeHuNjaODQYtvyKlgdti1JRGsR+52gqdRkTr3SWj6o36eUzRnJDg0A6dhm7gNW9SSf8OXL9x+08vbXpndhdPa3Mbz44/ZUTsOLG8m5AAAEIYvvT0kQmiE990qieyc2va78dz9oMGBnM4Qd/8tbbNzbFBdlZundZwJu75rZ+ourceSYe+g5w3dPMUubRbRU3csaibNQBZZ9TG7YNzZ4c4WolDZuUM3LbxM8MjV1VeLAYYKhkgxH0mfqZaHGxnax+24B7zygAqP1gWbeduH18yu03j0PZdpj5s2Klu+QfQKb8hk3smT/G2tRX33pCJCTZ6xxiGKSwsxM6BazOsA7UcVoBa7pUVgH2dlSAIuVxeUFBQVFRUXFwskUgKCwtpmjYzM6t6IDG1Wl1YWKhbsLCwkB222tAT5vrZdXFxsaHVsg2tDMNIpdLi4uKSkhJDjcA16htct3W1Ws3uIxtzQUGBSqVih9HWT0EpimJ77ZJKpUVFRYWFhRKJhKKocmkq26ovk8nYaNn/V7+pvxwul0sQBDsAOLvvbErJPhEAAIUvSCQShmFe+d6yUS4CPB7P0tKSvWchkUjY6iGVSvPz8/Xft2fvF5SVlRUWFpaUlJSUlLD3FHSZOVsIEolEJpO9dpFWH6Ffz/SLzNDnas5W9efLly+HhIRUGtClS5eioqL27dvXrFmzAQMGREZGVt1Znw6Px8NufkyAJnX7R+1nqGed2j2q3lt5+117b17LnpmLE1d3/MBfrq91NBpNdHR0p06dyk0/depURETE236Gp7i42MXFJTMz87Xf6vlQabXaxMTEevXq4ai2tRbWgVoOK0AtV80KQNM02zGVVqvV/fK3tLSsmMywYyDZ2Niw2axarWYbrtkF2TGfyjV05+XlkSQpEol0U9hkT6PR8Pl89g93QUEBTdMODg76IZWWlrIjPAGAbs5K4xGJRDVqlWXfMGdHw2ancDgcW1vbircDGIZhO6lmn283MzOzsLCQSCTlolWr1TKZjG1mZ18753A4EomEy+Xa2tqy88hkMrlcbmVlVe4V5Yr7rr82ANBfRC6Xs8/bkyTJ5/MFAgHb3m5nZ6drAS63XbYOiMVirVarm01XDiUlJRYWFlXcQGFXWO4IlsPeYdHfqG46mzY/ePDA39/fysqKy+XyeDz9J8lVKpV+i7SuarEjb7FPnrNFCgBFRUX6NcFQkb4GPp8fHR1tKu9ax8fHs69Se3h4DBgwYNasWe+gDzf0FnA8hm8+yFl17Oq9Ep8m1e0pDSFkWH5+/sGDB/v27Wtvb2/sWBBCCKFKkCQpEAh0+RWbschkMh6PVy4nL5ffcrncV7ai6SeNLIIgynWgXTFtI0nylbfL2T7JOBxOTZ92NjMz0yVjGo2GzfMVCkXFWwkEQeiXjKFoK2aVUGHHK66nRmtjWVhYlGuMLDcnRVEVCxwAbGxsKt5e0S8HQwytsFzAlc7DVg+lUpmamtq0aVM7O7uK8/B4vEofOqh0ejWL9LUZP7WOi4v7+uuvBQJBZGRkTEyMs7OzsSNCb0gQPOT7YGMHgdAHg2EY/fviCCGEkIkTCATsO7FFRUW2trYm+64+wzDm5ubVfELWEPY58KKiotLSUrYp+L8KD713jJxax8XFTZs2befOnT4+PsaNBL0vqKAfrqcZOwiEag32djt2DlybYR2o5bAC1HKvXQEsLS1JkmTvDptstsk2tr/5eng8no2NjVqt1mg0H16P+ngRqD4jp9bTp09fsGAB5tUIIWSaSJJ0d68wIhyqTbAO1HJYAWq5N6kAtaoXJEOPJX8A8CJQfcZ8PCMuLm7+/PmtW7c2YgwIIYSqQNN0Xl7eKwe9RB8wrAO1HFaAWg4rAMI6UH3GTK179eqFeTVCCJkyhmEKCgrwTe/aDOtALYcVoJbDCoCwDlSfiXYq8BxTmnDo129HD4kcuSZeA7Jbe/6IycAbJgghhBBCCCGETIopp9bK+GX9+i26K2zpTyQkF9FgZpG+55d9T7TGjgshhBBCCCGE0NvH5XLd3NzesCP3d8OEU2vVlW3bLabs3vC/Tzt4mhMAQNX198x4mKQxdmAIIfQOWVpatmvXztLS0ihbJwjCxsYG+wWtzbAO1HJYAWo5rADI6HXA1ta2V69ehkbqNikmnFozZXKVnb1QL0JlqZzgcfHURghVadCgQfXr1xeJRN7e3p999llWVpaxI3oj5ubm/v7+5ubmRtk6SZLOzs4mOyQpegewDtRyWAFqOawACOtA9ZlwGfEatQm+tm7FpXwtAwAMXXh11YY7zds3eg+eBUAIGVPbtm3//PPPW7du7dixIyUlZfjw4caO6D1G03RWVhb2C1qbYR2o5bAC1HJYARDWgeoz8rjWVSEcBy76+dqwoQ02cHkK5liT3XKHwat2Roiw1RohVKXx48ezH+rUqTNlypQhQ4ao1er34hUdE8QwTFFRkVgsNnYgyGiwDtRyWAFqOawACOtA9Zlwag3Ace+7LLbjpFvX7mfK+U6BzZp42lDGjgkh9P6QSCS7d+8OCQnBvBohhBBCCL1VJpxaM3n7J3ye8Mmu/7XoWLcpAIDi6vx+2/3/WNHPHhuuEUJVmjlz5rp16+RyeYsWLfbu3VvpPEqlUqlUsp+Li4sBoLCwUKvVAgBBECRJ0jStG8Wx4hSSJAmCqDiFXYNuCgDoP0NVcQpFUQzD6KawG9KfwjCMTCbThcrOQxAEwzAkSbK9eujCKy0tVSgU+vOw4XG5XFtbW/3wiouLNRoNu379NZuZmVlYWOgXVGlp6ZMnT9j+S/RXa2FhYW5url8yEomEYZhymwYAKysrc3NzXVnRNF1YWFhuHnYpKysrDoejKyuNRiOVSvXD0wVsa2tLUZSu9DQaTVFRUcVNEwQhEon0d1OpVMrl8oqbBgChUKh/KEtLS+Vyuf487EGhKMrW1lb/UMpkMoVCoR8ei8/nW1tb6x/ckpISlUpVbtMMw/D5fIFAoH8opVKprjbq75SFhYVAINCvexKJhJ1Nv14RBGFpacnj8fQPJXuAdP9kaxrDMDY2NhwOR1f31Gp1YWGhbtNarVZXB2xtbXk8nm7TGo2muLi40kPJ1jfdhpRKJXuWlTuUBEHY2dmB3qlRVlbGFrsuPHYRiqJEIpH+qSGTydjCLLdpDodjZWWlm0KSpFQqVavV5cqcYRgzMzOBQKB/5rIXgYqHUiAQWFhY6JdwYWFhuUsEu1qBQMDn83Wb1mg07AGqeChtbGy4XK7u9NFqtex5UbE8hUKhfgdCbLGXO3PZgIVCIVuS7KlRVlZWWlpacdPseaF/TSsrKysrK6u4aQ6HY2lpWVpaKpVKKYoiSVImk5WVlZXbcZqmuVyutbW1/qEsLi5mi71ceZqZmVlbW+sfysLCQuYF/dWam5ubmZnpH8r8/Hz9E0oXsKWlJZ/P112WtVote+nQP3NZNjY2bB1m/6nVaouKisodbrbu2djYUBSl27RSqWTnrFieQqGQLRy2PJVKZWlpacVDSZKkvb29frVRKBRlZWXlzlx2Tl2PTWx5lpSUsNd2ePnU4HK5NjY2+n812Gt7xUNpbm5uYWGhP6WoqEij0VSsw+bm5mzvHmx5qlQq3RWg3KG0tLRk52Q3zV64yp25Fa/t7BGRSqUVN80wDHtt110J1Wo1W+wVy1MkElEUpfujplQqZTIZVPijxp5BuikURclkMnbOcqtlr+2E3h813bW93KZ5PJ6tra3+oSwpKWELs9xO8Xg89tquO5QSiUQXs355sn9S9X8S6K7t5VZrZWXF4/F0UxiG0Z1B5XbK2tqaLXbdoWSv7VDh1LC1tWUbIdgpuj+p7PoZhmHrAEVR+oXJrrOkpKTipgGAvbbrylMul7PX9nLlSVGUnZ0dofdHjf3Nox9eWVnZw4cPQ0JCdNd2eK0fVEQ1ft1VnFL17z2KoqysrHSXaBNOrdXxZ87adVxkpptgFugvjD19W92vE6+KxRBCH7hZs2YtW7as0q9u3Ljh5+cHAJMmTfrkk0/S09MXLFgwZsyYqKgookLPlkuWLFmwYIH+lICAgLcUM0IIIYQQ+vBkZmayNxbBpFNroGlardEfxVqrUWu0WsbgAgih2mDChAnDhg2r9CtPT0/2g729vb29fb169fz8/Pz8/K5evdqyZctyM3/zzTcTJkxgP9M0LZFIRCJRxQy8lispKfHz83v06JH+rWJUq2AdqOWwAtRyWAEQ1oGq6ReLCafW3EZtGk1a88uZtrM6OHIB1DmxS9ZcC/5sCTZZI1S7OTg4ODg4VHNm9ikg/aepdfh8PvvcJuu9GC/RWKysrHR3ZFHthHWglsMKUMthBUBYB6rDhAffIsQD5v8cFDMiuH7j0Lahjes3GnG64YKFgxw/9CYl9eOtIxq72AnFXValvOVO7pnsdZ1tB+4pe7tbqcXUl7/zD/j+qqaac1/6tn7g9GvVnBsZcu3atbVr1965c+fp06exsbEjR4708vKq2GSNEEIIIYTQf8iEU2sATt3+K8/fvbhryYxvZyzZffHu2eX96ppwM/t/Q31x1Y83Ou17Js39Z7znf354tAkLWzqP+lv15mtipNf/GN8tyMXKzMzGLbj31O33Sv99VF/99J9Fn7b3d7Iy4wvEfu0+nnf4ieLNN/muMHkbu9v0+bPwzddEeUbM/CHCw6TPsg+Pubn5oUOHevXq1bhx46+++iowMPD48eP6rdMIIYQQQgj950w4U5Ud/qrPjY+PzQlpHu5l7FjeIVXWM4lbdx+zcpMZjYbhcEwmRyu7Mb9nlz/sp/1xbldbdzo1ZtWEz8MHlJw/MtaXA3Rm1Kh2n8e3m7/63N4WbrzCxPN7Vu4/mtJjgn+tGzqNdAz96CNjB1HrBAUF/f3338aO4sPB5/OnT5+O9yZqM6wDtRxWgFoOKwDCOlB9JpOrVcR3dbIoKtW+esYPByPd82mjicdLzk1v6unT7deH8v1DhWETF4/r2qKBX/jCOxrZvS1ftvUSWVmJPNuM2XxXBgCg3D9U2G7a6ikdve0sbX26zz+f9WDr5yF1bS3t/Af8Hv/ys97qi7OH/RKft3ekr4eHT7dfH2oBgFEl7fqqjYetwMaj47QTOWzLs/rp0Zl9G7rYWlo7NYxccDavXNdxTNau2UuyBq3f8V1nH1s+386vx8ydKzvd+Wne0SIA9eVl0w+6z9q3cWwHXweBhY1rcK/JGzeOr08BvGb86rMTvYJGzJrYu11Y84D6IZ9uuPt8NBzVk/3fdPUTW1sJ3ZsPW35JyrAzezcY/fN3/dq0CPbxajxs/T22vZwpvrHuszbe9laWdp5tx+1IUICBmbUPlw+bcbY0Zmqwh4dXy5kX1arEnV+183awFQrtXQP7r3n4Us96ib+Euo0+pgAA5T9jnLnuX51RAYD80CfO7Vak0v8+EM5kr+ts0236slGdWzX19/TVFTVTcmPV0EZONkKXoD7zzxfoCrrirjH5W3o79P2zgAHQ3JgRwLMZtKcYANRxU30bzLiBT5Gjt4XP58+YMQP/oNZmWAdqOawAtRxWAIR1oPpMOLWmvLuHZy4et2z/+Vv3H77w6FnJB9xDOCEctOXW8i5W7RbeSEk6PtmfAlBfPZbYbcvFu4/PzvC7PGvAt4+67ngslSbu7J70/YAfzrOZs/pK1MWGv8fn5VwYq1ryUYcpN3vuSijIONovZdaMXTn6xcUN/XHHt8EOAzc/Tk19vn5Qntp1tf3m+5L8a/+z2jphyWU1gDp+UeTo00G/Xs8pzrk013HnxxP2vpxcKy6fijPr3L/dv93hEaJuke2VsSdvqbWJZ85kNors5/1SEzVBEFB24fXj1z46ca/pqlNx1+78M142d9jP11QAmvvLPvr8dODK2/lFT499Jl86cGK0hAEA0Dw6+aDJitNX4x8cG57+w7S/chhg8vaNjfhFNupgcmFh0p6IlOnDFt1ihzitMDPp//WOn9tZhi+NT019cmVuqPr43K+vtduVJJFKsu4d+F+7l0ZVpzxah1pePntXA5r7569Y1OFfPv9IC+rb566IwsLcyp1dyguxJWP3Xbzx4MavXnsmLrmsBig7P3PoPMmI4+kFT09/Ld8X9ZR9u76yXQNhaBufG2evKoHOjLukdBfdPHdDBdqUCxfLQtoGmvDjJwghhBBCCNUCJpxaqy5t33Q77eK66Z8N6R/xwsBf4v6D94TfI5T/R+N7OHMAQHN1z375gBlTW4k4HLuQqT8MVOzffVkNAMCpP/y74f6WlCBgQJ+gQtc+3/XxMKdsmw/o7poQ//hVzZm8ll/MiPQRkOa+Q4e0yr1zV8Kor2/7M6vPrJnhLnzS3LPPzK/qx+w/J9dbhCktKFDbie1fSp559g7W0tx8LS3JlxL2YvsKNUv9JvET4j5fDqnLBeDUGfRlz6Lo/fEa7aMDexM7fDerqwuPtAoaPWeU6Oiu0zIAANK9/5eRdbgAPO9OHVwT4h9pmPwjm496jl8wIsiG4tg1Hz8tovDQkUfaymcuFzfJ5VKFybfupRVpuULfZgEOL+0ZN7htC0nc+aearIuXyQFT+6ovxuWoUy5clIW0DSqf7vLCRn3ZzJoAwq5Dl6ZsUV+NOqiKnDaukTXJEXecPqkT+x5A5btGebYOFVw+d1ddcikuo8PU0S5XLzxWS+LOJzVp2xxvIyKEEEIIIWRUJtzYxe+2/M6j5caOwthIe7GIzeW0+bkSx2BXLjud41bHSXo9TwMAQNg52JEAAATfjE/Z2T//B8+Mp5aoX9XIT4ocnm+AZ2ZGqlQqRpuVkSk5PbbRuefDnKlKeI1KShkQvGiuJQQiEVeSm6/Vrz+q/Lwiga01SdrZC5kbufk0WL6cXb9+/AQAKdJl8qS9WFjwNJ+mzXMKhO5uAnYq5ebhqjmfU0gDAGltY83GSnB5HJVKxdA5GVll8Uu6Bqxlp2vLyiyGFtFQ6czlCsis6/ydY3/4eWzotxmCxv2+XrjkqxChXsO1ect2jR4ejstwu5DWfOTQjnnL/7qYYXY+scnA5nyAl9P0fzdFcbmESqVitPm5Uuembs9LUVCnLntHgs6rdNe4wW1bSJZcSL6WHN+gw2/hojVz4p42unzLr+0K2w+923yEEEIIIYRMnAm3WqOXUfZiu5z0zOfZmibjabbQ0aHGt0YIAOYV2TYldnZyHbb1TsJzT5492//pS2Oembfs2Krs5P6zJbopTMGJfWegVdvGXMqnQweX2/sPprz0ljzDMG8UP52dlv58XGJVelqOnaM9STo4iqTpGc+b07UZaZlcB7Ft5RWadHAWW7Safe7h811KTMuMXxjGNbQ1giDg30IiHdtN/uOfO1k597eEJ8/+csWdlxJmwj60jdf1k2tPxge1bW7Tsq3/rX/Wnbrp2zZUWI10l7IX20nzCp6PsabOzytk030Du2YR0i74YcymI1ecWrcSBbdt/uT0+uOXrUNb18XTGL1LSqWyVatWlpaWd+7cMXYs6N1JS0sbN25cYGCgvb19gwYN5s2bp1LVrqfIaq1169YFBASIRKL27dtfv37d2OGgd2fJkiVt27Z1cnLy8PAYMmTI48ePjR0RMpqlS5daWlp+9913xg7E1Jnwb3JNwv5F88pbtD+htnbYxG0xMMI8asHya1KtVnpt+fy9/H4DWxrMDg0gbUW2ZWlPcqrqHY7b4uOPbXb/MO/kUzkD2rLcBzF/X3v5XWvCZeiPk8W7xwz/5VRykUopfXxs3kfjD7tMmNbfgQBeyJT5vdPmRI5ZfzapQF5WnHnn6K+fffZ7Avkm8TPFR39dckmipaVXli0+LOrbL5hD+UVEep9ZOj8mW82UPtwyd1Net8EdBZUvToh7j+r2YMnULbcLNMCoitKuRJ9ONFgGhMBOyH32JFUFAMAU3Tx88EamXAt8WyexFZekqJdzZsqnTSjv0Jr9otatbElhWGvbfWuOWlYz3eU2j+xNH9hwIo8GUCdv/+N4EQMAYGjXCPvQNp5x67bKQsLqUGYt29aPXbe9oGXbBib87An6EP3www/Ozs7GjgK9a48fP6ZpeuXKldeuXVu4cOHGjRvnzJlj7KDQWxcVFTV9+vTp06dfuHAhKCgoIiIiNzfX2EGhd+TChQtjxow5ffr04cOH1Wp13759ZTKZsYNCRnDjxo1NmzYFBQUZO5D3gAmn1qBRyHRKC7Pundj655kUWW3NrAHM28zdu8Dz8EBvW1vvgYc85u+d186ipusgxH0mfqZaHGxnax+24J6B5JLXZMaBjR0fTA9zs7a29wwbtfqahC43i0XzWcdOTHM9OSnMVWAu8uu9hhkbfXR2SwsAANJl4JZzuwZrdnwRVtdO5Nlq+PIHAUN6e1FvEj/Hb3Bv5U9hbiLPiAMeP+/4oQUPgBP0zV9r29wcG2Rn5dZpDWfint/6iQy1FBOOA9cd/NZq++AAkbXQtUHvGYdSFIYb7806jpvssL2T2FbkP/WcMv/Kqk9buAlthO7hG62nr5kQWG4MMW7jtiGE3D4srC4JlEebMLsyKqRtw+rdNLBoO3f7+JLZbRqHtu8y9WHDTnXZdRvaNapem1AbOTekbQMOEMKwNp5l6ubtWppXa1MI/Sf++eefmJiYn3/+2diBoHetc+fOa9euDQ8P9/T07Nmz56RJkw4dOmTsoNBbt2rVqk8//fTjjz/29/dfuXKlubn5tm3bjB0Uekeio6OHDx8eEBDQoEGDtWvXpqen37p1y9hBoXettLR09OjRq1atsrW1NXYs7wFCoVDo/sHoPSps6HM1Z6v68+XLl0NCQoRCoVQqrX6s6pSNn/9stmD9MOfKbgjweDwLixqnmujNKe6t6NN1TZ3fT6+PcHkrt2rUZyfW/1q4/9qPwdg8+yHTaDTR0dGdOnUqN/3UqVMRERFKpdIoUSF9OTk5bdq02bVrl0gkCgwMvHjxYsOGDY0dFDKOH3/88dSpU+fPnzd2IOgtUqlUDg4O27dv7927NztlzJgxRUVFu3fvNm5g6N1LTk4ODg6+cuVKYGCgsWNB79SYMWOEQuGiRYu6devWsGHDxYsXGzsiE8Xn86Ojo0251bo8bp3QoOyLt9XGjgO9zCxo4q6o7zyTrjxWvHpmhNB7imGYL7/8cvTo0U2aNDF2LMjIkpOT161bN2rUKGMHgt6ugoICrVYrFot1U8RicU5OjhFDQkZB0/T333/fqlUrzKtrm717996+ffvHH380diDvDVNuBlQW5Rb921ClKXpyetX+EpfOVBWLIKMg7FqNmtHK2FEghN7MrFmzli1bVulXN27ciImJKS0t/eabb95xVOidqboC+Pn5sZ8zMzP79evXr1+/kSNHvsPoEEJGM3ny5AcPHpw8edLYgaB3KiMj47vvvjt8+LCZmZmxY3lvmHBqrTwxpfGog2xuTRAU19olqPOXS8c3NOGQ0VvBbbcyGd/tQeitmzCmT9kPAAAgAElEQVRhwrBhwyr9ytPT89y5c1euXLGzs9NNbNOmzeDBg9evX/+uAkRvV9UVgP2QlZXVo0ePli1b/vbbb+8wNGQcIpGIoij9fstyc3MdHR2NGBJ696ZMmXL8+PETJ064uroaOxb0Tt26dSsvLy8sLIz9p1arjYuLW7dunUQioShs6qycCeep/D4bn+VvNHYUCCFUSzg4ODg4OBj69pdffpk5cyb7OTs7u2/fvlu3bm3evPm7ig69dVVXAADIzMzs0aNHo0aN1q5dS5Lv0wtl6PXweLzGjRvHxsay71rTNB0bG/vFF18YOy70jjAMM3Xq1MOHDx87dszDw8PY4aB3rX379leuXNH9c+zYsb6+vpMnT8a8ugomnFoDAKizr+zZtj8uIVfJF9cP6//xoJZONR1uCiGE0Jtzd3fXfba0tAQALy8vbMSoPTIzM7t37+7u7j5//vz8/Hx2IjZgfvDGjx//xRdfNGnSpGnTpr///rtcLh8+fLixg0LvyOTJk/fu3btr1y4rKyv2HXtra2tzcxyYpLawsrLSf7vewsLCzs4O37evmimn1tqUbZ90n5XcsF/vlsEW8qdXVw7ZdOSnv7d/7Im3ShBCCKF36fTp08nJycnJyb6+vrqJpaWlRgwJvQMDBgzIz8+fN29eTk5Ow4YNDxw4gPdTao8NGzYAQPfu3XVT1q5di/dWEKqCCQ++pb42M2wKtfrEnGbPh9SSX5/TdRy9LO6n5pW1XOPgWwi913DwLYQQQggh9D4y+cG36LzcEt+mQf9myxZBTX1LcvJoI8aEEEIIIYQQQgiVZ8KpNde/kfeljWuuS7UAAKCVXl+z8ZJ34wB82RohhBBCCCGEkCkx4XetSc9PF069+HGfgNXOXs5miqwnWZad524f4WHCdwMQQgghhBBCCNVCJpxaA5gHjd52JSLx5q1HuQozsV/jpvVE2GSNEEIIIYQQQsjEmHRqDQDAE9UL6VTP2FEghBBCCCGEEEKGmGZqTWde2HWR37l/cwcSQJ16cObEuQful9o2HvrTyh+6uuDYWwjVOnw+39ghIIQQQgghZJBJvrisTYmau/BCiRUJAHTG9qkTD/H6//jrj31h94SZh6XMK5dHCCGEEEIIIYTeHZNMrTXJj9L8WzTmAwCTf+rwtQZf/zptSJ/B0xaP9bp6Ll5t7PAQQgghhN4TFy9eJAiiph8QQgjVlEmm1qBRa59f2JV3bz50bdnCmQQA0slFLC8uxVZrhBBCCKHqCQsLe40PCCGEasokU2uOXwOvm7u33S0qe3b0r9Pc1u39OQAA2oynmSInB5MMGSGEEELIBDEMwzBMTT8ghBCqKZPMUymvT2Z+LFvWwdOlwdiLjb4fH8oHANAmHz2S3qhVII6/hRBCCCGEEELIlJhmD+GETeuZJ+59lpRWauPp42jOvvPDOESu/sfSy9LIsSGEEEIIIYQQQi8xzdQaAIAUOPsG6E/gCOv4CY0VDUIIIYQQQgghZIDpptYIIfTOqW7PDf9GsPHEFF+KkZ6bN3T0quuF1h9t2+M0u9/TeY/XduYp/x7tvTDoXOxkLxN8nUZ5dJTXLw0vnP7as/LglAeGu60Ou3VirNvrB69NWtHt4/wFsXOb4UjjCCGEEDJ1EklBakpKxT4kCAAPT087O5GhBYuLiu7fv1fpV4GBQdY2NhWnm+CPQ4QQqpL23rxmwsi/Sv7zFTN5+xb+5fHlyHoUAJMVtWCN2Q/xuXmJK7vW6Tx51iA/zsvzbu4l6ret8D8PwsRR3iPG+e5dsDsb+zlCCCGEkMlLTUlRKBV2QjsrS0tzM3P2P0uBQKFQpKakVLHgo0cJHA7Hzc3d3b2O7j83N3cOh/PoUUKli2BqjRBCLDp97+arrQZ1ExIAoM3NyLH29hGRAECImg8Z1bHOG10vaa32pWS0LD+3hH6TFRoLYdtlcOtbW3anvpfRI4QQQqhWYQCcnVw8vbwyM5/l5eXm5+fl5mbn5uaKRPavbCYQix0PHjyQmPjYzd3dzd09KenxwYMHxGJHQ/O/N6l1YQHIiqHgGRSkkWnnOTdXmBk7IoSQCWGkV34b0crTQSh0Duj+7f4UFTtZkfDnF6Ee9g5uAV2/WzEltM7nJ1SGV5F/+vjtBu1DBQCamws69/v90dMtQ/x9fHx8fOo6WLl9eVJvUW3Cyk9mnis9/V1TH596obMvqQHKHu2a0j3Q1c7W3qPVyLU3ixkAJvuPbqKeM5aP7twyOGDgxgy9ZFSbsW1QPd/2n87ecia5+E2SVCb/7PyIQGd7R6/mH6+Ll734I0HnnV86PMTLwVboHNDzf0eeal7Mr0k9MLljfVcHB+8OX0elqAFAfX6yX9M58ZrnO7awlefYUyoAAE3awW871RPbO/u2Hbt6ZndR/x1FAABg0ap9o3vHT+VguzVCCCGETJ9KpTpz+lRyUlJSUuLjx4+SEhNpmtZoNa9ckCCIBg0aHjp44NTJE2djzxyMPlC/vj9BEIbmN93Uetsl5Z6L6r1nNMWlAAAWlsAzA4EtCES0wIE2d8AGE4TQC4zk8NQhS0s/PZxckHNzZYOzYz9adk8DoLm7bMT3Cd13Pcp+enmx2+m9d6q8hmoe30kQ+nhbEwCcJtNP7htbr+7I3Q+TkpKSHv09OeDljimo+hP/nNvWsuPiG0lJiRd/bMUtPfu/ftMSu2yOzylIOTZW/etHP8TKAABAef5k7sg9l+MT9n/urnfBpepNPn5z5/gGkv2T23j7dRz1059nU0prflljJNFTP9kkmHY+PfthVP8HO/8uYQAA6NQNI4Zut/3+VKok5+bqphe+GvPH80Zm9dXdZ/1/u56ReW99s7jxo9ckaQ2tmk7bNPbLmAar4rMy4zeFXdl9TndngbCs5+vw+E6CusbhIoQQQgi9a1qthiSpQYOHDhk6rH/kAIIgSNJgelxOu/YdIvpFHj1yOPrAvp69+nTq3KWKmU0utU6Qalbfku+MV/i5UINCueFNqcMxmjNnmEf3gcsDMwGYWYJ9fdr/I8NNTwih2kZ+du/ftp/OHB1kTfKcO834tmNKVHSCVvvoUHR6p8mTQ0QUaR38xdcR4iovo3RxUamFpaC6l9qXlMVu3cP5+McJLey5lJX/sOmf2ByLvq4GAOC1GDkuVEgAQPkVm7k0HzD1t0O3U+7uGt+gYP/k1t7+ncdsvqsCAG3iyu4ebhV4j9pX7gVz+dl9J+xHTBvqY07w6/adPqYZBQCgTY7aej3k27kRXgKS59z+u6/b3Dn4D/tyNOk++NvRgZYE17Hj9Emhd/cdfWogn2dy/tl/pdG477s4cwlznyHfDNd71Zy0tLIoKSrGVmuEEEIIvRcIguDx+QKBwNzcnCRJ5u38iDGtHsIfFKljs1QjgixOJqoeZWpSnzGUmrAXksENiKePiftXIbAF/pZDCJXHFOXlqlw6uFLsPy3c3IWS7DwtTeZKhK7Oz3uy5ji7OVJJVayFtLaxlJfKXuciwxRmZhY/PflR8H42BEZZou3IvkpN2tuLqr6HyXfwadAwOLjhzduHHz1IK9QCAOU1evuVQeryoRBmtpYvb7coL0/t1sWN3SrpVLeOGQAAnZWeLT83s3XQz+xsGjnXvUkxDSIAysXdiZ2bsHR1sS7IyTeQWtMFOQVmLi5C9oYA5eTmTN7WfVdaIre0tn6tmxAIIYQQQu8aTdNqlYogCI1GS9O0VqvlUNVKhM/GnmHbq7lcbvSBfQDg6+tnaGbTSq13ppX91MB60x25kMMhCGBohqbBzAJiY2khn0q9ST67TXDUhJsX4dsdW60RQs8RNg4O3MynmVpw4ABA2bMMqV2AA0USYjtpQpYSgAsAmuyMXINPPwMAcHwb+Emjk4uZMFF1kkaCIOBF7kvYODpa1W8fHTetPqU3C5Nd5Ro0BQ9ORm3fvm3XyacOHYaMmH50aZdAEQcAQJu8YWj4gjvln7jmd111b+sAq5d3nCfJK6BBSAEwRfn5SgAAUuwitu3+/ZWtES9n4koAbVZ6thbqkgBM6bPMYpGPPQm5fK5G/XxbTEkx+0w5KRKLFBczpQw4EQDa7IwsXQ7OlCY+zvPt5setRikhhBBCCBkXSVEAcP36NYoiaZr29PLu0DE85ckTkL1iQYZh7t690zeiX/sO4QDAMPS9e/fq1fM1uKH/NOw35cAj9j0pE1qQhQo6yJXj50r1bc3hUlBUROQXMF4NaEsRbWnPyCTYdo1QbUdrFC8oldw2A7pL/5z/Z0Ipo84+PX9JjEf/vvUpyq93H7dTy3+9LNHSxXfW/xpddcdbhH3HbsF3z16WV2v7hEAo5D5LSWPv8ll0HDGg6I9pqy5lKwE0pZnxx0/eK6lqa3Tan4Pr+XadE6MOnX7oYfKV3YvH9XieVwMA5TVq++XrFVxc1vPlXBks2vbvnLFz49ViBkB+Z+PmCyoAAKrewBFBZ36ecSCxWAu0UpJ04cjFdDYzpp/uWbrlYSmjyY1d/NvFwH4965Ac7wb18y7EJqkAtDnHN+x7ogUAIBy7RDS/tXrRySw1U5a8e+n2R7oX1csun40P6NbJCVutEUIIIWTiCABJQYGTk5ODg4Odncje3sHF1TXlyZOs7MxX/pLJzc3p27efj49vRnp6Rnq6j49v3779cnNzDM1vQq3Wi5KKgqx5TyRaSkP5WVOXnqiDHTl7Tmua+XDsHcAcAF78sFOVEPJc0kKMPZkhVGspj4x2sRrNfibsPolOW7YzdeLX3TymFHGcWgxbtWNqAw4ANJy6ZUHq6MG+K7XiJkM/7x8Ur6zqdiJZZ+DIZqt2n5D26C98ddpo1uHLSetGdnVZyTiNjLr1S8dFhxbOmTq2xdw0GWXtHND28wVhnatYmrBt+e2RR8GB9pU3/XKsxK5WlX5TbjWifks33f9sXGgLa0eha5tW7ayvAgBQXmO2/aX47n89fEcXaMzsPJtFTFvRHQAAuC0Gtbn7VTO3OwX8wCErN42rRwHYD/hp3j8j+gTvdnB0bd65/fMe20iP0WtXPx41rqGzzDag78SBrQ8/pigAYAr/2X0+eNRPnqZ1ZxYhhBBCqCIPT8/UlBSZXK/pRKmUyWRmfDMPT88qFvTzq3///r2MjPSKXwUGBlW6CKFQKHT/YPRe6Db0uZqzVf358uXLISEhQqFQKpXqvlr2pHiKl/WahzKOhujvbZZTxNxO1tYRUOZcIjONKM0kbc2Aqya4atJeTChTOE2mlOlHxePxLCwsKt1JhJDp02g00dHRnTp1Kjf91KlTERER/8EGlDFfBX7rEHV1TqMqbikqb/4Y/p31phOTfSnDM9VCmvg5LSJyf3m0OpxK/q3H8Jy5Z+a1wBEQEUIIIYSei46ONqFWaxUD9Iv/EnK1SVm0lz0nJ5MhNcBoCIIAvjlhZkFwNVCYSopcqnxnEiGEAIApuHEyQdwm1J0rubpicbRZl30BVV/0+E1mXzj1joIzeWX3Y+L4LTr4COT3t87fJu24sjkPALwnnLhk7MgQQgghhEyOCaXW4SKzWXeLnCmOOUkCAQzA01xtsCfHy4nYvJnp0wPUcsYrgAaA29vJ+kOwGzOE0KswqtQDXw/4OE1Gk9Z+PWdtm9GSZ+yQ3iPa7Jg5Xwz/qFgDFh4dx21d1MPa2BEhhBBCCJksE0qtm9vyLmYre7rwDyYqHykYR0tOPRFVIIGjp+l63uSNy0zEEACA1KuUtTu+ZY0QqgbSOXLNtcg1xg7jPWUZPv9C0nxjR4EQQggh9F4wodQaAAiCUANE1DM7cl9VRjB3n2r5NEmSRF1PyHjRh1nBE7LpkPJD0iCEPmwxMTHGDgEhhBBCCKFKhIeHAwAo9JTpkeuRvaxUT4meYj1Fegr1SKVSqVR67NgxtgMzaQU/nc9Yci5z8YnM03fyTl3Pv3pXsnVP/l+7CrasLnx8rzDxbuGhhaXpCUUVF5TJZAxC6L2lVqv37t1b8dTeu3evWq0+fvz4SzNnzgj85dMjaoZhGLp0XZf5rX+Vahnttem/+055qmIYhtHEjP216dwczYslVOeP+zQ8fUPN/ouWbPnLstn5+HMn/R23/f4g6UvXXz47rmFqQBs74VcK5gDMAWKeT8SFszk0wzDFe/baNThzi92KpmBxq2Vj/tEwWumvrRf036lkGIZRP5setHLyBa32yaXWVjs259MMwzAlDwbbrZp+Tctosuc0WjRgl4LdRtnhA07tLqdqGYZhFEei7ev+fUZZIRDFw49Em1ek0S/iqmRbBoqrcpXvAsMwDH3nx3Ue41J0IWgzrnW02vLbU3bT2kvfrGo4I1NdfCdCvP+gQnv5u99DFuRp5PcGiPcfVGhPf/VrnbFP2B2jC271tNv9VxHDFN/pa7V5ZRrNMIw25VIb+/0Hn+86nb1um03kvWpd1VWpE73XzLxV+Q7Jo/batbuSpmVoye1eln8sekQzDMOonk7xWfblyedHXHXmb8/GsfHqF8sYOAp05o2u1qvHReXklNEVN1RBhV3QSpaFze++UUYzDMPQ9+atEw66X2pgYcXB/UL/U1fVDMMwmkcXWoqi9j1fUfmjwEjj+1j/sTCBXWvJus7zw1cXs/Fp7p9r5n7kZMU68xL10ZHLuq4vVT2Ma+0atVuqvfnDmsazs1+cKKV/dF3Qf6fqxS5UWrueTQ/4ZcRhtnaVrAl/fjJenbbK4eMEOcMwDKM6e8yz7tEzqiq3VQOKbX0W9Ngs11X6a9NXt1qQr5be7i3ef1ChvTVrbasFeercm90sNy57wh7x9Kn1lk+IfV5Jyh/xKrZU4byr/jluqGQqPxeqvy2GYTQFv4TMD19TUp2K+N/vwmtcpspVJIQQei9cuHDhvJ5zL5x9ITY2NjY29uTJk8ePH2d/x5pWqzUATAgSFJTRXAI2XFA1cOAkp2qCfSi5hCjjMQVZBEmDS0Pt/f2clmPxXWuEai+6LK/QXOxAAgAQZmL7mnbnTfAFXEqh1rbq9CAbQPn4UwXX1qLGwzSLRg1L3+BZcPpM/y+zMlQAwEizS2UpaYOC7rEDUymLmfYyBgCAoAQCNkiKy2FKNUBL5YUWAkcrAgCAL3C2IwAAtLKsHPXp7/+oPxsAADRq0tleNzw25WLjWp0drbCtmhSX4V2ogM4tyVVlLer0+yo29jIlf6CKAQCKpAAoiuRwSCBJkqY1oM3JkuVdOBx8ml2rttTMMaKEAWsAkitgh3bgUBwNral0SzXEFGWu//70mlP5hRqAsjKZj+vzB51IC7H9iw92yhtSAwOPGzgKhHPDlZsKZ6/c22CUzKZlk2Wbwnu51aTO0Ip8KcdBzGeXcRBbaCRlxQwIDKyDtDG3JdgPZjaqsvwSBiqronRhmZSxcHQAAADCXOxASvLLaLCq9ilBNQlxSLie/Yx5qnAsO32pVHarrPFXIoO/DSqpXYqCInMnMVu7zJ0c2W8ZaYHC1lHwfCA3B4FdYa6EpkJrtK0aICgOQVAkRZEUEBRFajQMAABpJrRhI+ALrRX3JVUObG/Ay+ddDc5xAyVj6FwgarAtACC4bu7m1ax///Eu/CeXKYQQei+EhoayaTYAVJp+A8ClS/9272pCqfXqtNJQG56nBYdHETwShrXg8UgAbxBakukUJGSCupjgaoCrIW3qvM6fR4TQB4M0d7BVPMunAUhgFHn5z0cMoDikpvR5VwxymaaKCwXPW+SemZ8oh8bWQGcUJCqFH3vXOLUGACAo5/B287ut/XxBat/fPYWOAquWQbEnm7nor6yyziFIO4FQmV+kBOABaBXSYjAHAFLg5Gg5dNnYVR0q+3FKEORrxWiouCrdn8p3AQAAKIrQaP/dGdLBUmzlM+v6gIH6I3CX3C2/GAMApNhJUH/c4GuzHV/asZIqAqkw7qPhWQkAvVnpW78cnJvT4uStJv5WhGxvlPty3Tfy3HwAOwBanivh29u92EOSILX0v4Vi8ChwfCM77ozsSJfk/fHJtinL63dd4lb5sOSV7gJpJhJqbuUq2RQ5L1fOsTO3NnxA6dySXBrqUUDnyfI4ZnaWz2ctfxRszIWEPCcPwA6AKcvNo4Vh/2ZcBLyyDAn7Zi62W9J2yMhPZtb551T8pQRxSOOa5Eakub2tqqCIAQBgVIVFbEESQpFZUaZcA8AFoPNkEhtzIfnG26oBBgCAlmXnPj/iOQV8kdDAEa/aS+edgROk0g5gKi8ZA+dC9bf14tsaXA3+2114S5cphBAyZYZatgnipWseaaz4AODw4cNxcXG6f2YqNNkKOqeMLiij5WqGIEBoSQotSQBwrwOde0O7vnRoJN18sKZ+T3zXGqFajSPu2Z1/Yu3tR2VMyc0bWy8+/zXv7m1TeCszTQtMfvL+WEUVSQUnqH4/t5Tff88qVstOLr+VEBrYw+n5tZGRPBgV8PuwbaXVvoXHbTu+qXj3hT/TGMvODXsmX525K7+YBo2s+Nr+hOuFla+GdPfq4fds3+FSGpj84w9i2Nk4DgOHWB76+eL5bC0wWsmjlF2npP/BQIOVF1flDO8C4eJjq7iZ/qDsxS64+H0Ukr7kx8Q0OcOolU/O3zt631AXk1SroQHKrWfX3yzTAFOWk3M06mlmVd1RElb2Fpyk3ARFNfaONHcQlj54UKYr6JJilZWX2MOKALV01/YUue4LTfa2Nal5Gm169PVohVeP5s8TA8rDrk5mxsWnLwIycBTonIzoE3n5SobkcHgUmFtwqswgKuwCadu5m+jyhqtXpIw6M3HVjqLW3etYGF5e+/Thmv1FKo3s9Pp76W3qhT2ftfxRAJu6PUIk21YlZ6uZgrirG284dgu3ZP+yk3YWwuL8B89e0e8nx9+tUdqt9fmunbt4eZy9/rfApZl9TXIjjrh7F+7p/amFDGifPox6XrvIBt29rY5f++O+mlFId/7+UB1erym3qm3V/LyrBnXO9jUpuRrt0303Dmm8ujV9/pOn/BGvieqf4wZKpgbnQg22xaKl6/qvChyfWPV586a78JYuUwgh9AEwZmrdunXr6dOnX7hwgf3nPD/bbk5mvjYcDxtKaE6KrYwZG0LIpFEhP0RMJi+HOS1pMk3VvC2PIACAsO/f5lvB9Y5BG8LHpNo0ErDXkOL9h/w8ltcbeCst4Uovn+WeAceOqwC4zt//GV4n6i9Xm9++vOu1fm3jOrorjlaZ9USaWURX/yc+5dtofLvs5SvSFULflVEtiTV/edsuEHlumbhLojCUpFAOE/9oZ7Z8a6OWmwbspP0c2fnIwCkDVrd6Nr7JL1Y2S4MHxsXlG4yCTrsZWW+5h9+Rg9JnP4at8PDaOOeKwcy2suKqHGF4F2z7tp3jdreDaL5N4KlLagDSeuTG/pHPYtu6LbQRr+o5M+mZ4ee5zVt33Pez9YFhv4usFrq3PLjutrLq7M2ic4vv3B70rLPUzXPnmpQqDwVlP/zb+unTVju7/ho4+YkKyLDJnTpeOdi42ab23U4+9nD5t2WY6xrKvRjmtDh4WumQjZ10Y4mR7sE/TYLFjRYIhJuXPGIMHgV18cm5uwLFC62cN61gWqya5Fj1Y18VdoFoMCViaWDyYK8Fdg1PJPXss2q4ZRWFwA2qY79zh5vdb6NueK5Z0cDZ4FGwGbW2d5cHfwfaLag3PLXx8j4T/J7PSjj6Tx0Ly1suc3VbOWKP4TtNZs4t/ZXmjTx9LFzD66uVjV3rcwA0OQs7rvTwXD/tnOr4xN89PFb2/K3AQPpEhc2K+Dz7VGiTDW0+S7fy57KbN2vTfsd0/ubOS63EGxcrm/65yMfa0LZYNT/vXo3nEkLFhToubjRDNnxTeI8Xj1dUOOI1UMUJUkHlJVP9c6Em2wIAAEYrzZA+yVZVfc/gjXehBpcphBCqXQiF4t+7m/qPrhn6XM3Zqv58+fLlkJAQAIiLi5s2bdrmzZt9fHzebEeAx+NZWFTRAoAQMmkajSY6OrpTp07lpp86dSoiIiImJqZr166VL6ncHrF8V7+Jh0dU983D2g2L6z2gPHTAdZHTlfOtvPEecyVUe4ev+jqOZxfS+u8djdwNFBGTd6uHT8LQtKGf2L7b6NDL6NSbQ3pdvCZRtVs1YUv/ql6hQAgh0xIXF9eqVSv9x78PHjwIAL169dJ/IPzSpUtarTY8PDw6OtrI71qHhYUtWLBg6NChAoEgMjJywIABzs7Oxg0JIfQ+oJ9lnMqybdfUkkm6u+u6uOMvZpgoVgGLC31AeAO3Txlo7CBQNZEeTfbca2LsKBBC6I0dOnTo0KFDAMAwTK9evSqdx/jdmLVu3fratWvx8fH79u0LDw/38PAYMGBAZGSkjY2NsUNDCJkspjRn/Se7h+TQPBu73j/3GVcPU8WqYHEhZAD9YO+FPeW7CSDsQhuP72KNDw0ghBAC0MurAeDw4cOGsmvjp9as4ODg4ODgn3766dKlS1FRUT/99FOzZs3YHJvLxeeHEELlUH5Nox40NXYU7w0srvcLv0+//D7GDuI9Rzg0PlbUuBozkgED287BRnCEEEKGHDx4kH0UXMdQdm0qqbVOq1atWrVqtXTp0piYmKioqIkTJ3bv3j0yMrJPH/yZgRB6beoLJwK+4u2+0aGJyV31/lOq5K+bHIkukOc4hl653q7ha+4s82TZ5sDZEkcX32XX+vS3NjDXf7OtmgX2cP6GLmnhyeu8eNWbP3/TXz6HGqZHN7B69cwfEKZs14gN084p8rMdFyd/PM71FQ8p0E+u9+9y/kKmvN2m7/YNwbvZCCGEkJ6+ffv26dPH0LjW+kz3Yafw8PA1a9bk5uZGRkbu27dPLBaPHTs2JibG2HEhhN5HlLf/7Om+7nrjsMrjb3zSbKmVxUKv9sf3p77OMDwAit0DF1KCHRuyX9lBLpOzfrvtgPvy19pMDfC8l9+blLizkfObXtwJ6/59Hj0ynFe/xrbUaZN81s66/XpF/ZxT17C5Q+3fh9sj7+qIV4owH/LnhNRHvXqaVWt20vLHEIUAACAASURBVKtZdOKYhW3f0hDTCCGEUO1guqm1Tp8+fbZu3frs2bN27dqtWbPGw8Nj6tSply5dMnZcCKH3COlcZ/gQVwdd6506a+EnMSl9hzyVfPWbf/LnX9x6nTFuS9OOnue7CTOOnqpqDG303yGETQM+bY9vwCKEEELI9Lw3P1C4XO6QIUOioqLi4+MDAwPnzp0bEBAwa9as+Ph4Y4eGEHr3mKKMpf1+t7da5NnhnwndFrddXkgDff1/q/2mpqsBALSnxy1vNi9XCwDqjFkhKzycF5oHnbn5YuBlzb2EA+me4ye6Cs2suk9t7Hfxwd8vWp4Z6YPPG675ZEfpK7NlxcXEM9b+//vUNu5oagk7qeRuhO2OTfkMANAZ1zoK9+1Xgjbxai+fFc3/l1Zy7G9/j+Ue9XavS2EAQJOW8G3n3+ytFoi8tozfJVGya1Dmb/18o4f9QqFoqX/42fMvGj01z5J+7L/OTbhA6LSq88wn+QyAVrpr/J9BbousrRbVbXng9+vKqgLWFP89c2eQ00JL4fKwL+8+UtawtPNudbdaP/jzHa2ar/VrsGN2jLyqbZXl/fnFJg+7BdbiVd3/l/hMCwDaE5NWe9aL2vI0f2WPlR4eK4KnpqgqXVabv6Dligmxevc5NJnTg9b87zqtTbjc3We5q93Pzl880S2rOnHYtXH09DE7Wjda4dbgwJ9JNAAAXXps+p/edgvsff9aeEn5Yl1M7ulzfYOW2Fgtcm8Zve62qoptVV4IOUkzeq6rK1pgZbusUeT52DwGoGZHvLKSMVTiymsr9zV1W2QrXOwevOu3+BchKSR7v9kR6LTQ2m5Zwz6XrsgBgL6zbn8bvyW2VgscfLZ8tT1fYXitAJpHu4538v3FymqxT+dTRzLwjhBCCCFUDYQB5WZ7Hx6re5mNjc2oUaNGjRqVlZUVFRU1adIkmUw2cuRIuVw+b948Y0eHEHontJfnHVyianEuu5nrvbPdwlWcbobn5br9dHnSzAsnAr76d5oquSDd1d6n+MmSRZK2k+3r8a8lJDPgQgAAaJQZjwsUUpoBqPIVVe21v59o2/YY2hUWD358Qe7fw6Ly+ah6LY4kNc9Zv8Pvn8YPowKfz0UXb/7y0DG//nf/9uZdOdMj4uDqpp9OrkeUHjn7zQ2Pw09GhQhUyRdzGfYRXW3+8sEHDjbteyGzXl2m6NTxYi0DAFquX5NNl32bujB3l+3uNPJi6K0OjSu/pjMPl0eNOOqy5crAbiLp2qE7Rq1wPvedfc0e/9XkZ/r2i/3DQXX+n1Yfx7S617ubZeXbuvdr9OQ79Y4kj2wqT5zY6eD4gLH7hwu6rhiXsiRtkv8xm6gxPzUyfFOXErZsCnNuFdHthexMjCT7tsTpywCSsgg5ltTy4fwNXdJejuthDrV+1IX1EDtx7afLnw5ZVVcW/c/o3VYr4r+L4D6Z2H6Xun5zAGCkj6YOvyn4ZXTOUMGj5bu7DD/f5GZ7Q9uqfMfU4D6gW+xf7nV5hX9+svWTWa6P13hV+sB15UfcQMlUWseYrHsz5pR+dHHqlPqk9OHTpOdzaS7N3jX+euCeW0PaiTUPjqTKaQAAtcBl6t6uXYPMZXFnuvc5sr71iIkelddcxaXYyEnZg/aOOxZG3fh558Bx8U2iG7m8N7fYEUIIoXcoPz8/Nja26nnKvW79Hv9JdXZ2njBhQmxs7M6dO5OSkkjyPd4XhFCNaHKPHivrNq5xgICwadF8ZFhN3xFllDK11ozLTX2yaVPC9XzKwkwte9EQSzg0Pqb44cz4Vz11rMk59k9ZaLibVVOvNtonRy9X0QRZmeLUIxftPp7g7cwlRGEtRjfJPnqqlAEAioCiovuPS2UE37uNuw8fAED74P6OBM/pc309zAnCwrZz/zqOJABlHzkhqIUbjyL5wcMCG6fnGWyL1ubu2ZHf/vsOPetyKEuH0V94PPk7OaOmD8BznPoPsOcDWIUEdOM9+fuGgeXpwpN/57f8vEWIkOC51psw3Prs4bSavG9MNgpxTLqeU6ZO/z7otwkxWvWdzIf+bk0NvzNM+fpGNuYCcJu2dJAmS4sY+uqxFMvI5hHuFNfJ56thYvZug/rK45OCgPGDhGYkL/iLZuE5Scce1WxbpJvPlyPretqQpLnd4CHusof5uTUqwxqVDEGQWvmT+5I8BWHn79GiDgEAoM7YsVs1cHbrds4UUPyAvn7NLAGAbDo8JKKhwJwk7UMb9PGSPkgyFBZ9ace9ksh237cVcCmzluMbB8c9Pltak11ACCGEag9LS0uPV6lTp06dOnV0i7x/rdYV+fj49OjRIyIiwtiBIITeEbosr9Bc7EACABBm4hq2vwIQfAGXUqi1rTo9yAZQPv5UwbW1qNlQz9qEpGPpzmPa8glz906tlHOOZqo7ule/b2W6sEzKWDg6sOGYix1ISX4ZDVaWvTptf3hmyeg/pj4lGwwJ37iigS8PtLml+SIbd/Nyq5CdXXpy9ta01FIGaHWe0u0TtaFYZVk56tPf/1F/NgAAaNSks31JTZ8FJs2ENuwHvtBacV9iYHlakS/lOIj5bGk6iC00krJiBipvnK0EYdPU1X1Z5v0H3Ft8PnMmK9E617J5YyfD9zkICx576DhcitDQWqAlBSqH+gK2ToidBeyiSkmZXCSyZ//BE4itFfkSpkbbYooy139/es2p/EINQFmZzMfVUHlXriYlQzg3XLmpcPbKvQ1GyWxaNlm2KbyXGwFqWbbEsqVbufjotCPnvp1//9ozNUPQxTnaCINhaXOyZHkXDgefZtegLTVzjChhwBoHOUcIIYQMMDc3r+JbrVYrl/97o/xDSK0RQrUNae5gq3iWTwOQwCjy8p+3GFMcUlP6vMlOLtNUkTzyvEXumfmJcmhsDXRGQaJS+LF3jRIMJuV44gN55tfeP08GAJrW+ibeXeTehCIpoNUaAABGrnrpjWTipaeGSBtzISHPyQOwA2DKcvNoYZg5AQAc667T+3adzpSlPvyqy9GfT/hv7c2hxJb2BXnpcgix+Xd9ipgzw/8gfv3nq0gPzv/Zu8+AJs4+AODP3YVNCIEQQFbYyFARBQTFgahAVRStizrrxv06aqtWW/dCxT2rorhxAFLAUVFRQZzI3jIDYUNIcvd+CKDFJIKz6P/3CePlnn25/z3P3aH8uP4dU9+khmO4iHwzjY6r6Girjtk2M7DvRzwCmqwpLEZIAyGytqhUQZPZVF0t01LUZAoTivkUUsYQKimupWkoNcVuGIbQO++paIkw07OvuB9xhaY731X5fNpluYrOTTPPrYNrshSqyvkkQjhCFbzGW9AVNJSUS2u4JLIgEGqoKa5UtGRibUmLTNh8+Y8ix8iErh3pWM258wYB4uy2tsVl1sy7aBa+/U779iOrSg6OP7EwwGrgFn05ORUdZnV2HonM37QjVfZy4aRk4zMTTvZTlRcVre52IvtNkhiBk6K32oato2I1a9SjVdrwKHAAAADg04NF1ACA9ofG9vZUiNj3JLmOqnoc/9c9cfSAGZgyyhPys0WI4qZfvCXrqd00W6th+pm7dxdUCmoiAxKSXGy8dBqjHKoscbL17nEnZD7GjKyKCCvquGp6bf1v9fW/1SYNcMpKC0+hkLy6mW7po4QGhETJl1OeC5u/gNFZyrS04qTmZ0wxjLycy04EphcKqNK7Dw/Haw9yV8URVZWQevlxdR2J0eQJGkZTVsYQQoS19RiLzPWrUrLrKKq+4ubl3CISiar49Rqatvo0DAmen3j+8K3HghEcDcP8vHvNDz2naY0crXpl7b07hSJEicqSM4OjeG1cv46QoOjk3sxioSjnQvwVockgB1xyWri6xyDN2EMPH/AoQX5qYFBFT0/DxpuNcSUtZnViYt17gmt5HSfbor1HUW8P074qL3bHajg7tOkiMO7obcwLe/W0DiF+yYVLheJJXDknc/fqxMBz5Xyy4dn+uCgt04FWmKy0SN7+4YE2/qnNLVZV2UA3YXPoGBLwgk9mNkbRrW9xGTXzDrIoLySihMuncBpNnkBKyjQMISSnP3aU/LnVMbcLRYhsSAlNia9GiN9QRap2tFaRR1TFvWcXXr21GpxgmhuW3b1f29TWRI8x1vy/bh94XCdEVF1RUej5nPyPehUaAAAAAN4CoTUAoB0inH/zWYDHuups6bqsobubPIYhhDDW8F6LVeL62R5yn5bF6NK4Erjy4hVLToD5yITspAc/mAUYW4dfb0BITnfpcXfD86f0GLtmPDc5sM/esPloKOIXZPDyK0gZESDFzQiLZw4e3LgSHTeyGGrHDbteTtI6zFpt+njG/q5upzfxNG3fWiCu7OG4RD/R23CrvvHpvZkUwhmT9w0ekBhmo7He3C/LPmDIHEsMISTi5u7026+rvp5lH5U7/IffxPPMhNaCMz4/ZN3oobuBaXRi7cMGAkMqXr3WmScO6XTIrW/wxiqtLm+lhRt0XjMPbeqyXoV5dEsyhRBus3DEnh6v/btupjO2dh559y637Y+Glu/gTNx10d7U5dcavyPuXnRpaWF2C3222qSPMlmv0SkizXtIoJ9q40ULguW32Cp32R5dve02CzIkPyEcIYQpdeuuUqFr2Iet4NZXvVKpQ3ddDCHRrSX7jTk7+m8pKjx90YITYOH3vERyITD1wR6HBuZN6nagR7/I1A6NL8HGmFbbjnep+vOQlto2r1MqK4/3cpKXlpa4jUW8PF5GYUPzNQPXBf37Pbhs3+1In0GRKZwOjRPOrW9xGTXzLkFl5B/BNuwNdN0jOyjHwHnaNIQQormsHrXDPndG581qrF2++7gCHGE6tquXETvd9vXo/dfoY5h9x7d+1WnsmWttcubvoKtu8jlWSyGk1LPfhbVql8bt1qRvMHC6vP8JH9aCAwAAAJ8MVl//5kUdby9dk/Z3KzeT/XdsbKyzs/NHZv1tUVFRPj4+NBqsbwegvRIKhSEhIf3792/xuXh0R0dHDxw4UPI3+Sd9AoKHzb06QQnihE+Eyt572mUtl65pujHGeygdUSUJXmZJY7LHjFf/2lkDnwGZ9Xj0D/celTX0DpxzbHjrnxgAAAAAfLuio6ONjY1bc691Zmamu7t7SEgIxKIAgPaIfJ0XVaDe20GVSnseHMfut1kR4upPBzOaOfb1zK+dC/Cl4JyuZ190/dq5AAAAANo5CK0BAO0RVV10YPyZ0UWkPENj8Nohs8whsm5nyKKsA/uzClvc60vTHLrQzl7aLcjfGjLxXMzZly2qANNwsfcf8L53vwEAAADgvwZCawBAe0RYOpxPdPjaufheYFr24RX2n3afuDZnxkrOp91ne4Nbj3T7feTXzgUAAAAAPgm4Lg4A+B4IYhaYd17xWPj+Lds3MjPAjT3+Kv9r50OyNreC4N4iS9vlcV+22ajS4PEWHA5bVdF9z+uWT9BuVRFEaVtcdCeHSX1S28cTJW901psa8alT+Ey7bYOv0uIAAADAJwGhNQDge0CY+q765QeDt97nW/t0//huHejK6iZ95l/MavupvKgg8g9f+w50RWUNoy6D/ncpt12+xkjwzzyzLiuffKFI5t1W+AQ+eREwzdHHU7KS93orSvjPz1IE0CZfttMCAAAArQULwgEA3wNct6ff6Lf+LXi8YfzyzBHhOTH69xb0Hz/9cLfw6YZtuNZIFQbNGHdKc0dk9jBjMu9xxM2qdhlZf2EtW6Ed+gaKAAAAAIDPAmatAQDtEVURu3WYNYvONO67aM4glltAJomEccttLRfdEyCEUMONWUbd/nwhQggJ7q90NuHoMpRs36zjFb64dCm3n/9cR6ZiB89Fky3vnQ8rbHxLIMU7P7WT3figQpmvfha8evBExWPCCBsNRWWWWc9xUz2NGg+n9ann/udpo8NQ09DrNGTrg1qEkHiFsM6YzXsmuXY00dc29NydIkIk987m0d301VUZeg5+ex5XidMT5oatGGyrw1BlGrnOCErmI4RQQ8RUPfuJv0zz6tnFWN/up+Np4mJQJTfWDLbRZaoz2cbdJ53IEDXVTdWjgBF2bFW6Qd9Vt8vFu61LPD69J0eDrsa28lwe9lqEEGqImGdjbD7qWM6rnV5mHI5x50U3ZC0EllQuYXbIYg9zFp2uadLbPziVjxBCgrsLzTmDJozq7eRgbeU06egr8QseJbUCQlRVwoEpPY016GraNj5rbze+brv22X4/ex01hp7jtOBMWXOTUopAVTzaOdZBX53O0O08YmNMqcyWpKqeHp7R11xLjaFp1H3soUSR1C0lFYEqOexJ7zZqqmeP7p0t7TxXRTe/bJssCPufG4epxrL22XRPeiug6rubfJ1M2Qw6Q8faa9nVnKY980Mn6fRYsvdX7y7mHF0dy7l/1yMkSD8zy9VAXU3bbtS+5/VIFom7ldKR2rBbUU7IIndzNpOpqWPec+HVYkpaWhUXxur22pYuvuBEFRwcpOV1uJD6XC2OqOIbfwy11WHQmQZOE/c/qX7zhRtLutkO2flKerMCAAAAnwqE1gCAdogf++ekLQ2z/ynkPtlAj4uplhU8yfVYE5uRem6y/luLeBvSU3L1rMwqo7Ys3xfHsDRXSE9Kbw67K/JSUnJ5ApkBGc2ye+eqC3+uOnHjRWHdW1vW3181zD/OeU9CSWXJq+ApZsSbyezK8DMpI889y8h7HbfTUwvlHZ8y8pD8wqi8iqKYxdj2n/6I5SMkehUwekKo6cYHRRW5YWNfL5+8I0kcEwhfPSOmnot58uJk3wcrA+40IIREL/ct3ie/5GFReVnug0OTbFUaH5JOVd++h/0Sk19wa0rtjj9OvSYREr3YPmHBM/fT6eXchM1Glyb5ny6ikPzAHS8zU89MNOw4NywtKyvz6dZ+8lLLK6lcZO7RGT+HW+58XlaecrznI/8pe1LFmRUVvCL8LsbGP4v4ueS36XtTRVJagSoPne/zR8noC+m80qfbjM5OXBJagZDoxfYpy7N9r+WVJR/u9OqWrIX2kotQH/P7uHW8CdfzKgrvLMT3jFsWUSV1D1R52Pyhv+cPP51UVlEYv29YB0p6s0sqgrht8i223nr0NG6P9YUpv0ZUN+445onW1qfcwpg5wu2T/rzPl9IKlFDEcv8jLKm4siR+g/6lSQvPlTXnQPg8+G/2+jspWflpYf42NDL36OzZdx3/Si3LuTwk/9ZLgfSKkb7bdztSW3YriNmy4LLJ9pdcXknGzW3DTRSkpsXo79s37fLVbBIhRBWFXXrcbbiXNvaZWpzihSzyO6zyy72iipxro/JX+q171HSViKouSE3J5PJljmYAAADgk4DQGgDQ/ghfhobzBs2abK1CMBxnTnKVa+P3KX5NrUhRWS4r8siRS3FcBWXF2praxpNvTGtKeD3/pr+BzMMj3mHCXxF/dknZO83NSNPAddrhZ9UIISSIDTpTPXLVst668ohQsx46tJtqc5pEt5+XenaQQ4jGNjdhFF49cbPjnLWjrVQJRWPf2T5kROhLoejl2aCkPktXexspEqo2U6b3zQj7O49ECCHCwtvXXgUhFQcna156RgWFECIIvL4oNTGrTKDA7uxmr92UYfleE6c5MGj0zl59O+SkZYsQmR0Zluw0dY4zk5DX85rjZ3D76p3aNlWYxHJV3r52z/SnOQN05QhN1zlTuj4JjSqiEEII1/D4cYAmhmiGw327PAm7USwlrqmOPBHCmLxmRlcmTU6nv7+fbuTVhw1kTtT1ZOcpMx3UCFW7qTMG0NuUUYSQ8HlYRPmAmVNtVXEls7H+PkTktXip4WJN1IkQ5s9/zuqmSUNyLIeRXjZtv02KZj98hJUCQnTnEYPkI8MaE1NwmzKrG4NQtpo4zYP39/VXQsmtgKm7TZ41wFJDAZPX8xw3UC01Mbt5ghVjDZ03sxMdQ5iqqYUerfKfsFizcTN7a9EUTEbPGqYn635vqbt9tyO1ZbeIIPCa/KSk3EpSWd+xpw0Dk5oWo79vn9TLV3NJRJVcD4nv5uutg32mFkeCB6GRKr7+P5oo4vTO02e4F4WHJzfWosKQExUNz9d0gbvfAAAAfH4QWgMA2h+ytKScydaSQwghTJ3Nkj7bKhmmoKJM1NeKemxMLIyYZdJQV6+sotzGN2NjTIdJm8/fS+Nyn+11Slg0futTIUKCosIyHSN9SefxmLK+gUZzGmRxfnFDwsb+1lZWVlZWdj+drRDVV1OiooKi2htLnaysrKysrLrMj8ZFteKF4piyqjiDNDk5TCgUIYQIK/+D2xwTf/e2YLHthq37p2ndM6bAYCghhBBSUFAQCYUIkTwuT0GLTccQQgjXYmsKy0or2zSNJ6lcZHkpj2Jpa+Hi2mBr0cq4ZSRCCGEMprr4U1V1BsHj8iRPQ5K8wqKazIM/2lpZWVlZ2fywJ4tqqBGRPG65sra2OLMK2roabf2ZInml5eraWuK84lraGuWlZdLmQUleYZFAz0j/o34KcXUmQ1y1DKZaeVMdKGlpqYpbjKWlXlFaRkpphbpXpxcN6W7B4XA45mP+KuAL3lwGIDoYvhXokhWlPJKtyyYQQohg67JlZlrabt/pSG3arZzr8uPLDW8u6GOkqdfNb8/jahlpMfr79k6+fC1PxL1+6WHX4d462OdqccQvK63V1GaJvyevxW5uBQAAAOBLgtAaAND+4Jpa6uXFXCFCCFHlJdzG1Z8EjRA2hhBUbU29jOBR3tTCID85tRYhhMi85FS+iZXpB85r4XQzr6nDzDNfpggQktPWYRZm50m8WxTHsTfRO66ly6YP3BaXJJaanf9ii5scrq2jrTPm6LPGT9NyX99ZZit1FhFT6zJx28X7qUVZoWPKty05nCrtflKcqcnklxSLg3SypJhL09BUa8wLhiFKxiLoRpLKhTM0mBi3qIRECCGKV1wiZGoyMYQQIksLiwQIIUSVFZWI1DXUJf/S4ExtNt1pxa1X4tImZ+bnHfVRwjW0mPyKCvHrw0TlvPdfA2hRBJypqV7R2DkQWVJUxtBkSvupw9V1tOXysvMk1RxO4CJRa27RJYsLi8nGxEqbEqPqSkrEFS7kllSoszRxia1AZR6YMS++x97YtKysrNSgn7Sxt4uL4fhbXUadpUFUlleKk6rgVcgIHkmZu21RBa3fLUKYluucvaFxWUXJJ/okLl9xtoSSnhbDw7d3Usi15+GXHnT19dbBPluLIwUNTeXSIq445w0lxZXq0pscAAAA+GzgxwcA0P7QbLw9GRH7jiXXkVWPD/4lfnIZIgxMjcoT4rNFiOL+ffFWhYwzdJrtsGH60bt3P64UFEUGHE1yGeml03Srctm5ydYdx50okHmCT2ae+3PDuYeZZXUNlWlhBy6kmnXqKI+QnNPYUSrnVm+4XdiAyOqU0Gvx1ZK/j3cYPNb5/pbVYdm1FCWoyLhzOvSlANGsR47WvbJ2853CBkQ1lCVHB0elSw3uRFk3L9xMLRdQuJyiHI4rKylIm3fHjTwGWcQe2vWAJxLkhwcG5fT0dFVu/C9NLWZhYmLZe6IZieVi9PZyTjsRGFkoEJXe3XU43m6Qu444rKy4fuDoq1qy4tGB44/tBvXTlpIxVQ8/7/TAFcFJlSQS1uQ9uhgSV07hBh5elo8uXC0gEcW9fi66/H2BVssi0Ow8PejX9xx8WUPVp5/efUng7ukg9Y4BVfdxQ3iHVuyNLxMiEe/ZpeuJTZcPCBNzw7S790veH1wLnp7ce6NY2JBz4cAVofugxsT4/xzeF19B1iUfOxipMdCzI01iK1C1VTUKhtbmTBoSFYYdDy2QEdjS3bxdUkNCMgQIVT04F5opI2efabdIlBRx9l52tQgR8gpyuJyyshwmIy2Gh6/by6Pz996zF0fWn6vFkZyTl3v1hcBzmXyy6tn+fVFaAwZaNV6Raoj+n72VdwA8xgwAAMAXAKE1AKAdUnD+7egCPMBVR7vrsurubqoYhiGEsYYvX6yyt5+ts/u0W4wu4pXKqPLiz5YcjvnII9lJu34w4xhbz7negJBc16XH1xme99ZjWM543v/Avilv3rwlqizIyMivEMo8wcdUmCh+21hnI3UVnR7/e+oU8NcCWwIhpOSy+uIO+3szOmupsSx89yUJpM6XGkw6fML39e9u+gwG28p7xfXXQoQQzWZh8J4eD/27sukM3c4jN93lyshFXfrFJQPMWAyGkfd5/TVbJxlJPaATdguPbbWJHGXC0Oi0MM37cKCfbmO0S1j5LfbJXWarq2dgsyBK+hPCJZULN5y87+CARH8bDYa53237gMNzLMXxDGHkZhLrZ8Y0GHLZbOP+2ZaElFbAmIN3np+L7fU2VadrGveZG5xajyFEWM89uEIxwL2Lk8uI0yJL7ff9TL1TBMVeq4N+YRz16EBnu27iTzu+0UtN6pcx5g87Lq1gn/vRQoPB7jz5XB5qugxAs5u59sec+aZ0VU2fYyWUtI6EEJLv7kxsdtFmdfm1xO/IOi/xrcKYes8uxQs7a7Jdd9IWHf3VSV5yKxDWU9f+VLDcsatrn/7TIjUdZL0wG9efsGu71bmh9o49vDZzTU1kLLP4TLtFZOWzozN6GjLpTIvx9513rhuuLjMthodv74w7z+yHN0bWn6vFMabPtuMTq/501lIz8DrFXnn8V6eme0SomqKMjJwyeIwZAACALwCrr3/zpo23F1hJ+7uVm8n+OzY21tnZ+SOz/raoqCgfHx8aDR5UAkB7JRQKQ0JC+vfv3+Jz8eiOjo4eOHCg5G9WnPThBA9LvzpBo413S4PPQXB3oc1UuZPPNjp+DwdkquSwl9nlMdlXxqt/7awAAAAA4BOKjo42NjZWUlKSsY1IJKqtrc3MzHR3dw8JCYFZawBAe0S+jv07rpBPofrU08Fxdv1cGBBXAwAAAACAr+Z7mFUAAHx7qOqnB8YPG10klGeYD157aJa5zFcGAQAAAAAA8FlBaA0AaI8Iy+nnE6d/7VyAd8m5bktJ8NqxLwAAIABJREFU/NqZ+GIwrSnhFVO+di4AAAAA8B8AC8IBAN+2hoipes4bk7+rJwRTxYcGqfsG17xns6ogH4b7nteSnh7NDxnH6rMz5z/zcmCqNHi8BYfDVlWUkuH3qgryUfc6IuuxcP81FSeHKqtoGZr7Hsz+wHYQvVrnaDA9UvrT6T5IK3vXW1/gHvFS9wmq+hSJ80Mn6bhuTf/PdMxvB5kZ4MYef5X/EbugeGcnWnC0VRV7/4cOHQAA8CVBaA0A+AZRJadH6Llt/zJn4GTGzj56w08Wf4morXVpYXSXWZt+dlD48HRonSdumNeH+fYN7PxLYzX6BuZ+6ipt3W4xzdHHU7KS93orStviE7eCKHFtd8OZMp6Z/kXI99n8JOXCVOnPfpcN1xm4+I8xVp94edrH965vE1V0YKD6iDO1XzsfX4akAYIxfzyWkrx/qCo8+AIA8L2CBeEAgG+PKPWv3bE95+43+RJXDxse7d+fN2zXcPYXOJ9sbVpK1kOmWX9MQoSxx8/GH7ODL+1LtkK7gTEdRk789Lv96N4FAAAAfJNg1hoA8M2pv7/vUMlI/yGaTWGWKCdkvhuHoaJh+9PxNCFCCCGq+MYfQ211GHSmgdPE/U+qEUINEVM7WPXsb67Voc+8paM6amp2mnedRyGEhLlhKwbb6jBUmUauM4KS/7VkkioPCzylMnlmb2WE/rXqmMzb3Y855iK/Mf1F7uZsJlNTx7znwqvimVWSe2fz6G766qoMPQe/PY+rGqdbBbFLOnacsXPjyO6WxnraJn7BRZTktBCZH7bU3ZipxrLyXjTZWW3YqWqEEFV0YqwFx5ClrPyvJbv8V39NdeGw1JmaHTq6r7nTYmaNLI6Y39V8yN6X9QghfthsS86/VnVS3NM/WXAsZ1ypiF3lYsLhmPZZ91gope6rgoZp2vV2M2IZD128yMtYnd1jTWwdQgjVJwfP72+hRaezzDyWXssTSd+t8Nl+v16WOup0NS2zPrNPJtVLSUpqK1CVj3aOcdDXUGeyDDr77HralFeKn/TXpO56dFVtxzlX8sVlE2aHLPYwZ9Hpmia9/YNT+Qgh4cPVPU1NPbY+Lwjys+BwOJaTz5dLTbjksCe926ipnj26d7a081wVXUIh8dpaluf8337s62hlYNJv1e1yCiGEqIrYrcOsWXSmcd9Fcwax3AIy27IEQBi33M7U2d1Gm+0w/ZcpXdlM03Gncklp1SVKCvA04+hpKOm+tSC8IWKqnv3EX6Z59exirG/XPBbeIUpa72Qy55bgrcQf/WJrtzxOIKl3CeOW25qNXDx/qJuDmb7lkB0JdQghhMiC8F/6m2qosSy8NtyvaCrpO+NOalrSehii6p7uGWmnraZu4Dr7bIYAIYREGcH+/W31NdToGkZO43fHVYrru+TGmsE2ukx1Jtu4+6QTGeKbQqSMO4kpFUX+5mHCVGNZei+d3ovRuKadqko4MKWnsQZdTdvGZ+1tLoWQKDXwBzPj7svvVIXP6cjhcMyH7ZfRtlL6p/D19dXD7fWZakwdK48VkeJ7FyjeX4MZXmsOze1na2qo08Fp9UOhtCJIOkxJaXHJNYMQVfUoYIQdW5Vu0Lep06K6xOPTe3I06GpsK8/lYa9FbRsgAADwfYHQGgDwjaHKrgae1fx5hkvz2mHhi6i0vkHJ3JQ9drdWBNxpQIjihSzyO6zyy72iipxro/JX+q17JI4/5HpveBg8LPuvRM+bLzcbnj8Vw0eiVwGjJ4SabnxQVJEbNvb18sk7kt7cuE3mnAqMtp812VrWE8oFMVsWXDbZ/pLLK8m4uW24iQJCiMw7PmXkIfmFUXkVRTGLse0//RHbHLKTuRcv1c6//irzdfY/q3rQmy4Q/Dstihey5OfzBluecwtuzqy5G98Ym2DaP51KyYzb4Cb/dg6qr635X3zv4Awerzjp2u/9dd/OLcW9seyH2SkTLwTPtFFECCl47U7O+teqTow15kRKVvK+IQzn1fcysrLSby3vKn3JEyVQH3Ysfk/3u6d5s+IfLMJOnUsQoPr7q3znPXE79KKsPPVkr3szZx3PJ6XuVqDisOjc04KKsldHezycM+NA1vsi0BY1U3D6198Lx0a95vEKngbPd2I2/dA1xN4smRiWVZKwgnl61aGXIoTI3KMzfg633Pm8rDzleM9H/lP2pIoQzXFVTHp65CI73XEnU7KyspKPjJD50mrhq3yLrbcePY3bY31hyq8R1eJaqH6cZ7U96mHi3cW03X+cek0ixI/9c9KWhtn/FHKfbKDHxVS3fek6Jeq0OObvWeTJfzqeSf6rZ8ypyDJKSnURVvPD09Kj/mfXoqGEr54RU8/FPHlxsu+DlQF3JC94J0ycHKinCW9W6VNlT5+UdXGylpPYuxAi8xKqPI/ejk8M98v/c8O1SoQoXsjiKWc6bHjKLfjHv+bOQ3H3lDDuRNLSktrDhM/vlIwKzSlLPdb9zuw5x1+TCKEGOcufj8Tm8CqyLo/MXzVp8xMhQqKX+xbvk1/ysKi8LPfBoUm2KhiSPe5aqri2bPIpnfVPSgrvzKuNeSCuK6o8dL7PHyWjL6TzSp9uMzo7cUloBSLM/a+lZT5a14vuuetVVlZW6qXpxlJPryT3T9GrgFE/XTZYE5NfUZYRvsReSdTcPxr+OXXX6WBcek5+4slxHFxyEaQepiS0uMSaQQhR1bfvYb/E5BfcmlK7Q9xpRS+2T1jwzP10ejk3YbPRpUn+p4uotg4QAAD4fkBoDQD4tpAZxwPvOM2aaPEmfCSMh08bYqAgp+fpZV+enlFBIcGD0EgVX/8fTRRxeufpM9yLwsOTRQghwtCUQzcy1tcxN9fUMDKQLy2uFr48G5TUZ+lqbyNFQtVmyvS+GWF/5zXFAYKEg/vSB/uP0JG9DJkg8Jr8pKTcSlJZ37GnDQNDZMHVEzc7zlk72kqVUDT2ne1DRoS+bJ6nU3T3/5+rJo6Qor65kbLktAQPw6NVh08foi8vpzto5hjpkUhjDlBFzsuU/BpMzbSXi1nzfbIk787KwSNDex07M7eTsqwdtB6mxDHRUzcy1jawMGMYGuvxirmk4H5QcJXviqVu2nIE08l/Sue7125XS9sBzcFvgU8nbSWcxnIZN8QkI1Ha5GqTlq2A4bioNONlWkk9TaNjH0fDph86Ofsx09y05BRNPQda5aZmChGqvH3tnulPcwboyhGarnOmdH0SGlXU5oiXZj98hJUCQnTnEYPkI8MaL3Io9hzho0cgvEP3bro5adkiJHwZGs4bNGuytQrBcJw5yVWurekghHcwMaYbmRiomlgY0g05mmXFXLKN1UVYePvaqyCk4uBkzUvPqJBcWrkuzp3S4p7WCe4ttbWYE80XPIt71dHZQeqN7phW/xF9NTAkb+loL5+Vni8Sd0/fmT4G8nI6g2aPsxV3T0njDm9jWojg+M4cZqhA0+ozc5zJvfCYGoQIK985ox31VQlcrfO4kfa5icl8hBBB4PVFqYlZZQIFdmc3e20cvWfc/VvDg6t/K4+YPdxIgcZ2nzm28SJFdeSJEMbkNTO6MmlyOv39/XQjrz5s4x35kvqnKPFcUFK/X/4YzFHCMGVjj+E9tZvPz2jm4xaNNlVECFc3N2MjiUUQST1MSWpxCTWDEEJIvtfEaQ4MGr2zV98OOWnZIkRmR4YlO02d48wk5PW85vgZ3L7acskLAACANyC0BgB8U/gP9x/IH+4/XOutYBdTU1fDEEJIXkEBEwpFCPHLSms1tVniI6C8FlutnFsmPg8lCIygEQSNQBhBI0RCoaiooKj2xlInKysrKyurLvOjcVFt0yLSyojAE7SJs/upys6TnOvy48sNby7oY6Sp181vz+NqhMji/OKGhI39ra2srKys7H46WyGqb57GxLUM9N6JLFqmxS/l1miwxUXAWY1/SKP6w/qTP8sHT3HU0+T0mhmU0hwLCJ/dSNOyLLt+Ib7yQ57/JXr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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Rebuild the design in the GUI and save a screenshot as a .png file.\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()\n",
    "gui.screenshot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "063f1f21",
   "metadata": {},
   "source": [
    "## Simulation and Analysis\n",
    "\n",
    "With our fully designed chip now laid out, we can start to focus on tuning the components to hit the circuit parameters we are interested in. These will tend to be;\n",
    "- qubit: frequency, the anharmonicity/alpha, and the coupling strength (as chi, g, or other)\n",
    "- busses: frequency (if resonant), the coupling strength\n",
    "- readout: frequency, coupling strength, coupling to external lines (as kappa, Q_external, or other)\n",
    "\n",
    "### Analysis via capacitance matrix and LOM\n",
    "\n",
    "All of the qubit paramters can initially be tuned via a capacitance matrix and the lumped oscillator method. This analysis is not as accurate as others, but allows for fairly fast and small simulations.\n",
    "\n",
    "We will start by selecting the Analysis class of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42af7aff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.analyses.quantization import LOManalysis\n",
    "\n",
    "c1 = LOManalysis(design, \"q3d\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddd204ef",
   "metadata": {},
   "source": [
    "We can see what options are available for the analysis and make any changes we may wish, such as increasing the buffer size for the bounding box when simulation subsections of the chip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31b36dcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_p = c1.sim.setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "565cbfe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'QubitTune',\n",
       " 'reuse_selected_design': True,\n",
       " 'freq_ghz': 5.0,\n",
       " 'save_fields': False,\n",
       " 'enabled': True,\n",
       " 'max_passes': 15,\n",
       " 'min_passes': 2,\n",
       " 'min_converged_passes': 2,\n",
       " 'percent_error': 0.1,\n",
       " 'percent_refinement': 30,\n",
       " 'auto_increase_solution_order': True,\n",
       " 'solution_order': 'High',\n",
       " 'solver_type': 'Iterative'}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_p.name = \"QubitTune\"\n",
    "sim_p.max_passes = 15\n",
    "sim_p.min_converged_passes = 2\n",
    "sim_p.percent_error = 0.1\n",
    "\n",
    "c1.sim.setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d1dd8cf",
   "metadata": {},
   "source": [
    "With the design and simulation setup, we render the qubit. For LOM analysis, we want the connection pads to be terminated with opens. We indicate this by stating which unconnected pins should have open terminations.\n",
    "\n",
    "*During the execution of the next cell, make sure to deal with any pop up boxes from Ansys as it opens up (if it was closed)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d4483e87",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 10:07AM [connect_project]: Connecting to Ansys Desktop API...\n",
      "INFO 10:07AM [load_ansys_project]: \tOpened Ansys App\n",
      "INFO 10:07AM [load_ansys_project]: \tOpened Ansys Desktop v2020.2.0\n",
      "INFO 10:07AM [load_ansys_project]: \tOpened Ansys Project\n",
      "\tFolder:    C:/Ansoft/\n",
      "\tProject:   Project23\n",
      "INFO 10:07AM [connect_design]: No active design found (or error getting active design).\n",
      "INFO 10:07AM [connect]: \t Connected to project \"Project23\". No design detected\n",
      "INFO 10:07AM [connect_design]: \tOpened active design\n",
      "\tDesign:    Qubit1_q3d [Solution type: Q3D]\n",
      "WARNING 10:07AM [connect_setup]: \tNo design setup detected.\n",
      "WARNING 10:07AM [connect_setup]: \tCreating Q3D default setup.\n",
      "INFO 10:07AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 10:07AM [get_setup]: \tOpened setup `QubitTune`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 10:07AM [analyze]: Analyzing setup QubitTune\n",
      "INFO 10:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpihmmvyj4.txt, C, , QubitTune:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 1, False\n",
      "INFO 10:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpwtqzabkl.txt, C, , QubitTune:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 1, False\n",
      "INFO 10:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpw29pkf6r.txt, C, , QubitTune:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 2, False\n",
      "INFO 10:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp_3iff39l.txt, C, , QubitTune:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 3, False\n",
      "INFO 10:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpdcnewiuw.txt, C, , QubitTune:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 4, False\n",
      "INFO 10:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpg4xhijml.txt, C, , QubitTune:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 5, False\n",
      "INFO 10:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpjyj_jhhn.txt, C, , QubitTune:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 6, False\n",
      "INFO 10:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpn_oauu6k.txt, C, , QubitTune:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 7, False\n",
      "INFO 10:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpmub93rqq.txt, C, , QubitTune:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 8, False\n",
      "INFO 10:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpmciuxb2g.txt, C, , QubitTune:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 9, False\n",
      "INFO 10:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmps434g9vw.txt, C, , QubitTune:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 10, False\n",
      "INFO 10:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmptwpcvf77.txt, C, , QubitTune:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 11, False\n",
      "INFO 10:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpqfysr16x.txt, C, , QubitTune:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 12, False\n",
      "INFO 10:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpsk74ngpm.txt, C, , QubitTune:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 13, False\n",
      "INFO 10:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp0pzx9126.txt, C, , QubitTune:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 14, False\n",
      "INFO 10:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpcjbs3_xh.txt, C, , QubitTune:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 15, False\n",
      "INFO 10:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp88p7cm_q.txt, C, , QubitTune:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 16, False\n"
     ]
    }
   ],
   "source": [
    "c1.sim.run(\n",
    "    name=\"Qubit1\",\n",
    "    components=[\"Q1\"],\n",
    "    open_terminations=[(\"Q1\", \"readout\"), (\"Q1\", \"bus\")],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d603c068",
   "metadata": {},
   "source": [
    "Once complete, we can grab the capacitance matrix (next cell) and/or (following cells) call on an LOM analysis of the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8fbf18dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bus_connector_pad_Q1</th>\n",
       "      <th>cl_metal_Q1</th>\n",
       "      <th>ground_main_plane</th>\n",
       "      <th>pad_bot_Q1</th>\n",
       "      <th>pad_top_Q1</th>\n",
       "      <th>readout_connector_pad_Q1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bus_connector_pad_Q1</th>\n",
       "      <td>50.94746</td>\n",
       "      <td>-0.01089</td>\n",
       "      <td>-34.51380</td>\n",
       "      <td>-13.78934</td>\n",
       "      <td>-1.64509</td>\n",
       "      <td>-0.13815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cl_metal_Q1</th>\n",
       "      <td>-0.01089</td>\n",
       "      <td>16.45248</td>\n",
       "      <td>-15.95762</td>\n",
       "      <td>-0.22648</td>\n",
       "      <td>-0.13072</td>\n",
       "      <td>-0.05207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ground_main_plane</th>\n",
       "      <td>-34.51380</td>\n",
       "      <td>-15.95762</td>\n",
       "      <td>223.34149</td>\n",
       "      <td>-39.67813</td>\n",
       "      <td>-39.72822</td>\n",
       "      <td>-34.46752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pad_bot_Q1</th>\n",
       "      <td>-13.78934</td>\n",
       "      <td>-0.22648</td>\n",
       "      <td>-39.67813</td>\n",
       "      <td>90.65573</td>\n",
       "      <td>-32.51137</td>\n",
       "      <td>-1.65418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pad_top_Q1</th>\n",
       "      <td>-1.64509</td>\n",
       "      <td>-0.13072</td>\n",
       "      <td>-39.72822</td>\n",
       "      <td>-32.51137</td>\n",
       "      <td>90.63398</td>\n",
       "      <td>-13.78691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>readout_connector_pad_Q1</th>\n",
       "      <td>-0.13815</td>\n",
       "      <td>-0.05207</td>\n",
       "      <td>-34.46752</td>\n",
       "      <td>-1.65418</td>\n",
       "      <td>-13.78691</td>\n",
       "      <td>50.95160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          bus_connector_pad_Q1  cl_metal_Q1  \\\n",
       "bus_connector_pad_Q1                  50.94746     -0.01089   \n",
       "cl_metal_Q1                           -0.01089     16.45248   \n",
       "ground_main_plane                    -34.51380    -15.95762   \n",
       "pad_bot_Q1                           -13.78934     -0.22648   \n",
       "pad_top_Q1                            -1.64509     -0.13072   \n",
       "readout_connector_pad_Q1              -0.13815     -0.05207   \n",
       "\n",
       "                          ground_main_plane  pad_bot_Q1  pad_top_Q1  \\\n",
       "bus_connector_pad_Q1              -34.51380   -13.78934    -1.64509   \n",
       "cl_metal_Q1                       -15.95762    -0.22648    -0.13072   \n",
       "ground_main_plane                 223.34149   -39.67813   -39.72822   \n",
       "pad_bot_Q1                        -39.67813    90.65573   -32.51137   \n",
       "pad_top_Q1                        -39.72822   -32.51137    90.63398   \n",
       "readout_connector_pad_Q1          -34.46752    -1.65418   -13.78691   \n",
       "\n",
       "                          readout_connector_pad_Q1  \n",
       "bus_connector_pad_Q1                      -0.13815  \n",
       "cl_metal_Q1                               -0.05207  \n",
       "ground_main_plane                        -34.46752  \n",
       "pad_bot_Q1                                -1.65418  \n",
       "pad_top_Q1                               -13.78691  \n",
       "readout_connector_pad_Q1                  50.95160  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1.sim.capacitance_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "45ae209d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 4] [5 0 1]\n",
      "Predicted Values\n",
      "\n",
      "Transmon Properties\n",
      "f_Q 5.061570 [GHz]\n",
      "EC 311.563069 [MHz]\n",
      "EJ 11.671114 [GHz]\n",
      "alpha -368.270728 [MHz]\n",
      "dispersion 122.011358 [KHz]\n",
      "Lq 13.994355 [nH]\n",
      "Cq 62.171130 [fF]\n",
      "T1 146.353071 [us]\n",
      "\n",
      "**Coupling Properties**\n",
      "\n",
      "tCqbus1 -6.070356 [fF]\n",
      "gbus1_in_MHz -91.032968 [MHz]\n",
      "χ_bus1 -1.408342 [MHz]\n",
      "1/T1bus1 1087.472253 [Hz]\n",
      "T1bus1 146.353107 [us]\n",
      "\n",
      "tCqbus2 6.068136 [fF]\n",
      "gbus2_in_MHz 1.305996 [MHz]\n",
      "χ_bus2 -0.000106 [MHz]\n",
      "1/T1bus2 0.000267 [Hz]\n",
      "T1bus2 597183178.606260 [us]\n",
      "\n",
      "tCqbus3 0.047788 [fF]\n",
      "gbus3_in_MHz 0.010286 [MHz]\n",
      "χ_bus3 -0.000000 [MHz]\n",
      "1/T1bus3 0.000000 [Hz]\n",
      "T1bus3 9627859915854.152344 [us]\n",
      "Bus-Bus Couplings\n",
      "gbus1_2 0.100231 [MHz]\n",
      "gbus1_3 0.004458 [MHz]\n",
      "gbus2_3 0.000039 [MHz]\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "        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>fQ</th>\n",
       "      <th>EC</th>\n",
       "      <th>EJ</th>\n",
       "      <th>alpha</th>\n",
       "      <th>dispersion</th>\n",
       "      <th>gbus</th>\n",
       "      <th>chi_in_MHz</th>\n",
       "      <th>χr MHz</th>\n",
       "      <th>gr MHz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.433205</td>\n",
       "      <td>363.111929</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-437.282573</td>\n",
       "      <td>412.984875</td>\n",
       "      <td>[-83.73617714546809, 1.1128526337938494, 0.021...</td>\n",
       "      <td>[-1.994068841431115, -7.989263976338063e-05, -...</td>\n",
       "      <td>1.994069</td>\n",
       "      <td>83.736177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.359559</td>\n",
       "      <td>352.526273</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-422.910894</td>\n",
       "      <td>328.817583</td>\n",
       "      <td>[-85.55836719637072, 1.2075255039009807, 0.011...</td>\n",
       "      <td>[-1.8712048806620007, -9.333269933033329e-05, ...</td>\n",
       "      <td>1.871205</td>\n",
       "      <td>85.558367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.289608</td>\n",
       "      <td>342.642825</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-409.587339</td>\n",
       "      <td>263.228965</td>\n",
       "      <td>[-86.58355838924892, 1.2287794333424544, 0.009...</td>\n",
       "      <td>[-1.7356985051848983, -9.594339235372308e-05, ...</td>\n",
       "      <td>1.735699</td>\n",
       "      <td>86.583558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.225892</td>\n",
       "      <td>333.78405</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-397.721712</td>\n",
       "      <td>213.807793</td>\n",
       "      <td>[-86.0689723014061, 1.2277846691044672, 0.0100...</td>\n",
       "      <td>[-1.5700613626258655, -9.516083595729247e-05, ...</td>\n",
       "      <td>1.570061</td>\n",
       "      <td>86.068972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.181793</td>\n",
       "      <td>327.732575</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-389.657353</td>\n",
       "      <td>184.582809</td>\n",
       "      <td>[-86.93865718651006, 1.2380032996345856, 0.010...</td>\n",
       "      <td>[-1.5083189493249503, -9.63170258686212e-05, -...</td>\n",
       "      <td>1.508319</td>\n",
       "      <td>86.938657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.143493</td>\n",
       "      <td>322.529414</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-382.749903</td>\n",
       "      <td>162.123297</td>\n",
       "      <td>[-87.53699141755507, 1.2635437523017465, 0.010...</td>\n",
       "      <td>[-1.4520753068740948, -9.994380459904714e-05, ...</td>\n",
       "      <td>1.452075</td>\n",
       "      <td>87.536991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.122549</td>\n",
       "      <td>319.704668</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-379.010078</td>\n",
       "      <td>150.892951</td>\n",
       "      <td>[-88.90295086829036, 1.2722707773971214, 0.010...</td>\n",
       "      <td>[-1.4563031695757316, -0.00010111569119596609,...</td>\n",
       "      <td>1.456303</td>\n",
       "      <td>88.902951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.103385</td>\n",
       "      <td>317.132734</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-375.611156</td>\n",
       "      <td>141.225627</td>\n",
       "      <td>[-89.0288019265413, 1.2787672658122802, 0.0100...</td>\n",
       "      <td>[-1.4235997603008703, -0.00010195477935975125,...</td>\n",
       "      <td>1.423600</td>\n",
       "      <td>89.028802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.092977</td>\n",
       "      <td>315.741024</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-373.774404</td>\n",
       "      <td>136.208482</td>\n",
       "      <td>[-89.58507857804825, 1.2841634593028393, 0.010...</td>\n",
       "      <td>[-1.421663253364802, -0.00010271006641627329, ...</td>\n",
       "      <td>1.421663</td>\n",
       "      <td>89.585079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.084037</td>\n",
       "      <td>314.548466</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-372.201855</td>\n",
       "      <td>132.025608</td>\n",
       "      <td>[-89.75013779296071, 1.2893744190101681, 0.010...</td>\n",
       "      <td>[-1.4101109966641798, -0.00010345292930883918,...</td>\n",
       "      <td>1.410111</td>\n",
       "      <td>89.750138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.078996</td>\n",
       "      <td>313.877183</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-371.317232</td>\n",
       "      <td>129.717505</td>\n",
       "      <td>[-90.3167187695716, 1.295820610268069, 0.01029...</td>\n",
       "      <td>[-1.4184853890828102, -0.00010443741877410954,...</td>\n",
       "      <td>1.418485</td>\n",
       "      <td>90.316719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5.072608</td>\n",
       "      <td>313.027676</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-370.198317</td>\n",
       "      <td>126.843837</td>\n",
       "      <td>[-90.53023682621938, 1.2988231682250144, 0.010...</td>\n",
       "      <td>[-1.413230425615815, -0.00010485521159682165, ...</td>\n",
       "      <td>1.413230</td>\n",
       "      <td>90.530237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.068138</td>\n",
       "      <td>312.434126</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-369.41691</td>\n",
       "      <td>124.866964</td>\n",
       "      <td>[-90.71509224226395, 1.3013751515724767, 0.010...</td>\n",
       "      <td>[-1.4106702883237312, -0.00010522084928401761,...</td>\n",
       "      <td>1.410670</td>\n",
       "      <td>90.715092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.065364</td>\n",
       "      <td>312.066053</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-368.932498</td>\n",
       "      <td>123.653727</td>\n",
       "      <td>[-90.92467056390035, 1.3048977251333183, 0.010...</td>\n",
       "      <td>[-1.4120265412685657, -0.00010576206087240421,...</td>\n",
       "      <td>1.412027</td>\n",
       "      <td>90.924671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.06157</td>\n",
       "      <td>311.563069</td>\n",
       "      <td>11.671114</td>\n",
       "      <td>-368.270728</td>\n",
       "      <td>122.011358</td>\n",
       "      <td>[-91.03296800373701, 1.305996499030395, 0.0102...</td>\n",
       "      <td>[-1.4083417525016215, -0.00010590030281529206,...</td>\n",
       "      <td>1.408342</td>\n",
       "      <td>91.032968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          fQ          EC         EJ       alpha  dispersion  \\\n",
       "1   5.433205  363.111929  11.671114 -437.282573  412.984875   \n",
       "2   5.359559  352.526273  11.671114 -422.910894  328.817583   \n",
       "3   5.289608  342.642825  11.671114 -409.587339  263.228965   \n",
       "4   5.225892   333.78405  11.671114 -397.721712  213.807793   \n",
       "5   5.181793  327.732575  11.671114 -389.657353  184.582809   \n",
       "6   5.143493  322.529414  11.671114 -382.749903  162.123297   \n",
       "7   5.122549  319.704668  11.671114 -379.010078  150.892951   \n",
       "8   5.103385  317.132734  11.671114 -375.611156  141.225627   \n",
       "9   5.092977  315.741024  11.671114 -373.774404  136.208482   \n",
       "10  5.084037  314.548466  11.671114 -372.201855  132.025608   \n",
       "11  5.078996  313.877183  11.671114 -371.317232  129.717505   \n",
       "12  5.072608  313.027676  11.671114 -370.198317  126.843837   \n",
       "13  5.068138  312.434126  11.671114  -369.41691  124.866964   \n",
       "14  5.065364  312.066053  11.671114 -368.932498  123.653727   \n",
       "15   5.06157  311.563069  11.671114 -368.270728  122.011358   \n",
       "\n",
       "                                                 gbus  \\\n",
       "1   [-83.73617714546809, 1.1128526337938494, 0.021...   \n",
       "2   [-85.55836719637072, 1.2075255039009807, 0.011...   \n",
       "3   [-86.58355838924892, 1.2287794333424544, 0.009...   \n",
       "4   [-86.0689723014061, 1.2277846691044672, 0.0100...   \n",
       "5   [-86.93865718651006, 1.2380032996345856, 0.010...   \n",
       "6   [-87.53699141755507, 1.2635437523017465, 0.010...   \n",
       "7   [-88.90295086829036, 1.2722707773971214, 0.010...   \n",
       "8   [-89.0288019265413, 1.2787672658122802, 0.0100...   \n",
       "9   [-89.58507857804825, 1.2841634593028393, 0.010...   \n",
       "10  [-89.75013779296071, 1.2893744190101681, 0.010...   \n",
       "11  [-90.3167187695716, 1.295820610268069, 0.01029...   \n",
       "12  [-90.53023682621938, 1.2988231682250144, 0.010...   \n",
       "13  [-90.71509224226395, 1.3013751515724767, 0.010...   \n",
       "14  [-90.92467056390035, 1.3048977251333183, 0.010...   \n",
       "15  [-91.03296800373701, 1.305996499030395, 0.0102...   \n",
       "\n",
       "                                           chi_in_MHz    χr MHz     gr MHz  \n",
       "1   [-1.994068841431115, -7.989263976338063e-05, -...  1.994069  83.736177  \n",
       "2   [-1.8712048806620007, -9.333269933033329e-05, ...  1.871205  85.558367  \n",
       "3   [-1.7356985051848983, -9.594339235372308e-05, ...  1.735699  86.583558  \n",
       "4   [-1.5700613626258655, -9.516083595729247e-05, ...  1.570061  86.068972  \n",
       "5   [-1.5083189493249503, -9.63170258686212e-05, -...  1.508319  86.938657  \n",
       "6   [-1.4520753068740948, -9.994380459904714e-05, ...  1.452075  87.536991  \n",
       "7   [-1.4563031695757316, -0.00010111569119596609,...  1.456303  88.902951  \n",
       "8   [-1.4235997603008703, -0.00010195477935975125,...  1.423600  89.028802  \n",
       "9   [-1.421663253364802, -0.00010271006641627329, ...  1.421663  89.585079  \n",
       "10  [-1.4101109966641798, -0.00010345292930883918,...  1.410111  89.750138  \n",
       "11  [-1.4184853890828102, -0.00010443741877410954,...  1.418485  90.316719  \n",
       "12  [-1.413230425615815, -0.00010485521159682165, ...  1.413230  90.530237  \n",
       "13  [-1.4106702883237312, -0.00010522084928401761,...  1.410670  90.715092  \n",
       "14  [-1.4120265412685657, -0.00010576206087240421,...  1.412027  90.924671  \n",
       "15  [-1.4083417525016215, -0.00010590030281529206,...  1.408342  91.032968  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1.setup.junctions = Dict({\"Lj\": 14, \"Cj\": 2})\n",
    "c1.setup.freq_readout = 7.0\n",
    "c1.setup.freq_bus = [0.1, 0.1]  # list of the bus frequencies\n",
    "\n",
    "c1.run_lom()\n",
    "c1.lumped_oscillator_all"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31d96295",
   "metadata": {},
   "source": [
    "We can finally observe how the main quantum parameters have converged after the previous simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "96f2e991",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 10:09AM [hfss_report_full_convergence]: Creating report for variation 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Design \"Qubit1_q3d\" info:\n",
      "\t# eigenmodes    0\n",
      "\t# variations    1\n"
     ]
    }
   ],
   "source": [
    "c1.plot_convergence()\n",
    "c1.plot_convergence_chi()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4387b26c",
   "metadata": {},
   "source": [
    "Note that it is possible for a (physically) converged simulation, to not have quite converged in the quantum realm.\n",
    "\n",
    "Looking at the convergence plots from the previous cell, we can see that we did not have enough passes for the simulation. In this case, we want to modify our simulation setup, 'QubitTune', to improve our convergence (increase number of passes, or changing some other parameters)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f33a64e",
   "metadata": {},
   "source": [
    "Once we will know that simulation has converged, we can use those results to make appropriate changes to our qubit layout. Say our anharmonicity/alpha is higher than we want? Since we know Ec~ 1/C, we can change parts of the transmon pocket to lower the total capacitance seen across the junction.\n",
    "If g to the bus is too low, we can increase the width of the connection pad so that the capacitance between the connection pad and charge island is greater.\n",
    "\n",
    "One must also keep in mind that any such changes will impact the other parameters, so careful tweaks and iterations of simulation/analysis often end up being necessary.\n",
    "\n",
    "If wanting to make changes and re-render your design, you first should clear your current design."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a224231",
   "metadata": {},
   "outputs": [],
   "source": [
    "c1.sim.renderer.clean_active_design()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7e56700",
   "metadata": {},
   "source": [
    "When done with all the simulations, you can disconnect from Ansys EDT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5363cc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "c1.sim.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd1840f3",
   "metadata": {},
   "source": [
    "### Analysis via eigenmode and EPR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2687d515",
   "metadata": {},
   "source": [
    "Now, although the previous simulation and analysis captured all of the parameters of the qubits and coupling to the bus, a more accurate (all be it slower) approach is to render the qubits and their coupling into an eigenmode simulation and perform EPR analysis on the result.\n",
    "\n",
    "We again start by selecting the Analysis class of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa028ec7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.analyses.quantization import EPRanalysis\n",
    "\n",
    "eig_qb = EPRanalysis(design, \"hfss\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "459e241e",
   "metadata": {},
   "source": [
    "Review and update the simulation setup by executing following two cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "432a7e87",
   "metadata": {},
   "outputs": [],
   "source": [
    "em_p = eig_qb.sim.setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d804e8b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'QubitTune',\n",
       " 'reuse_selected_design': True,\n",
       " 'min_freq_ghz': 3,\n",
       " 'n_modes': 2,\n",
       " 'max_delta_f': 0.1,\n",
       " 'max_passes': 12,\n",
       " 'min_passes': 1,\n",
       " 'min_converged': 2,\n",
       " 'pct_refinement': 30,\n",
       " 'basis_order': 1,\n",
       " 'vars': {'Lj1': '14 nH', 'Cj1': '0 fF', 'Lj2': '12 nH', 'Cj2': '0 fF'}}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "em_p.name = \"QubitTune\"\n",
    "em_p.min_freq_ghz = 3\n",
    "em_p.n_modes = 2\n",
    "em_p.max_passes = 12\n",
    "em_p.max_delta_f = 0.1\n",
    "em_p.min_converged = 2\n",
    "# Design variables can also be added in for direct simulation sweeps.\n",
    "em_p.vars = Dict({\"Lj1\": \"14 nH\", \"Cj1\": \"0 fF\", \"Lj2\": \"12 nH\", \"Cj2\": \"0 fF\"})\n",
    "\n",
    "eig_qb.sim.setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e254c357",
   "metadata": {},
   "source": [
    "We can now run the simulation on the specified layout."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d830d0e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 10:10AM [connect_project]: Connecting to Ansys Desktop API...\n",
      "INFO 10:10AM [load_ansys_project]: \tOpened Ansys App\n",
      "INFO 10:10AM [load_ansys_project]: \tOpened Ansys Desktop v2020.2.0\n",
      "INFO 10:10AM [load_ansys_project]: \tOpened Ansys Project\n",
      "\tFolder:    C:/Ansoft/\n",
      "\tProject:   Project23\n",
      "INFO 10:10AM [connect_design]: \tOpened active design\n",
      "\tDesign:    Qubit1_q3d [Solution type: Q3D]\n",
      "INFO 10:10AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 10:10AM [connect]: \tConnected to project \"Project23\" and design \"Qubit1_q3d\" 😀 \n",
      "\n",
      "INFO 10:10AM [connect_design]: \tOpened active design\n",
      "\tDesign:    Q1_Q2_Bus_hfss [Solution type: Eigenmode]\n",
      "WARNING 10:10AM [connect_setup]: \tNo design setup detected.\n",
      "WARNING 10:10AM [connect_setup]: \tCreating eigenmode default setup.\n",
      "INFO 10:10AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.HfssEMSetup'>)\n",
      "INFO 10:10AM [get_setup]: \tOpened setup `QubitTune`  (<class 'pyEPR.ansys.HfssEMSetup'>)\n",
      "INFO 10:10AM [analyze]: Analyzing setup QubitTune\n",
      "10:13AM 30s INFO [get_f_convergence]: Saved convergences to C:\\workspace\\qiskit-metal\\docs\\circuit-examples\\full-design-flow-examples\\hfss_eig_f_convergence.csv\n"
     ]
    }
   ],
   "source": [
    "eig_qb.sim.run(\n",
    "    name=\"Q1_Q2_Bus\", components=[\"Bus_Q1_Q2\", \"Q1\", \"Q2\"], open_terminations=[]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b781c36f",
   "metadata": {},
   "source": [
    "Once completed, we can check the convergence to see if more passes might be necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7e5e7f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "eig_qb.sim.plot_convergences()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aee59e3e",
   "metadata": {},
   "source": [
    "This eigenmode simulation is also a quick method to check for any bus or readout resonator frequencies. \n",
    "If happy with the simulation, we can then jump to some EPR analysis.\n",
    "\n",
    "At first we need to define the junctions in terms of name, inductance_variable, capacitance_variable, rectancle that was defined in the rendering to represent the junction `port` and line that was defined in the rendering to represent the direction of the current thorugh the junction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b6b4045c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'junctions': {'jj1': {'Lj_variable': 'Lj1',\n",
       "   'Cj_variable': 'Cj1',\n",
       "   'rect': 'JJ_rect_Lj_Q1_rect_jj',\n",
       "   'line': 'JJ_Lj_Q1_rect_jj_'},\n",
       "  'jj2': {'Lj_variable': 'Lj2',\n",
       "   'Cj_variable': 'Cj2',\n",
       "   'rect': 'JJ_rect_Lj_Q2_rect_jj',\n",
       "   'line': 'JJ_Lj_Q2_rect_jj_'}},\n",
       " 'dissipatives': {'dielectrics_bulk': ['main']},\n",
       " 'cos_trunc': 8,\n",
       " 'fock_trunc': 7,\n",
       " 'sweep_variable': 'Lj1'}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eig_qb.del_junction()\n",
    "eig_qb.add_junction(\n",
    "    \"jj1\", \"Lj1\", \"Cj1\", rect=\"JJ_rect_Lj_Q1_rect_jj\", line=\"JJ_Lj_Q1_rect_jj_\"\n",
    ")\n",
    "eig_qb.add_junction(\n",
    "    \"jj2\", \"Lj2\", \"Cj2\", rect=\"JJ_rect_Lj_Q2_rect_jj\", line=\"JJ_Lj_Q2_rect_jj_\"\n",
    ")\n",
    "eig_qb.setup.sweep_variable = \"Lj1\"\n",
    "eig_qb.setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa5999af",
   "metadata": {},
   "source": [
    "Note in the previous cell output that the dissipatives have alrady been defined by default.\n",
    "\n",
    "Now we can start looking at the EPR values. First we look at the electric field and subtrate participation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a9de55ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Design \"Q1_Q2_Bus_hfss\" info:\n",
      "\t# eigenmodes    2\n",
      "\t# variations    1\n",
      "Design \"Q1_Q2_Bus_hfss\" info:\n",
      "\t# eigenmodes    2\n",
      "\t# variations    1\n",
      "\n",
      "        energy_elec_all       = 8.21441028255477e-25\n",
      "        energy_elec_substrate = 7.56817871884e-25\n",
      "        EPR of substrate = 92.1%\n",
      "\n",
      "        energy_mag    = 3.16335953678474e-27\n",
      "        energy_mag % of energy_elec_all  = 0.4%\n",
      "        \n",
      "\n",
      "Variation 0  [1/1]\n",
      "\n",
      "  \u001b[1mMode 0 at 5.35 GHz   [1/2]\u001b[0m\n",
      "    Calculating ℰ_magnetic,ℰ_electric\n",
      "       (ℰ_E-ℰ_H)/ℰ_E       ℰ_E       ℰ_H\n",
      "               99.6%  4.107e-25 1.582e-27\n",
      "\n",
      "    Calculating junction energy participation ration (EPR)\n",
      "\tmethod=`line_voltage`. First estimates:\n",
      "\tjunction        EPR p_0j   sign s_0j    (p_capacitive)\n",
      "\t\tEnergy fraction (Lj over Lj&Cj)= 96.93%\n",
      "\tjj1             0.995427  (+)        0.0315155\n",
      "\t\tEnergy fraction (Lj over Lj&Cj)= 97.36%\n",
      "\tjj2             3.30364e-05  (+)        8.96519e-07\n",
      "\t\t(U_tot_cap-U_tot_ind)/mean=1.59%\n",
      "Calculating Qdielectric_main for mode 0 (0/1)\n",
      "p_dielectric_main_0 = 0.9213295243984593\n",
      "\n",
      "  \u001b[1mMode 1 at 5.78 GHz   [2/2]\u001b[0m\n",
      "    Calculating ℰ_magnetic,ℰ_electric\n",
      "       (ℰ_E-ℰ_H)/ℰ_E       ℰ_E       ℰ_H\n",
      "               99.6%  7.231e-25  3.25e-27\n",
      "\n",
      "    Calculating junction energy participation ration (EPR)\n",
      "\tmethod=`line_voltage`. First estimates:\n",
      "\tjunction        EPR p_1j   sign s_1j    (p_capacitive)\n",
      "\t\tEnergy fraction (Lj over Lj&Cj)= 96.43%\n",
      "\tjj1             3.38351e-05  (+)        1.25157e-06\n",
      "\t\tEnergy fraction (Lj over Lj&Cj)= 96.93%\n",
      "\tjj2             0.994201  (+)        0.0315222\n",
      "\t\t(U_tot_cap-U_tot_ind)/mean=1.62%\n",
      "Calculating Qdielectric_main for mode 1 (1/1)\n",
      "p_dielectric_main_1 = 0.9213896904120465\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING 10:13AM [__init__]: <p>Error: <class 'IndexError'></p>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ANALYSIS DONE. Data saved to:\n",
      "\n",
      "C:\\data-pyEPR\\Project23\\Q1_Q2_Bus_hfss\\2021-07-20 10-13-32.npz\n",
      "\n",
      "\n",
      "\t Differences in variations:\n",
      "\n",
      "\n",
      "\n",
      " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "Variation 0\n",
      "\n",
      "Starting the diagonalization\n",
      "Finished the diagonalization\n",
      "Pm_norm=\n",
      "modes\n",
      "0    1.032291\n",
      "1    1.032911\n",
      "dtype: float64\n",
      "\n",
      "Pm_norm idx =\n",
      "     jj1    jj2\n",
      "0   True  False\n",
      "1  False   True\n",
      "*** P (participation matrix, not normlz.)\n",
      "        jj1       jj2\n",
      "0  0.965013  0.000032\n",
      "1  0.000033  0.963818\n",
      "\n",
      "*** S (sign-bit matrix)\n",
      "   s_jj1  s_jj2\n",
      "0      1      1\n",
      "1      1      1\n",
      "*** P (participation matrix, normalized.)\n",
      "         1  3.2e-05\n",
      "   3.3e-05        1\n",
      "\n",
      "*** Chi matrix O1 PT (MHz)\n",
      "    Diag is anharmonicity, off diag is full cross-Kerr.\n",
      "       304   0.0398\n",
      "    0.0398      304\n",
      "\n",
      "*** Chi matrix ND (MHz) \n",
      "       345    0.696\n",
      "     0.696      341\n",
      "\n",
      "*** Frequencies O1 PT (MHz)\n",
      "0    5047.467672\n",
      "1    5480.401018\n",
      "dtype: float64\n",
      "\n",
      "*** Frequencies ND (MHz)\n",
      "0    5027.574497\n",
      "1    5462.263203\n",
      "dtype: float64\n",
      "\n",
      "*** Q_coupling\n",
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: [0, 1]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "#### Mode frequencies (MHz)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "###### Numerical diagonalization"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "    <tr>\n",
       "      <th>eigenmode</th>\n",
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       "Lj1             14\n",
       "eigenmode         \n",
       "0          5047.47\n",
       "1          5480.40"
      ]
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     "metadata": {},
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    },
    {
     "data": {
      "text/markdown": [
       "#### Kerr Non-linear coefficient table (MHz)"
      ],
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       "<IPython.core.display.Markdown object>"
      ]
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     "metadata": {},
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    {
     "data": {
      "text/markdown": [
       "###### Numerical diagonalization"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
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       "            0       1\n",
       "Lj1                  \n",
       "14  0  344.93    0.70\n",
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   ],
   "source": [
    "eig_qb.run_epr()\n",
    "# (pyEPR allows to switch modes: eprd.set_mode(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aeb553d",
   "metadata": {},
   "source": [
    "From the analysis results we can determine the qubits anharmonicities and coupling strength."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c56722a3",
   "metadata": {},
   "source": [
    "Other analysis is still being added, such as the impedance analysis, though some already be done manually by renderering to a driven modal simulation and performing frequency sweeps to extract the S-Parameters or Impedance matrix. This would be an easy way to, say, determine the external quality factor of a readout resonator. *See tutorial notebooks 4.3*\n",
    "\n",
    "Once the analysis and tuning is complete, we can disconnect from the renderer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e6f2df2",
   "metadata": {},
   "outputs": [],
   "source": [
    "eig_qb.sim.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b396ac8",
   "metadata": {},
   "source": [
    "### Rendering to a GDS File"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1f9f3ab",
   "metadata": {},
   "source": [
    "Once all of the tuning is complete, we will want to prepare a GDS file so we can create a mask and fabricate our chip. We first create a gds render instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b884024c",
   "metadata": {},
   "outputs": [],
   "source": [
    "qhk21_gds = design.renderers.gds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9b8175e",
   "metadata": {},
   "source": [
    "The various options for the gds renderer can be checked and changed as necessary. A key option is the gds file which holds the cells for your junction ebeam design. Make sure this is pointing at the correct file so they are placed in your final mask at the appropriate locations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "deb46e61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'short_segments_to_not_fillet': 'True',\n",
       " 'check_short_segments_by_scaling_fillet': '2.0',\n",
       " 'gds_unit': 0.001,\n",
       " 'ground_plane': 'True',\n",
       " 'negative_mask': {'main': []},\n",
       " 'corners': 'circular bend',\n",
       " 'tolerance': '0.00001',\n",
       " 'precision': '0.000000001',\n",
       " 'width_LineString': '10um',\n",
       " 'path_filename': '../resources/Fake_Junctions.GDS',\n",
       " 'junction_pad_overlap': '5um',\n",
       " 'max_points': '199',\n",
       " 'cheese': {'datatype': '100',\n",
       "  'shape': '0',\n",
       "  'cheese_0_x': '25um',\n",
       "  'cheese_0_y': '25um',\n",
       "  'cheese_1_radius': '100um',\n",
       "  'view_in_file': {'main': {1: True}},\n",
       "  'delta_x': '100um',\n",
       "  'delta_y': '100um',\n",
       "  'edge_nocheese': '200um'},\n",
       " 'no_cheese': {'datatype': '99',\n",
       "  'buffer': '25um',\n",
       "  'cap_style': '2',\n",
       "  'join_style': '2',\n",
       "  'view_in_file': {'main': {1: True}}},\n",
       " 'bounding_box_scale_x': '1.2',\n",
       " 'bounding_box_scale_y': '1.2'}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qhk21_gds.options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e58ef29d",
   "metadata": {},
   "outputs": [],
   "source": [
    "qhk21_gds.options[\"path_filename\"] = \"../../tut/resources/Fake_Junctions.GDS\"\n",
    "qhk21_gds.options[\"no_cheese\"][\"buffer\"] = \"50um\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "141edb57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qhk21_gds.export_to_gds(\"QHK21_Tutorial.gds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "423d6c0c",
   "metadata": {},
   "source": [
    "(optional) Now that the design is finished, we can close the GUI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2c1269c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# gui.main_window.close()"
   ]
  }
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