{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Defining and using symmetries\n",
        "\n",
        "In most FDTD simulations, you can use field symmetries to significantly\n",
        "reduce computation by 1/2, 1/4, or even 1/8, potentially saving\n",
        "significant time. Symmetries are also useful to filter out undesired\n",
        "light polarizations. To correctly set up the symmetries in a simulation,\n",
        "it is necessary to consider the geometry of structures and source\n",
        "planes, as well as the vectorial nature of the fields. This notebook\n",
        "will give a tutorial on setting up symmetries in\n",
        "[Tidy3D](https://www.flexcompute.com/tidy3d/solver/). This\n",
        "[notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/PhotonicCrystalWaveguidePolarizationFilter/)\n",
        "shows a good example on how to set up symmetries to select between\n",
        "different waveguide modes.\n",
        "\n",
        "If you are new to the finite-difference time-domain (FDTD) method, we\n",
        "highly recommend going through our\n",
        "[FDTD101](https://www.flexcompute.com/tidy3d/learning-center/fdtd101/)\n",
        "tutorials."
      ],
      "id": "b18b15e3-05e1-4279-ab02-35f7a2769301"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "# standard python imports\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# tidy3d imports\n",
        "import tidy3d as td\n",
        "import tidy3d.web as web\n",
        "from tidy3d.plugins.mode import ModeSolver"
      ],
      "id": "f46a7ccc"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## How Can I Set Symmetry?\n",
        "\n",
        "You should set the `symmetry` parameter in your\n",
        "[Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html)\n",
        "object using a tuple of integers defining reflection symmetry across a\n",
        "plane bisecting the simulation domain normal to the `x`-, `y`-, and\n",
        "`z`-axis. Each element can be **0** (*no symmetry*), **1** (*even,\n",
        "i.e. ‘PMC’ symmetry*) or **-1** (*odd, i.e. ‘PEC’ symmetry*). Note that\n",
        "the vectorial nature of the fields must be considered to determine the\n",
        "symmetry value correctly.\n",
        "\n",
        "The figure below illustrates how the electric and magnetic field\n",
        "components transform under PEC- and PMC-like symmetry planes. You can\n",
        "refer to this figure when considering whether a source field conforms to\n",
        "a PEC- or PMC-like symmetry axis. This would be helpful, especially when\n",
        "dealing with optical waveguide modes.\n",
        "\n",
        "<img src=\"img/pec_pmc.png\" alt=\"PEC and PMC Boundary Conditions\"  width=\"400\" align=\"center\"/>\n",
        "\n",
        "We can refer to this figure in the following examples to properly adjust\n",
        "symmetry.\n",
        "\n",
        "## Symmetry Example 1: Reflectance From a Metasurface\n",
        "\n",
        "In this first example, we will build a dielectric metasurface and\n",
        "calculate its reflectance. A plane wave source will illuminate the\n",
        "structure at the normal direction."
      ],
      "id": "cd1712ca-c2db-4a0c-865b-e530ec9c203d"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Simulation wavelength\n",
        "wl_min = 0.4\n",
        "wl_max = 0.8\n",
        "n_wl = 201\n",
        "wl_c = (wl_min + wl_max) / 2\n",
        "wl_range = np.linspace(wl_min, wl_max, n_wl)\n",
        "freq_c = td.C_0 / wl_c\n",
        "freq_range = td.C_0 / wl_range\n",
        "freq_bw = 0.5 * (freq_range[0] - freq_range[-1])\n",
        "run_time = 1e-12\n",
        "\n",
        "# Geometry\n",
        "p = 0.35\n",
        "t = 0.27\n",
        "w = 0.255\n",
        "sub_t = 2\n",
        "clad_t = 2\n",
        "\n",
        "# Material\n",
        "n = 2.00\n",
        "ns = 1.46\n",
        "mat_brick = td.Medium(permittivity=n**2)\n",
        "mat_sub = td.Medium(permittivity=ns**2)"
      ],
      "id": "5e04d68e"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The metasurface structure is composed of a periodic pattern of\n",
        "dielectric nanoresonators. First, we will use no symmetry."
      ],
      "id": "6f65e58a-cd43-4f59-ba04-103ca9166306"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Simulation size.\n",
        "size_z = sub_t + t + clad_t\n",
        "_inf = 10\n",
        "\n",
        "# Substrate.\n",
        "sub_box = td.Structure(\n",
        "    geometry=td.Box.from_bounds(rmin=(-_inf, -_inf, -_inf), rmax=(_inf, _inf, -size_z / 2 + sub_t)),\n",
        "    medium=mat_sub,\n",
        ")\n",
        "\n",
        "# Nanoresonator.\n",
        "res_box = td.Structure(\n",
        "    geometry=td.Box(center=(0, 0, -size_z / 2 + sub_t + t / 2), size=(w, w, t)),\n",
        "    medium=mat_brick,\n",
        ")\n",
        "\n",
        "# Create a plane wave excitation source.\n",
        "plane_wave = td.PlaneWave(\n",
        "    source_time=td.GaussianPulse(freq0=freq_c, fwidth=freq_bw),\n",
        "    size=(td.inf, td.inf, 0),\n",
        "    center=(0, 0, size_z / 2 - wl_c / 2),\n",
        "    direction=\"-\",\n",
        "    pol_angle=0,\n",
        "    angle_theta=0,\n",
        ")\n",
        "\n",
        "# Create a flux monitor to measure the reflectance.\n",
        "ref_monitor = td.FluxMonitor(\n",
        "    center=(0, 0, size_z / 2 - wl_c / 3),\n",
        "    size=(td.inf, td.inf, 0),\n",
        "    freqs=freq_range,\n",
        "    name=\"R\",\n",
        ")\n",
        "\n",
        "# Simulation.\n",
        "sim_met_no_sym = td.Simulation(\n",
        "    size=(p, p, size_z),\n",
        "    center=(0, 0, 0),\n",
        "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=40, wavelength=(wl_min + wl_max) / 2),\n",
        "    structures=[sub_box, res_box],\n",
        "    sources=[plane_wave],\n",
        "    monitors=[ref_monitor],\n",
        "    run_time=run_time,\n",
        "    boundary_spec=td.BoundarySpec(\n",
        "        x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
        "    ),\n",
        "    symmetry=(0, 0, 0),\n",
        ")"
      ],
      "id": "9baeac01"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let’s look at the metasurface unit cell before running the simulation to\n",
        "ensure all the components are in their correct places. You can observe\n",
        "that both the structure and source plane have mirror symmetry about the\n",
        "`x`- and `y`-axis."
      ],
      "id": "9c7c3869-7f2a-4728-a8b1-3285c667acb5"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
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              "    <script>\n",
              "        \n",
              "        /**\n",
              "        * Simulation Viewer Injector\n",
              "        *\n",
              "        * Monitors the document for elements being added in the form:\n",
              "        *\n",
              "        *    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"{...}\" />\n",
              "        *\n",
              "        * This script will then inject an iframe to the viewer application, and pass it the simulation data\n",
              "        * via the postMessage API on request. The script may be safely included multiple times, with only the\n",
              "        * configuration of the first started script (e.g. viewer URL) applying.\n",
              "        *\n",
              "        */\n",
              "        (function() {\n",
              "            const TARGET_CLASS = \"simulation-viewer\";\n",
              "            const ACTIVE_CLASS = \"simulation-viewer-active\";\n",
              "            const VIEWER_URL = \"https://tidy3d.simulation.cloud/simulation-viewer\";\n",
              "\n",
              "            class SimulationViewerInjector {\n",
              "                constructor() {\n",
              "                    for (var node of document.getElementsByClassName(TARGET_CLASS)) {\n",
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              "                    this.observer.observe(document.body, {childList: true, subtree: true});\n",
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              "\n",
              "                onMutations(mutations) {\n",
              "                    for (var mutation of mutations) {\n",
              "                        if (mutation.type === 'childList') {\n",
              "                            /**\n",
              "                            * Have found that adding the element does not reliably trigger the mutation observer.\n",
              "                            * It may be the case that setting content with innerHTML does not trigger.\n",
              "                            *\n",
              "                            * It seems to be sufficient to re-scan the document for un-activated viewers\n",
              "                            * whenever an event occurs, as Jupyter triggers multiple events on cell evaluation.\n",
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              "                            var viewers = document.getElementsByClassName(TARGET_CLASS);\n",
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              "                        frame.src = VIEWER_URL + \"?uuid=\" + uuid;\n",
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          }
        }
      ],
      "source": [
        "sim_met_no_sym.plot_3d(width=400, height=400)"
      ],
      "id": "436fad4e"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We will run the simulation with `verbose=False` to reduce the amount of\n",
        "output information."
      ],
      "id": "b891ff73-b2cb-4caa-8321-b09e95c9be3a"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "job_no_sym = web.Job(simulation=sim_met_no_sym, task_name=\"sim_met_no_symmetry\", verbose=False)\n",
        "sim_data_no_sym = job_no_sym.run(path=\"data/simulation_data.hdf5\")"
      ],
      "id": "d84b72be"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We should now consider the source field components concerning the\n",
        "symmetry axis to correctly include symmetry conditions. Both the\n",
        "electric field `Ex` and the magnetic field `Hy` should be continuous at\n",
        "the symmetry plane perpendicular to the `x`-direction, so we will set\n",
        "the symmetry to **-1** (*odd, i.e. ‘PEC’ symmetry*) in the\n",
        "`x`-direction. These field components also should be continuous with\n",
        "respect to the symmetry plane at the `y`-direction, but in this case,\n",
        "the `Ex` field component is parallel, and `Hy` is perpendicular to that\n",
        "symmetry plane. This case conforms to a PMC-like symmetry plane (as one\n",
        "can observe in the schematic “PEC PMC” figure), which we will obtain by\n",
        "adjusting the symmetry to **1** (*even, i.e. ‘PMC’ symmetry*) in the\n",
        "`y`-direction.\n",
        "\n",
        "We can modify the previous simulation and change the symmetry condition\n",
        "to `(-1, 1, 0)`. After that, we can plot the simulation set up again. If\n",
        "you enable the symmetry visualization, you will see the “PEC” domain\n",
        "highlighted in red and the “PMC” region in blue."
      ],
      "id": "59447a0a-121b-4678-9fa2-98583c029bde"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "\n",
              "    <div class=\"simulation-viewer\" data-width=\"400\" data-height=\"400\" data-simulation=\"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\" ></div>\n",
              "    <script>\n",
              "        \n",
              "        /**\n",
              "        * Simulation Viewer Injector\n",
              "        *\n",
              "        * Monitors the document for elements being added in the form:\n",
              "        *\n",
              "        *    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"{...}\" />\n",
              "        *\n",
              "        * This script will then inject an iframe to the viewer application, and pass it the simulation data\n",
              "        * via the postMessage API on request. The script may be safely included multiple times, with only the\n",
              "        * configuration of the first started script (e.g. viewer URL) applying.\n",
              "        *\n",
              "        */\n",
              "        (function() {\n",
              "            const TARGET_CLASS = \"simulation-viewer\";\n",
              "            const ACTIVE_CLASS = \"simulation-viewer-active\";\n",
              "            const VIEWER_URL = \"https://tidy3d.simulation.cloud/simulation-viewer\";\n",
              "\n",
              "            class SimulationViewerInjector {\n",
              "                constructor() {\n",
              "                    for (var node of document.getElementsByClassName(TARGET_CLASS)) {\n",
              "                        this.injectViewer(node);\n",
              "                    }\n",
              "\n",
              "                    // Monitor for newly added nodes to the DOM\n",
              "                    this.observer = new MutationObserver(this.onMutations.bind(this));\n",
              "                    this.observer.observe(document.body, {childList: true, subtree: true});\n",
              "                }\n",
              "\n",
              "                onMutations(mutations) {\n",
              "                    for (var mutation of mutations) {\n",
              "                        if (mutation.type === 'childList') {\n",
              "                            /**\n",
              "                            * Have found that adding the element does not reliably trigger the mutation observer.\n",
              "                            * It may be the case that setting content with innerHTML does not trigger.\n",
              "                            *\n",
              "                            * It seems to be sufficient to re-scan the document for un-activated viewers\n",
              "                            * whenever an event occurs, as Jupyter triggers multiple events on cell evaluation.\n",
              "                            */\n",
              "                            var viewers = document.getElementsByClassName(TARGET_CLASS);\n",
              "                            for (var node of viewers) {\n",
              "                                this.injectViewer(node);\n",
              "                            }\n",
              "                        }\n",
              "                    }\n",
              "                }\n",
              "\n",
              "                injectViewer(node) {\n",
              "                    // (re-)check that this is a valid simulation container and has not already been injected\n",
              "                    if (node.classList.contains(TARGET_CLASS) && !node.classList.contains(ACTIVE_CLASS)) {\n",
              "                        // Mark node as injected, to prevent re-runs\n",
              "                        node.classList.add(ACTIVE_CLASS);\n",
              "\n",
              "                        var uuid;\n",
              "                        if (window.crypto && window.crypto.randomUUID) {\n",
              "                            uuid = window.crypto.randomUUID();\n",
              "                        } else {\n",
              "                            uuid = \"\" + Math.random();\n",
              "                        }\n",
              "\n",
              "                        var frame = document.createElement(\"iframe\");\n",
              "                        frame.width = node.dataset.width || 800;\n",
              "                        frame.height = node.dataset.height || 800;\n",
              "                        frame.style.cssText = `width:${frame.width}px;height:${frame.height}px;max-width:none;border:0;display:block`\n",
              "                        frame.src = VIEWER_URL + \"?uuid=\" + uuid;\n",
              "\n",
              "                        var postMessageToViewer;\n",
              "                        postMessageToViewer = event => {\n",
              "                            if(event.data.type === 'viewer' && event.data.uuid===uuid){\n",
              "                                frame.contentWindow.postMessage({ type: 'jupyter', uuid, value: node.dataset.simulation, fileType: 'hdf5'}, '*');\n",
              "\n",
              "                                // Run once only\n",
              "                                window.removeEventListener('message', postMessageToViewer);\n",
              "                            }\n",
              "                        };\n",
              "                        window.addEventListener(\n",
              "                            'message',\n",
              "                            postMessageToViewer,\n",
              "                            false\n",
              "                        );\n",
              "\n",
              "                        node.appendChild(frame);\n",
              "                    }\n",
              "                }\n",
              "            }\n",
              "\n",
              "            if (!window.simulationViewerInjector) {\n",
              "                window.simulationViewerInjector = new SimulationViewerInjector();\n",
              "            }\n",
              "        })();\n",
              "    \n",
              "    </script>\n",
              "    "
            ]
          }
        }
      ],
      "source": [
        "sim_met_sym = sim_met_no_sym.copy(update={\"symmetry\": (-1, 1, 0)})\n",
        "sim_met_sym.plot_3d(width=400, height=400)"
      ],
      "id": "f14067cc"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "job_sym = web.Job(simulation=sim_met_sym, task_name=\"sim_met_no_symmetry\", verbose=False)\n",
        "sim_data_sym = job_sym.run(path=\"data/simulation_data.hdf5\")"
      ],
      "id": "1ccdc045"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Lastly, we should compare the reflectance spectra obtained in both\n",
        "cases. As one can see below, they match exactly, confirming we have\n",
        "correctly set the symmetry in the second simulation."
      ],
      "id": "96cd4bec-dfa5-476a-ae14-bb142ab2bac0"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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nTrkoMxHXatGiBf/973/dnYaUgN4rkYpFqwSLiIjTpNvtwxoQEGCIc4KCjPGZ\nM07PSURERCoPFawiIuI0FytYzTarBgOYz51zek4iIiJSeahgFRERp7lYwWqpUcN4QUKCkzMSERGR\nykQFq4iIOI19wWq/d57pwnL7+Txs5rOKiIiIaNElERFxCovZzFMpKaSD9SvA39/QxqtmTWOcmuqy\n/ERERKTiU8EqIiJOkZ2WxrM2cQ7g7eNjaONTp44xTktzfmIiIiJSaWhIsIiIOEWG3QJKhZWifvXq\nGWObPVlFREREVLCKiIhTZJw9a4gzPQr+ygmoX98YZ2c7NScRERGpXFSwioiIU2TGxxvirEIK1qCG\nDQ1xcG6uU3MSERGRykUFq4iIOEWW3RY1WV4Fl00IbdLEENewWLCYzc5MS0RERCoRLbokIiJOkW23\nRU22p2eBNn41avBvLy/O5uaSACQA36WmEhQS4pokRUREpEJTwSoiIk6Rk5hoiLO9vQtt92mtWsTG\nxlrjxORkFawiIiICaEiwiIg4Sa7dE9Zcuy1t8tWoUcMQJ9gNJRYRkcKtXr2afv368eCDD7o7FSlE\nUlISAwcOZNCgQSTafYgrJacnrCIi4hS5ycnG2Ne30HZhYWGGWAWriMjF5eXl8eCDD7Jnzx4mTJjg\nkHseOnSIzZs388cff7B//37MZjNBQUF89913Drl/Zc0F4K+//mLhwoUcOHCAjIwMIiMjGTRoEIMG\nDcKrkDUaAEJDQ6lbty6fffYZU6dO5a233nJx1lWDClYREXGKvNRUY1xEwWr/hDXJ7smsiEhRbrrp\nJuLj4xkzZgx33323u9NxqdmzZ7Nnzx4uu+wyRo0aVa57LVy4kPvvv594u9Xd4XzR5UoVKRc4/8HA\nY489xrvvvovFYjGc++CDD+jUqRMLFy6kefPmhV4/efJk5s+fz3vvvcf9999PixYtXJF2laIhwSIi\n4hQWu4LV7OdXaLsQu/mqyXZPZkVEirJu3Tp++eUXDh065O5UXCo7O5v//Oc/ADz99NOYTKZy3S82\nNpb4+HiaN2/OmDFjmD59Ol26dHFAppU7F4BJkybxzjvvYLFY6NevH3PnzuXbb7/l0UcfxdfXl23b\ntnHttddy1m7v8XzNmzdn1KhRZGdnM2XKFBdnXzXoCauIiDiFfcFq8fcvtF0DLy86AiFAKOBRzf7w\nFBEprcWLF3Pq1CmCg4O5/vrry32/m2++mdtvv53w8HDrsVWrVpX7vpU9lw0bNvDBBx8AcNdddzF7\n9mzrhwPXXXcdAwcOZODAgRw9epTnnnuOmTNnFnqf22+/nS+//JLFixczffp06tSp47LvoSrQE1YR\nEXEKS1qa8UBAQKHthh04wDZgNfAdUO+PP5ycmYhI5ZZfRI0cORL/Ij4MLI26desaCkR3qki5TJs2\nDTg/FPntt98u8CS7f//+3HLLLQDMmTOH06dPF3qf/v37U7duXXJycpgzZ45zk66C9IRVREScwpSR\nYTxQRMFqsd/CRkOCpRI4fvw4ixcvZseOHcTHxxMUFES9evVo1aoVN9xwA/Xq1bO2XbhwIR999BEm\nk4kvvviCWrVqFXlfs9nMyJEjSU5OZsCAATzxxBPWc8OGDSMjI4M777yTW265haSkJD799FO2bNlC\nWloabdq04b777qNhw4aGe27fvp158+Zx9OhRfH196dWrF3fffTd+RQzTd1U/ttauXcvKlSs5evQo\naWlp1KxZk27dujF69OhCi5fnn3+ejRs3WhdpW7BgAVu3bjW0sZ/Xav99paamMn/+fH7//XdiY2Pp\n2rUrbdu2dch75UxHjhxhw4YNANxwww0u6bM6SkpKsj7ZHTlyJMHBwYW2u/POO5k7dy65ubl8//33\njB8/vkAbT09PRowYwaxZs/jkk0/497//7dTcqxyLVDnx8fEWwAJY4uPj3Z2OiFRTz48ZYxkMlpFg\nuR0sXzzzTKHt1txwg8UC1q81l13m4kyrliNHjlj27t1rOXLkiLtTqbJeffVVi7e3t/V3rf2Xh4eH\n5Y033rC2//vvvy0eHh4WwHC8MCtWrLDeZ8WKFYZzgYGBFsDy4osvWrZu3WqpW7dugb79/PwsP/zw\ng8VisVjMZrPlX//6V6E5tmvXznL69OlCc3BVPxaLxXLo0CFLt27divxvGRISYvnss88KXDdixIgi\nr8n/euqpp4r8vrZs2WKpV6+eof2IESMc9l4504cffmjt98yZM07rZ8iQIRbAEhoa6rQ+KnIuP/zw\ng/W/c2H/BvNlZGRYfx7cdtttRbb77LPPrPf7559/Sp1PWX62V5WaQE9YRUTEKf728WGFTdy7iJUR\nPe22tfG0H0osUoF89913PPXUUwBcccUV3HrrrTRp0gRPT09iYmI4ePAgX3/9NbGxsdZrGjduzMCB\nA1mxYgWzZ8/mscceK/L+s2fPBiAyMpIBAwYU2ubs2bMMHjyY+vXrM2XKFBo3bszRo0d59dVXiYmJ\nYeTIkRw5coS33nqLmTNncs899zBgwAB8fHxYunQps2fP5q+//uK+++5j8eLFRebi7H4OHjxI9+7d\nOXfuHKGhodxxxx10796d4OBgjh49ykcffcTOnTsZN24cXl5ejB071nrt1KlT+de//sVNN91EQkIC\nN998c4FVgps0aVLo93X69GkGDRpEeHg4jz/+OJdccgk+Pj7Url3b4e+VM6xduxaAFi1aULNmTZf1\nW93s27fP+rp169ZFtvPz86NRo0YcOXKEvXv3FtmuW7du1tdr1qzhtttuc0yi1YAKVhERcYr09HRD\nHBgYWGg7L7vhfl72Q4lFKpD8+WcdO3bkt99+w8fHp0Cb6dOnc+bMGcOxiRMnsmLFCvbv38+GDRu4\n+uqrC1wXFxfH0qVLgfPDDD08Cl9q5IMPPmDAgAF88803hv0fBw8eTLt27UhNTeXee+9l+fLlfP/9\n9wwePNjaZvjw4QQHBzN9+nS++eYbjh8/TuPGjV3ej8ViYfTo0Zw7d47WrVuzatUqIiMjDf1PmDCB\nMWPG8PXXX/PAAw8wdOhQ66ri7du3B7D+92/cuDH9+vUr9PuwN3PmTHr06MHKlSsLHa7siPfqww8/\nZNGiRSXKpyjPPPMMffv2LXA8f+jzpZdeWq77S/FOnDhhfd2gQYNi2zZo0IAjR45w8uTJItu0atUK\nPz8/MjMz2bJliwrWUqjWBWteXh7bt28nJiYGX19fWrZsScuWLR3ez+nTp9m9ezfnzp3D29ubevXq\n0b59ewKKmM8lIlIVpNk9KS3qZ56P3RMCHxWsrlXW7TA6doRt2wo/16kTbN9etvva7XNoNWECfPRR\n+e7hANHR0QBcffXVhRarAB4eHgVWAR0yZAiRkZHExMTw0UcfFVoEffrpp+Tk5GAymbjrrruKzMHD\nw4M5c+YYikiAZs2aMWzYML766iu+/fZb7rnnHkMRme/hhx9m+vTpWCwWVq9ezR133OHyfpYsWcKf\nf/4JwNy5cwsUqwDe3t58+OGHrFixgsTERBYuXFjo/MDSysvL45NPPilybq0j3quDBw/yyy+/lCvP\not6XuLg4AD1ddbJUm5XuL/Y3e/75VLvV8e2Fh4dz8uRJTp06Vf4Eq5FqWbBaLBY++OADXnzxxQL/\nYDp27Mj//vc/unfvXu5+1q9fz7///W/rxHhbfn5+3HTTTUybNu2in9qIiFRG9k9Yi/qF72u3qIlf\ndrbTchIpr8aNG7Nz506WLl3K008/bVhcqTienp7cfffdvPDCC3z11Ve8++67hIaGWs9bLBbrENO+\nffsWOZwV4Nprry1yMaArrriCr776CsAwhNZWo0aNCA8PJz4+noMHD7qln++++w6ANm3aFLvHZnh4\nON26dWP16tVs3LjRIQXr1VdfXex/X0e8VxMnTmTgwIHlyrOwJ6g5OTkkJiYCVJiVdKuqnJwc62v7\nD23seXt7A+f3xy1OREQEJ0+eLDACQ4pXLQvWBx54wLoceJ06dejWrRtpaWmsW7eO7du306tXL775\n5huGDx9e5j6++OILbrvtNsxmMwBt27alZcuWpKens2vXLuLi4vj888/5+eefWb9+Pc2bN3fI9yYi\nUlGUtGD1t3sS5W/zR4JIRXP77bezZMkSjh07RosWLRg2bBj9+/fn6quvLnaeG8A999zDSy+9REZG\nBvPnz+f++++3nluzZg2HDx8GKDAX017btm2LPGdbxFysXXx8PMnFrMrtzH62X3gCf+bMGWthZ7F5\nMp7/2mKx8NdffwEY5gWXR4cOHS7aprzvlbNG7WVmZlpfl2T1ZSk722ksmZmZ1uHohcm4MDIoKCio\n2Hvmb0Fk//tRilftCtb58+dbi9Vx48Yxa9YsfH19gfNL1A8ePJi9e/cyduxYDh48SP369UvdR0JC\nAvfeey9ms5mgoCC+/fZbw7yK7OxsXn75ZaZOnUpsbCyTJk1ixYoVxdxRRKTy6RgXRx0gHUgDAouY\njxdQt64hDszLc3puImV144038vzzzzNt2jTS09NZuHAhCxcuBM5/CH7dddfx8MMPc8kllxS4NjIy\nkiFDhrBkyRI++ugjQxH00YXhzuHh4Vx//fXF5lDUfHA4/3SwNO1yc3Pd0s+5c+eA8ws7/fjjj0Ve\nb8tRf+SX5Mmko94rRwsODsbHx4fs7Gzrlj6FSUhIYNSoUUWe/+mnn4qcIy3n1ahRw/o6ISGB2rVr\nF9k2/72wfRJfmPj4eEDDuUurWhWseXl5PPfcc8D5ISgfffSR9RE+nB/ms2jRItq3b09aWhovvfQS\n77//fqn7WbVqFSkpKQBMmTKlwCIAPj4+vPDCC6xevZr169fz008/kZaWVuwPfBGRyubpEyewXcrl\neBFze4LsPhgMujAyRVzEGfM9i5rbWh4ffnj+qwL4z3/+w/jx41m4cCG//vorGzduJDExkbi4OGbN\nmsXHH3/MnDlzCl1UZeLEidb5m9u2baNTp07Ex8fzzTffAHDrrbdaP0ivyvKHWEZFRfHss8+W6Brb\nAqI8bIvt4pTnvXLmoku1atXixIkT1uKnMFlZWcXOoTWbzSpYL6KFzcr2x48fL3YExT///ANw0afq\n+R/UFFf8SkHVqmDdsGEDf//9NwAPPvigoVjN165dO+ty5gsWLODdd9+96Lh1e7afeBU3L6NLly6s\nX7+evLw8kpKSVLCKSJXib1d4+hbxVCOwdm3MQP6fTgFATno63lqYTiqwBg0a8Oijj/Loo49isVjY\nvXs3X3/9NW+99ZZ19dwhQ4YUeJo3cOBAGjduzPHjx/noo4/o1KkTn3/+OVlZWcDFhwNXFQ0aNOD4\n8eMkJyeXeHVfVyvPe+XMRZeaN2/OiRMniImJKfLa8PBwfv755yLPl7Ror87yV6IG2LZtG9dee22h\n7Y4fP26dk2p7jb20tDTr/ONmzZo5LtFqoFoVrCtXrrS+Lm4ifH7BmpCQwB9//EGPHj1K1Y/tpyv5\nqwkWJv9cUFBQiRdtEBGpLPzsCla/IgpWk4cHyYDtQKrU2FjCNLdfKgmTyUT79u1p3749LVu25Lbb\nbiM9PZ3ff/+9wOq5Hh4e3HPPPUyePJkvv/ySN99807qAT5cuXYr9g7cq6d27N5s2bWLHjh0cO3aM\npk2bluk++YWXxQkjBcrzXjlr0SWAXr16sW7dOrZt21bkk1IfH58K+0FAZXH55ZfTsGFDoqOjWbZs\nGc8880yh7ZYvX259PWTIkCLvt3nzZuu/0969ezs22SquWhWsu3fvBiAsLKzY1eE6duxouKa0BWuv\nXr24/PLL2blzJ6+88gojRowoMFF706ZN1hXyJk2ahKms2wqIiFRAFrMZ++ejAcXM2Un19CTUZu5q\nmgpWqaQaNWp00Tb5K9AmJyfz8MMPWxcVqi5PV+H89/r666+Tm5vLpEmTWLJkSZmGqIaEhHDy5Mli\n53OWR1nfK2ctugTni52XXnqJ1NRU9u7dq/1YnWjcuHG89NJLbNq0iXXr1tGrVy/D+czMTN555x3g\n/JPvwrZAyvf7778D5xfL6tq1q/OSroKq1eD1I0eOABf/ZWJ7Pv+a0vD09OSbb77hsssuY9++fbRo\n0YInnniCWbNmMX36dG699VZ69+5NTk4Ot99+O88//3yp+xARqchy0tMNn4jmAD7FTHtIt5t6ke6g\n1UBFHO2RRx7h66+/JikpqcC548ePW5/C+Pv7c9VVVxV6j3r16ll3Ish/YhcQEMCYMWOclHXF07x5\nc+vc1eXLlzNkyBBrMWgrNzeXdevW8fTTT1tX5rWVv0Kx7fohjlQR36urr76asLAwAFavXu22POzt\n2bOHfv360a9fPz5083xzR+Xy+OOPW+ebjh07lq1bt1rPJSYmcsstt1i3bPrvf/9b7FDrNWvWANC/\nf/9qMU/dkarVE9b8JdUvtoKX7fnilnsvTrNmzdi2bRuvv/46U6ZM4Y033jCcr1WrFp9//nmZh4sU\n90li/vh4ERF3ST97Fh+bOA2oUUz71bVrszI6miQgGbixlGsHiLjKJ598wttvv42Hhwe1a9embt26\nBAcHc/r0aQ4ePIjFYsHDw4MZM2ZYi4rCTJw40bp4D8CoUaOK3TajKnr++edJTk5m+vTp/PDDD/zw\nww80btyYRo0akZmZSVxcHKdOnbLubTly5MgC97jtttv45ptvOHbsGA0bNuTSSy+1bvcyZswYhzy1\nrmjvlZ+fH+PGjePtt99m7ty5TJo0qdz3TEtLY8SIEYZjO3futJ6zH1783//+l86dOxuOJSYmWuft\nlmTroMqQS2hoKN9//z39+vXjxIkTdOnShUsvvZSgoCB2795NWloaAE899VSh/z7znTp1yprPfffd\nV+Z8qqtq9RdB/j+qi32qYbuvVf41pRUfH8/YsWOtS7VfdtlltG7dmuzsbLZs2UJsbCwjRozgiSee\n4MUXXyz1kGBtFi0iFVnG2bOGAjXzIkP9vm3Xjh9s5vxH+fgU01rEfd555x2WL1/OqlWrOHXqFKdO\nnbKe8/T0JCoqiilTphQYOmivf//+NGvWjKNHjwLVazhwPpPJxFtvvcWwYcN47bXXWL16NcePH+f4\n8ePWNj4+PvTp04eBAwcWOsT2uuuu44033uC///0vZ8+eZePGjdZz9kVMWVXE9+ree+/l7bffZsuW\nLezfv7/QbZRKIycnp8hFonJzcwucO3v2bIF2tg95SjI0vjLkAtCtWzf+/PNPHn30UVauXMmePXus\n59q0acPkyZMv+sT9iy++IC8vj6ZNmzJgwIBy5VMdVauCNb9QzbnIpvT5n+TZXlMaqamp9OjRg/37\n91OrVi0WLVpEVFSU9XxeXh5vv/02TzzxBC+//DKJiYnMmDGj1P2IiFRUWXajQLIuUrDaP60o6+gW\nEWcbN24c48aNw2KxEBcXR2xsLMnJyYSGhtKqVSsCSri6tclkolatWhw9epRWrVrRs2fPi16zbNky\ncnNzDdtt2Bs8eLB1dVh/f/8i233yySekpaURGRnptn7y9enThz59+pCZmcmhQ4dISEggKCiIOnXq\nULt27UJ3dbD12GOP8cgjj3Do0CHi4uKsf8fZr1dSku+rMGV5r5ytdevWDB8+nCVLljBz5kzefvvt\nct0vKCio2FWF7V1xxRUFjq1atQo4XyBOmDChSuSSr0WLFixZsoSkpCSOHDlCRkYGkZGRNG7c+KLX\nms1m63Dyxx57TNsJlYWlGmnWrJkFsHTp0qXYdqdOnbIAFsDy4IMPlrqfF1980Xr9smXLimx3//33\nW9vt3r27VH3Ex8cX+XX06FHrfePj40udv4hIee1fuNBiOb/Dp8UClgO+vsW2v+eee6w/twDLzJkz\nXZRp1XPkyBHL3r17LUeOHHF3KlKMvXv3Wv+9v/rqq+5OR4pRUd+r/fv3W7y8vCx+fn6WkydPujsd\nS5s2bSyAZd68ee5OpULlMm/ePAtgueSSSyw5OTllvk9ZfrbHx8dXiZqgWpX4+Z/unThxoth2tvta\nNWzYsNT9LF26FDg/T7W45a3vvPNO6+tly5aVqo+wsLAivxy1sbaISFll282lz77InFQ9YZXqJn8h\nGB8fH8PfA1LxVNT3qnXr1tx7771kZmYybdo0t+YSHR3Nvn376Ny5M2PHjlUuF+Tl5TF16lQA3nzz\nTby0PkOZVKv/au3atWPdunXExsaSkJBQ5GII+/bts77OX32uNE6ePAlcfMy87TCC/GtERKqCAgXr\nRYb02S+Gp4JVqrIdO3Ywa9Ys4PyiQbVq1XJzRlKUiv5evfzyy4wYMcLtq84GBATw888/06pVK7dv\n1ViRcsnIyOC9997D29tbe6+WQ7UqWKOiovjggw+wWCysW7euwApk+fKXCPf29i71HqwAwcHBQPEr\n+cL5hZnsrxERqQpy7bb8yL3IIkohISF4AyEXvkz6EE+qmB07dvDEE0+QkpLCjh07yMnJISQkhBde\neMHdqYmdyvRehYSEFFg11x0iIiIqRB5QsXIJCgqqMLlUZtVqSPDgwYMJvLAPYFF7MiUmJvLVV18B\ncO211150C5zCtG/fHoCjR49y4MCBItutWLGiwDUiIlVBnt1+iBcrWDvt2kU2cBY4CvRft85puYm4\nw7lz5/jll1/YvHkzOTk5tGzZkqVLl9KgQQN3pyZ29F6JVCzVqmANCgri4YcfBs4Xi5999pnhfF5e\nHhMnTrRuPP3cc88Vep+lS5cyb948Fi1aVOj5W2+91fr6zjvvLHRz8e3bt1s/qQsPD2fQoEGl/n5E\nRCoq+4I1z2a7sMJ42U3R8E5Pd3hOIu7UsWNHfv75Z3755Rf279/Pvn37Lrr1jbiH3iuRiqVaDQkG\n+Pe//82PP/7I1q1bufPOO/nhhx+IiooiLS2NL774gm3btgHw5JNP0q1bt0Lv8cQTT3DgwAFCQ0O5\n6aabCpwfOnQoo0ePZuHChfz2229ccskl3HbbbdZ9WH/77TcWLlxIdnY2JpOJ//3vf9Vus3ARqdpi\n6tThaSAACATCmjaluI0gfGrWNMTemZlOzE7E9cLDwzU0sJLQeyVSsVS7gjUgIICVK1dy5513smzZ\nMhYsWMCCBQus5728vHjuueeYMmVKufr5/PPPady4Me+88w6nTp3i9ddfL9Cmfv36/O9//+OGG24o\nV18iIhXNP2FhvGoTP3755cW297VbyMQ3K8sJWYmIiEhlU+0KVoCaNWuydOlStm3bxooVK4iOjsbX\n15dWrVpx4403Ur9+/WKvHz58OCdPnrTOhy2Mj48Pr776Ko8//jgrV65k165dxMfH4+XlRb169eja\ntSv9+/e/6GbYIiKVUbrdkN6AgIBi2/vVrm1sn5Pj8JxERESk8qmWBWu+Tp060alTp1Jf99prr5W4\nba1atbj99ttL3YeISGVW2oI1sF49Y/vcXIfnJCIiIpVPtVp0SUREXMO+YC1uRApAoN3IliCz2eE5\niYiISOWjglVERBwuLS3NEF/sCWtQnTrYlqhBQF52tuMTq0YsFou7UxAREQepzj/TVbCKiIjD+Z09\nSxOgFudXCQ64yLY2Hl5epNgdS42NdU5yVZynpycAOTk5mPWkWkSk0rNYLORemCrj4VH9yrfq9x2L\niIjT3bVlC8eA00Aq0HTv3otek3ah0MqXevKkU3Kr6vz9/YHzf+AkJye7ORsRESmv9PR06weQ+T/j\nqxMVrCIi4nDedsN5vUqw13S6l3EdwPRTpxyaU3VRo0YN6+vY2FjOnj1LtoZXi4hUOhaLhbS0NE7a\nfIAbUoLfp1VNtV4lWEREnMPbblsaH5siqigZ3t5gs/9q5unTjk6rWvDx8SE8PJz4+HgAzpw5w5kz\nZ/Dy8sLDwwOTyeTmDEVE5GLyhwHbTu0ICgrC7yJTbKoiFawiIuJwPnbb0pSkYM3y9YXUVGusgrXs\n6tSpg4+PD6dsnlLnaqsgEZFKKygoiAYNGlTLDx1VsIqIiMP55uUZ47Cwi16TY/epcc65cw7NqboJ\nCwsjODiYtLQ0UlNTtQiTiEgl4+Hhgb+/PyEhIfj5+VXLYhVUsIqIiBP42RVGvuHhF70mrnZtVp84\nQTKQBNTw9XVOctWIl5cXoaGhhIaGujsVERGRMtGiSyIi4nD2BWtAzZoXvWZt795cA1wHjAMO1a7t\njNRERESkElHBKiIiDmWxWAiwO+YfEXHR6+xXPtSWLCIiIqKCVUREHConPR1vmzgX8A6wL2ELsh+2\nqoJVREREVLCKiIhDpZ89a4jTAJPHxX/d6AmriIiI2FPBKiIiDpVpt7pvZgmKVVDBKiIiIgWpYBUR\nEYfKSkgwxGUtWFOSkhyWk4iIiFRO2tZGREQcKjM+3hBne3qW6Lq6p0+zHwi58HVs0yaH5yYiIiKV\niwpWERFxqOzEREOc5VWyXzWBwcG0tIn9c3Icl5SIiIhUSipYRUTEoVK9vJgPBAIBQGqNGlxagusC\n6tY1xrm5TshOREREKhMVrCIi4lBna9bkVpt48OWXc0MJrguqX98QB5rNDs1LREREKh8tuiQiIg6V\nnp5uiANKsAcrQFC9eoY4BDDrKauIiEi1poJVREQcKiMjwxD7+/uX6DpPHx9S7Y6lnjrloKxERESk\nMlLBKiIiDlXWghUgxW4LnLTYWIfkJCIiIpWTClYREXGo8hSs6XYrCqtgFRERqd5UsIqIiEOVp2DN\n8PY2xJlxcQ7JSURERConrRIsIiIO1e2HH0gG0oEM4K/du0t8bZavL6Sl/X985ozD8xMREZHKQwWr\niIg4lGd6OsFA8IX471Jcm+PnZ4izz551UFYiIiJSGWlIsIiIOJRHZqYhNpVwWxuAHLu2ufHxDslJ\nREREKicVrCIi4lAeWVnGODCwxNea7drmJSY6IiURERGppFSwioiIQ3lkZxtiz6CgEl9rDg42HkhK\nckRKIiIiUklpDquIiDiUZzkKVktYGGeB5Atf8SaTQ3MTERGRykVPWEVExKG8cnIMcWkK1sNDh1IL\naA5cAaxs3tyhuYmIiEjlooJVREQcytuuYPUOCSnxtSF2bZOTkx2Sk4iIiFROKlhFRMShvHNzDbGX\nClYREREpIxWsIiLiUN55eYbYJzS0xNeqYBURERFbKlhFRMSh/OwKVt8aNUp8rX3BmqRVgkVERKo1\nFawiIuJQvmazMS5HwaonrCIiItWbtrURERGH8rVYDLFfWFiJrw3x8uIrIOTCl9fJkw7NTURERCoX\nlxesZrOZtWvXsmnTJuLi4khPT2fy5Mk0btzY2iYjI4O8vDwCAgLw8NBDYBGRysTfLi5NwRocHs5I\nm9icl4fFbMak3wUiIiLVkksL1l9//ZWJEydy+PBhw/F7773XULCOHz+e+fPnM3/+fMaOHevKFEVE\npBxys7I4yPmi1R/wBcJKsQ+rd0AAaUDghdgDSD19mqC6dR2dqoiIiFQCLvvIesmSJVx77bXWYjUy\nMhJ/f/vP4c8bPHgwAD/99JOr0hMREQfIyM7mUqA5UB9oGBBQ6qejqXbtUzUsWEREpNpyScGamJjI\nuHHjyMvLo0+fPuzdu5fo6Gjatm1baPv+/fsD8Msvv7giPRERcZCMjAxDXNQHk8VJ9/Q0xKkxMeXK\nSURERCovlwwJ/vzzz0lMTKRdu3asWLECPz8/AEwmU6Hta9WqRUBAADExMSQlJRFaij38RETEfRxR\nsKb6+kJOjjVOP3Gi3HmJiIhI5eSSJ6zr1q0D4KGHHrIWqxdTp04dAGJjY52Wl4iIOJYjCtZMu2sy\nNSRYRESk2nJJwXrq1CkA2rVrV+Jrgi4s0pGVleWUnERExPHS09MNcUBAQKnvkR0YaIwv/A4RERGR\n6sclBauPjw8A2dnZhuNFDQkGiIuLAyCsFNshiIiIezniCWtuSIghzjt7tlw5iYiISOXlkjmsjRo1\nAmDHjh1ERUVdtP2ePXs4ffo0gYGB1KtXz8nZiYiIo3jv2MEGIANIB85e+PCxNCw1ahjjc+cckZqI\niIhUQi55wtq3b18AZs6caRjiW9QT1ilTpgAQFRWFt7e38xMUERHHiIujB9APGA5clppa6luYwsON\ncWKiIzITERGRSsglBeuoUaNo0KABBw8e5KabbiIhIaHQdsnJyYwfP55vv/0WgEceecQV6YmIiIPk\n2hWouRemhJSGZ61ahtgrOblcOYmIiEjl5ZKC1c/Pjzlz5uDp6cmSJUto3Lgxo0ePJjo6GoBZs2Yx\nevRoGjVqxOzZswEYP3689cmsiIhUDma7gjWvDAWrT926xjgtrVw5iYiISOXlkjmsAAMGDOCbb75h\n3LhxJCYmsmjRIuu5/CI134QJE5gxY4arUhMREQcx2xWXZl/fUt/Dv359Y2y3kJOIiIhUHy55wppv\n+PDhHD58mKlTp9K5c2fD/NTatWszevRo1q1bx6xZszR3VUSkEjLbbWtjLuHe27YCIiOtr3OA3Ly8\n8qYlIiIilZTLnrDmi4iIYPLkyUyePBmAlJQUfH19rVvfiIhIJWZXsFrKULAGdehAIyAeSAMiAgPR\nxjYiIiLVk8sLVnvBwcHuTkFERBzEYlewmsqwD2tY7dpE28QJCQmYzWY8PFw6KEhEREQqAP32FxER\nhzFlZhoPlKFg9fHxITAw0BqbzWZSUlLKm5qIiIhUQi4rWB977DHGjx9Pcgm2J3jvvfe45557+PPP\nP52fmIiIOIyHXcFqCggo033C7fZiLWo7NBEREanaXFKwbtu2jbfeeosdO3YQEhJy0fY+Pj7MmTOH\nmTNnuiA7ERFxFFNWliH2sHlSWhphYWGGOD4+vsw5iYiISOXlkoL166+/BmDkyJElap/fLv86ERGp\nHDwdVLDaP2FVwSoiIlI9uWTRpcOHDwPQsmXLErUPCwsjIiKCs2fPkpSURGhoqDPTExERB/HMyTHG\nQUFlus/AjAwGAuEXvrw2bIB+/cqdn4iIiFQuLilYT548CUC9evVKfE39+vU5d+4cJ06cUMEqIlJJ\neGVnG+MyrgTfPSGBnjbxukOHypGViIiIVFYuGRLsd2EfvsTExBJfk7/Ahre3tzNSEhERJ/DKzTXG\nZSxY8+w+qDSfO1fmnERERKTycknB2qRJEwDWrl1bovZHjhwhJiYGT09P6tev78TMRETEkca3aEEg\nUBNoCOR07Fi2G9nNYTVpDquIiEi15JKC9ZprrgFg1qxZREdHX6Q1PPvsswB07drVsBefiIhUbKkZ\nGaQD54AYwN9utd+S8oiIMMZJSeXOTURERCoflxSsN954I/Xr1ycpKYm+ffuyefPmQtslJSVxzz33\nsHDhQgAefvhhV6QnIiIOkpGRYYj9/f3LdB/vOnWMcWpqmXMSERGRyssliy75+voye/Zshg4dyqFD\nh+jatSudO3emW7duREREkJmZyf79+1m1ahVpaWnA+SK3pNvglFVOTg6///47MTEx+Pr60rJlSy67\n7DKn9bdr1y6OHTtGamoqDRs2pG3bttSsWdNp/YmIuJqjClbfunWNcXp6mXMSERGRysslBSvAoEGD\nWLBgAXfeeSdpaWls3bqVrVu3Ftr2tttuY9asWZhMJqfkYjabefvtt5k2bRrn7BbyaNu2Le+99x5R\nUVEO6SsvL4/33nuPN998k3/++cdwztPTk2uuuYapU6fSrVs3h/QnIuJOjipY/Rs0MMQBmZllzklE\nREQqL5cVrACjRo2iZ8+evPvuuyxfvpy//vqLvLw8AOrWrUtUVBT33nsvvXv3dmoe48eP5+OPPwYg\nMjKSHj16kJaWxi+//MLevXvp27cvCxcuLPcT3rS0NIYOHcqaNWsAiIiIoHv37oSFhREfH8/WrVv5\n+eefueaaa1SwikiV4KiCNbhRI2Nst7+riIiIVA8mi8VicVfnFouFlJQU/P39XbZ9zWeffcYdd9wB\nwIQJE3jvvffw8jpft588eZJBgwaxa9cu/P39OXDgAA0bNixTPxaLhQEDBvDzzz/j7e3NG2+8wQMP\nPICnp6e1jdlsZvXq1Xh5eTm0SE9ISCD8wgqb8fHxhJVx0RMRkdLIy87mQ19fMoAMIB14yWwu02iZ\npH/+IbRxY2ucCgS579eViIhIpVNVagK3FqyulpeXR9OmTYmOjuayyy5j+/bt1mI134EDB2jXrh15\neXlMmDCBWbNmlamvWbNmce+99wLw6aefMm7cuHLnX1JV5R+niFQuqadOEVSvnjVOBwLK+CvGYjaT\n5+lpGAaUnZKCT1BQ+ZIUERGpJqpKTeCSVYIrirVr11q31Zk0aVKBYhWgdevWDB48GIBFixaRU4Zh\naGazmVdeeQWAHj16uLRYFRFxl8yEBGNcjnUITB4eJNldn/T332W+n4iIiFROLp3Dmi8lJYWjR4+S\nnJxsncNalE6dOhEcHOyQfn/44Qfr6wEDBhTZbsCAASxdupTExER+//13evbsWap+fv31V44fPw6c\nny8rIlIdZCUmGuNyLpyX7OVFhM2Hhsl//02tSy8t1z1FRESkcnFpwfrll18yffp0tm7dSklHIm/Z\nsoXOnTs7pP89e/YAEB4eTiO7BT1sXXHFFYZrSluwrl+/3vq6T58+AJw5c4Y//vjD+mi+S5cu1K5d\nu1T3FRGpyAoUrDZz9ssi3ccHbArW9JiYct1PREREKh+XFaz3338/H3zwgau6K9SRI0cAii1WAcNC\nS/nXlMaOHTsACA0NJTg4mDvvvJN58+aRm5trbWMymRgyZAjTp0+nRYsWpe4jwW7ona1Euz8aRURc\nITspyRiXt2D194cLe3MDZJw8Wa77iYiISOXjkoL1xx9/tBarTZo04d5776VLly5EREQYVs0tTFmK\nuaIkJycDEBISUmy70NDQAteUxtmzZ6399O3blx07dhAeHk7v3r0JCAhgx44d7N27l2XLlrFx40bW\nrFlD+/btS9VH/gRqEZGKItvu52VOOQvW7zp35uEffiAeiAfebNaMK8t1RxEREalsXFKwfvnll8D5\nobbr1q0jyE2rPKanpwPg6+tbbDs/P78C15RGfpEbHR1NdHQ0o0ePZvbs2Ybv+7PPPuPuu+8mISGB\n0aNHs2fPnosW7yIiFVmuXcGaXc7typJbtGCTTXzO7gmuiIiIVH0uWSU4f1jtU0895bZiFf6/UL3Y\nyr/Z2dkFrikNHx8f6+sWLVrw2WefFfi+x40bxyOPPALA/v37WbJkSan6iI+PL/Lr6NGjpc5ZRKS8\nclNSjHE5C1b75ffj4+PLdT8RERGpfFxSsAYGBgIXnzvqbPmrDafZzIkqTGpqaoFrytIPnC9Miyp6\nJ0yYYH29atWqUvURFhZW5FeNGjVKnbOISHnl2vzsBMgrZ8FqP/WhuLn7IiIiUjW5pGDt2LEjgHUP\nVHfJL5hjLrLSpO35shTZjRs3tr5u165dke1atmyJ94U/6P75559S9yMiUpGY7Z6w5tmMNikL+4JV\nT1hFRESqH5cUrPfeey8BAQF8+umnruiuSPnF46lTp4r9w2ffvn3W123bti11P5fa7BN4sXmp+edt\nVxAWEamMzHajV/LKMKXCloYEi4iIiEsK1kaNGvHll1/yyy+/8Mwzz5CXl+eKbguIiooCwGKxsGbN\nmiLb/frrr8D5uajdu3cvdT/XXHON9fWxY8eKbHfq1CkyMzMBqF+/fqn7ERGpSCx2i9RZylmw1vLy\nYizwL2AyMGDv3nLdT0RERCofl6wS/Ouvv5Kdnc3jjz/Oq6++yuLFixk1ahSXXHIJAQEBxV7bt2/f\nAp+yl9WgQYMIDg4mJSWFWbNmccMNNxRoEx8fz+LFiwEYOHDgRbfAKUznzp1p0aIFhw8fZtGiRTz0\n0EOFtlu0aJH1dZ8+fUrdj4hIRVKgYPX3L9f9aubkMN8mPqZ9WEVERKodlxSsTz75JNu2bbPGhw8f\n5pVXXinRtVu2bKFz584OySMwMJDHHnuM//znP/z00098+OGHhoWPcnJyuOeee0hNTcVkMjF58uRC\n77N48WISExPx9fXltttuK7TNCy+8wC233MKmTZt47bXXePLJJw3nd+3axfPPPw9AZGQko0aNcsj3\nKCLiLvYFK+UsWEOaNDHEQZo6ISIiUu24pGCtSJ566il+/PFHfvvtNyZOnMjKlSuJiooiLS2NBQsW\nsHv3bgCeffbZIgvl5557jgMHDhAaGlpkwTpmzBhWrlzJvHnzeOqpp1ixYgVDhgwhICCA7du3M3/+\nfLKysvD19WX+/Pll2j5HRKQiWd+uHc9s2EAA4A9c37kzUeW4X6hdwRpmsWAxmzF5uGQ2i4iIiFQA\nLilYV6xYYdjbtDTq1Knj0Fz8/PxYvnw5EyZMYPHixXz33Xd899131vM+Pj5MnTqVp556qlz9mEwm\nPv74YyIjI3njjTdYu3Yta9euNbRp06YNH3/8Md26dStXXyIiFUGcpyd/2MQDy7mVmW9ICGlA4IXY\nC0g+eZKQyMhy3VdEREQqD5cUrLVr13ZFNyUWFhbGV199xd69e1m+fDnR0dH4+vrSqlUrrrvuOmrV\nqlXs9aNGjSI2Nvai82+9vb155ZVXeOihh1iyZAmHDh0iPT2dOnXqcPXVVxMVFYWHnhSISBWRkZFh\niP3LOSQYIMnTk0CbhfqSjx9XwSoiIlKNVLshwbbatm1bpm1rXnzxxVK1r1u3rmGurIhIVeSMgjXF\n2xtsCtbUf/6BHj3KfV8RERGpHPR4T0REHMIZBWuG3fz+9JiYct9TREREKg8VrCIi4hDOKFgz7aZe\nZJ06Ve57ioiISOXh8iHBeXl5bNmyhR07dnD27FnS09OxWCxFtp80aRINGjRwYYYiIlIWnaKjqQ9k\nXPgKzskp9z1zgoMhNvb/47i4ct9TREREKg+XFqzz5s3j2Wef5Z9//inxNSNHjlTBKiJSCdx6/Di2\nqwL8dfZsue+ZFxJiiM3nzpX7niIiIlJ5uKxgff3113nyySetcXh4OCkpKeTk5FCzZk3S0tKsw8lq\n1qxp3ZfUx8fHVSmKiEg5eNssjgTgExpa/puGhxvj+Pjy31NEREQqDZfMYf3777/597//DcCQIUM4\ndOgQ586do3379gCsXLmS1NRUNm7cSO/evQkLC+O7774jJibG2kZERCo2HycUrB4REcY4Kanc9xQR\nEZHKwyUF6/z588nNzaVp06Z88803tGjRomAiHh50796dX375hWbNmtG/f38OHjzoivRERMQBfM1m\nQ+xTo0a57+lVp47xnikp5b6niIiIVB4uKVg3b94MwIMPPljoEF/bRZc8PT159913SUxM5Omnn3ZF\neiIi4gD+dgWrX1hYue/pV7euIfZNTy/3PUVERKTycMkc1rMXFt5o06aN4binpycAWVlZhuOtWrWi\nUaNGLF26lIyMDIdsjSAiIs7lZx87oGD1uvxy7gMSgHgguE4dvi73XUVERKSycEnBGnBhHz37wjMo\nKAiAM2fOFLgmODiY3NxcYmJiaNmypfOTFBGRMsvLzsbX7pifA4YEB7duzUybuJHdXq8iIiJStblk\nSHD9+vUBOHnypOF4o0aNANi9e7fheG5urnXrmxwH7OMnIiLOlZmYaIgzAJNH+X/FhNutEhyvVYJF\nRESqFZcUrK1btwbOrxZsq1u3bsD5RZkyMzOtxz/66CNSUlLw8vKicePGrkhRRETKITMhwRibTA65\nb3BwsHX6CEBqamqBaSQiIiJSdbmkYB04cCAAK1asMBy/4YYbCAwM5ODBg3Tu3Jlnn32WMWPG8K9/\n/ct6XWBgoCtSFBGRcsiye8Ka5aCC1cPDg9q1axuOnTp1yiH3FhERkYrPJQXrFVdcwZVXXklcXByJ\nNn/U1KpVi9deew2Av/76i2nTprFgwQLMZjMRERFMnz7dFemJiEg5FShYbZ6Klle9evUMsQpWERGR\n6sMliy6ZTCb++OOPQs/df//9REZG8s4777B37158fX3p3bs3zz//PM2aNXNFeiIiUk7OLFjb1KiB\nD1APqAukbtkCXbs67P4iIiJScbmkYL2Y4cOHM3z4cHenISIiZZSTnGyIsx1YsE6MjqanTbzut9/g\nwtQRERERqdpcMiRYRESqNvuCNdfLcZ+H5tWqZYjNJ0447N4iIiJSsbnkCeuyZcs4e/Ysw4YNIyIi\nwmnXiIiIe8TXrMkEwB8IAMIbNaKDg+5turA1Wj6P06cddGcRERGp6FxSsP7nP/9h27ZtbNmypcTF\nZ1muERER90gMDOQjm/j6Fi0cdm+fC3t25/O120JHREREqi4NCRYRkXLLyMgwxP7+/g67d2Dz5sY4\nJcVh9xYREZGKrcIWrDk5OQD4+Pi4ORMREbkY+4I1ICDAYfcObd3aENfIzHTYvUVERKRiq5AFa1pa\nGkeOHAHQcGARkUrAmU9YI9q1M8S18vKwmM0Ou7+IiIhUXE6Zw5q/YFK+c+fOWY/v2bOnyOvMZjPn\nzp3j66+/Ji0tjdq1a9OgQQNnpCgiIg7kzII1qG5dUoDgC7EvkHj8ODWaNnVYHyIiIlIxOaVgzV8w\nyd4LL7xQqvv8S/vsiYhUCj6nTtERyLjwFerh2AE857y9Cb4wVQTg3F9/qWAVERGpBirckGBPT0/a\nt2/P22+/zbPPPuvudEREpAQ6btzINmAvcAzo/uefDr1/kt0T2+QDBxx6fxEREamYnPKEdc2aNeTm\n5lrjPn368Oeff7J69Wo6dOhQ5HWenp4EBgbi4eBP5kVExLlMWVnG2IGLLgGkBwdDcvL/x0ePOvT+\nIiIiUjE5pWANCgoyxM2bNycjI4NatWpRo0YNZ3QpIiJu5GG3cq8pMNCh98+KiIATJ6xxTkyMQ+8v\nIiIiFZNTClZ7ixcvdkU3IiLiJh7Z2YbY08EFK3XqGOPYWMfeX0RERCokjb0VEZFy87QrWD3sRtqU\n+/52K8Z726xELyIiIlWXywrWxx57jPHjx5NsMwepKO+99x733HMPfzp40Q4REXEOL7uC1Ts4uIiW\nZdSxI28CjwO3AvNq1XLs/UVERKRCcsmQ4G3btvHWW2/RqVMnQkJCLtrex8eHOXPm4OXlxcyZM12Q\noYiIlIeXzUJ7AF4OLlgDe/TgcZv40vR0h95fREREKiaXPGH9+uuvARg5cmSJ2ue3y79OREQqNm+7\ngtW7BB9Olka9evUM8alTpxx6fxEREamYXFKwHj58GICWLVuWqH1YWBgRERGcPXuWpKQkZ6YmIiIO\n4OPkgrVWrVqYTCZrfPbsWXJychzah4iIiFQ8LilYT548CRT8hLw49evXB+CEzTYGIiJSMfnk5Rlj\nB29h5uXlRS27eatxcXEO7UNEREQqHpcUrH5+fgAkJiaW+JqEhAQAvL29nZGSiIg4kI/ZbIh9nbDn\ndt26dQ2xhgWLiIhUfS4pWJs0aQLA2rVrS9T+yJEjxMTE4OnpaX3SKiIiFZefxWKInVGwNqxdm4bA\nlcBwIGnXLof3ISIiIhWLSwrWa665BoBZs2YRHR190fbPPvssAF27diXQ0ZvPi4iIw9kXrH5hYQ7v\n4+ljx/gH+AP4HvAp4YegIiIiUnm5pGC98cYbqV+/PklJSfTt25fNmzcX2i4pKYl77rmHhQsXAvDw\nww+7Ij0RESkHc14e/nbH/JzwhDW3Zk1jHBPj8D5ERESkYnHJPqy+vr7Mnj2boUOHcujQIbp27Urn\nzp3p1q0bERERZGZmsn//flatWkVaWhpwvsgt6TY4IiLiPplpaXwM+F/48vXw4EYvx/96Mdkt3Odx\n+rTD+xAREZGKxSUFK8CgQYNYsGABd955J2lpaWzdupWtW7cW2va2225j1qxZhi0MRESkYsrIyWGS\nTRwWGsqNTujHu2FDQ+wbH++EXkRERKQiccmQ4HyjRo3i8OHDPPPMM7Rv3x5PT0/rubp163LzzTez\nZs0aPv/8c/z97QeYiYhIRZSRkWGInfXzO6B5c2OckuKUfkRERKTicNkT1nx169Zl2rRpTJs2DYvF\nQkpKCv7+/tq+RkSkknJVwRrSqpUhDrXrV0RERKoelxestkwmEyEhIe5MQUREyik9Pd0QO6tgjWjX\nzhDXzM3FYjZj8nDpYCERERFxIf2WFxGRckmxG5obHBzslH5CIiOxfaYaCKScPOmUvkRERKRicPkT\nVrPZzNq1a9m0aRNxcXGkp6czefJkGjdubG2TkZFBXl4eAQEBeOiTcxGRCi01Ph5/sBaTzipYTR4e\nnPXyomFurvXYub/+IiQy0in9iYiIiPu5tBr89ddfad26Nddccw3PPfcc//vf/5gzZw5nzpwxtBs/\nfjzBwcEsWLDAlemJiEgZhP76K+lALpAAPHbokNP6SvTzM8RJBw44rS8RERFxP5cVrEuWLOHaa6/l\n8OHDAERGRhY5z2nw4MEA/PTTT65KT0REyig3IQEAT6AGEGCzAryjpdk9vU0/etRpfYmIiIj7uaRg\nTUxMZNy4ceTl5dGnTx/27t1LdHQ0bdu2LbR9//79Afjll19ckZ6IiJRDXlKSMQ4MdFpf2eHhhjjn\nn3+c1peIiIi4n0sK1s8//5zExETatWvHihUraNOmDXB+leDC1KpVi4CAAGJiYkiy+0NIREQqmORk\nQ2gJCnJaV+batY19xcY6rS8RERFxP5cUrOvWrQPgoYcews9u/lFR6tSpA0Cs/hgREanY7ApWnLTo\nEoBHgwaG2NNuDQQRERGpWlyySvCpU6cAaGe3h15xgi58Qp+VleWUnERExDE80tKMcWio8/q67DIW\nALHAKcAjLIyeTutNRERE3M0lBauPjw8A2dnZhuNFDQkGiIuLAyAsLMx5iYmISLl5ZmQYYyf+3Pbt\n04cxNnH77GxecVpvIiIi4m4uGRLcqFEjAHbs2FGi9nv27OH06dMEBgZSr149Z6YmIiLl5G1XsHo5\nsWCtX7++IY6OjnZaXyIiIuJ+LilY+/btC8DMmTMNQ3yLesI6ZcoUAKKiovD29nZ+giIiUmY+dlM3\nfCIinNZXvXr1rKN2ABISEkhMTHRafyIiIuJeLilYR40aRYMGDTh48CA33XQTCRf27LOXnJzM+PHj\n+fbbbwF45JFHXJGeiIiUg6/ddA/fmjWd1peHhwdNmjQxHDt27JjT+hMRERH3cknB6ufnx5w5c/D0\n9GTJkiU0btyY0aNHW4dyzZo1i9GjR9OoUSNmz54NwPjx461PZkVEpOLyz801xH52W884WrNmzQyx\nClYREZGqyyUFK8CAAQP45ptvqFGjBikpKSxatMi6Zc3s2bNZtGiRdc/VCRMm8N5777kqNRERKYeA\nvDxjfGFbMmdp1qwZHkAk0BNI3rTJqf2JiIiI+7isYAUYPnw4hw8fZurUqXTu3NkwP7V27dqMHj2a\ndevWMWvWLM1dFRGpJILMZmPs5MXyhp8+TToQDawDmv3wg1P7ExEREfdxybY2tiIiIpg8eTKTJ08G\nICUlBV9fX8MiGiIiUjnkZmYSYHcs0MlDgkObNsXXJva/sA2aiIiIVD0uL1jtBQcHuzsFEREpo9T4\neDYDIUAw4OXhQWtPT6f2WeOKKwxxzQvTSURERKTqcXvBKiIilVdybi4DbOL6detywsl91uvRwxDX\nz8nBnJuLh5d+pYmIiFQ1Lp3DKiIiVUtKSoohDgkJcXqfoY0aEW+zj7cvELdjh9P7FREREddz6MfR\nAwcO5M8//3TkLfnxxx+5/PLLHXpPERFxDPuC1VXTPE75+xOenm6NT//+O/W6dHFJ3yIiIuI6Di1Y\nz549S5yDF7/Iyclx6P1sZWZmsn79emJiYvD19aVly5Z0cfIfPPv372fVqlXWeNy4cZrHKyKVlrsK\n1qTwcLApWFN27XJJvyIiIuJaTpnwM27cOOrWreuQe9VzwvYIeXl5vPrqq7z66qskJycbzjVv3pz3\n3nuPAQMGFHF12eXk5DB69Gh22fxhNXToUBWsIlJp2f8MddXPs6wGDSAmxhrnHjzokn5FRETEtZxS\nsP7rX/+ic+fOzri1Q4wbN4758+cD5zeg79mzJ2lpafz4448cOXKEQYMGMW/ePMaOHevQfl9++WVD\nsSoiUtm5Yw4rgEfz5vDHH9bYOzraJf2KiIiIa1W7RZdmz55tLVYnTZrEoUOH+PTTT/nqq684cuQI\nnTp1wmKxcNddd/H33387rN9du3Yxbdo0/P39GTlypMPuKyLiTg1/+okjwA5gHTD4+HGX9Bt42WWG\nOPjsWZf0KyIiIq7lkoL1lltuoUOHDuzdu9cV3RUpNzeX559/HoAOHTowffp0PDz+/z9BrVq1WLBg\nAV5eXmRlZfHiiy86rN+77rqLnJwcpkyZQvPmzR1yXxERd/M8c4ZmQAegJ1A3K8sl/UbYrTdQOy3N\nJf2KiIiIazm0YDXZbDNg68CBA+zcuZN0mwUy3GH16tWcPHkSOP901bOQze1btGjB0KFDAVi8eDHZ\n2dnl7ve1115j27ZtXHbZZTz22GPlvp+ISIVhNyQYF81hrd+tG3k2cV2zmYz4eJf0LSIiIq7j0ILV\n398fOL9acEX0448/Wl9fe+21RbbLP5ecnMxvv/1Wrj737dvH1KlT8fDw4MMPP8Tb27tc9xMRqUhM\ndk82TaGhLunXJzCQWLsPHU9u2uSSvkVERMR1HFqwtmzZEoBZs2a5/WlqYfbs2QNAREQEkZGRRbbr\n0KFDgWvKwmw2c9ddd5GVlcV9991Ht27dynwvEZGKyNPuZ71njRou6/u03dPc+K1bXda3iIiIuIZD\nVwm+6aab+Pjjj/nuu++oVasWjRo1wtfXl4MXthu45ZZbrE9hS+qLL76gbdu2Dsnv2LFjADRq1KjY\ndg0bNrS+Pnr0aJn7mz59Or///jv169dn2rRpZb5PYRISEoo8l5iY6NC+RESK4m1fsIaFuazv6KZN\n+WfHDo4Cx4DOgHN30hYRERFXc2jBOmDAAJ544glef/110tPT2b9/v+H8wTLsk+fIJ7X5+wVebNsF\n2/P2WzaU1OHDh5k8eTIAM2bMcPhWD+Hh4Q69n4hIWXjbLbLk48KfTXtuvJHnduywxo/Y7QkrIiIi\nlZ/D92F97bXXGDduHMuWLePvv/8mMzOTpUuXcu7cOYYOHUpERESp7lezZk2H5ZaRkQGAr69vse38\n/Pysr8tSMOdvi5ORkcGIESO4/vrrS30PEZHKwNduYTrfWrVc1nfTpk0Ncf4oGhEREak6HF6wArRr\n14527dpZ486dO3Pu3Dmef/55Onfu7IwuSyS/UL3Yyr+2522L15KaMWMG69evJzg4mBkzZpT6+pKI\nL2Y1zMTERJo1a+aUfkVEbPnl5BhjFxas9j/nyjOFQ0RERCompxSs9oKDgwkNDcXLyyXdFZvH6dOn\nSU1NLbad7fngUm7RcOzYMZ555hkAXn755WIXdyqPMBfOExMRKUpAbq4h9q9d22V92z9hPXr0KBaL\npcgt1kRERKTycUkFuXr1ald0c1GNGjXiyJEjxMTEFNsuOjracE1prFixgrS0NIKCgrBYLIU+Yd2+\nfbv19eeff054eDj+/v7cfffdpepLRMTdAs1mQxxUr57L+q5duzYBAQHWqRupqamcO3fOoVNJRERE\nxL3c+8jTxdq1a8fq1as5deoU586dK3I+7d69e62vS7tCscViAc7/4fTQQw9dtP3zzz8PnN9qRwWr\niFQmFrOZILtjgXXruqx/k8lEs2bN2LdnD5FAU+CfrVupOXCgy3IQERER53J5wWo2m1m7di2bNm0i\nLi6O9PR0Jk+eTOPGja1tMjIyyMvLIyAgAA8Px20Ve80111ifeK5evZqRI0cW2u6XX34Bzs957dGj\nR6n6aN++PQ888ECxbX777TfrU9bbb7+d4OBggoLs/+wTEanY0k6fNhSs6UBAGeb9l8cbyclcA3hf\niDetXAkqWEVERKoMlxasv/76KxMnTuTw4cOG4/fee6+hYB0/fjzz589n/vz5jB071mH9DxgwgNDQ\nUJKSkvjggw8KLVjPnj3L119/DcDgwYNLXUj26tWLXr16Fdvm6aefthasL7zwAk2aNClVHyIiFUHq\nyZOGgjXNZCLAxTn4hYfj/c8/1jjbbjs1ERERqdwc9/jyIpYsWcK1115rLVYjIyPx9/cvtO3gwYMB\n+OmnnxyaQ0BAAE888QRwvnh+9913DeezsrK48847SU9Px2QyMWXKlELvM3/+fGbMmMFHH33k0PxE\nRCqT9IQETgFp+bE7FtazWynY4/hx1+cgIiIiTuOSgjUxMZFx48aRl5dHnz592Lt3L9HR0UXOD+3f\nvz/w/0NzHemJJ56wPgF96KGHGDRoEK+++ipTpkzh8ssvZ9myZQBMnTqVDh06FHqPF198kUmTJlmL\nXxGR6ig+NJR6QBDnh+vc0qaNy3Pwt/s9EnLqlMtzEBEREedxycfhn3/+OYmJibRr144VK1ZY9zYt\nauuBWrVqERAQQExMDElJSYSGhjosFx8fH5YuXcoDDzzAvHnz+OGHH/jhhx+s5/39/XnllVdKtGCS\niEh1lpKSYn2dB3jWqOHyHOpERcFLL1njRsnJWMxmTA5c/0BERETcxyUF67p164DzTzT9SrggR506\ndTh27BixsbEOLVgBQkJCmDt3Lv/5z39YsWIF0dHR+Pr60qpVK4YPH37R/m699VZOnTpFQEDZZmt1\n797dujBTSEhIme4hIuJuycnJhri0+1Y7QsPevUkDAi/E4RYLp/78k7odO7o8FxEREXE8lxSspy4M\n0WrXrl2Jr8lf7CgrK8spOQE0b96cSZMmlfq65557rlz9Dh8+nOHDh5frHiIi7mb7hBXc8wGch5cX\nxwIDuTQtzXosZsUKFawiIiJVhEvGTPn4+ACQnZ1tOF7UkGCAuLg4AMLCwpyXmIiIlJl9weqOJ6wA\n8ZGRhjh10ya35CEiIiKO55KCtVGjRgDs2LGjRO337NnD6dOnCQwMpF69es5MTUREyqgiDAkGsFx6\nqSH23rfPLXmIiIiI47mkYO3bty8AM2fONAzxLeoJa/52MlFRUXh7exfaRkRE3Kvlhg18B3wOvAdc\nfuaMW/II7dnTENfUSsEiIiJVhksK1lGjRtGgQQMOHjzITTfdREJCQqHtkpOTGT9+PN9++y0Ajzzy\niCvSExGRMoiIjmYEcBtwP9AwKckteTQeOtQQN83MJDs11S25iIiIiGO5pGD18/Njzpw5eHp6smTJ\nEho3bszo0aOJjo4GYNasWYwePZpGjRoxe/ZsAMaPH299MisiIhWPp81CRwCeDl7RvaTCmjfnpKen\nNfYB/v7xR7fkIiIiIo7lso3qBgwYwDfffEONGjVISUlh0aJFxMbGAjB79mwWLVpE0oVP5ydMmMB7\n773nqtRERKQMPDMyjLEb9mHNdyI83BCf/fVXN2UiIiIijuTSndWHDx/O4cOHmTp1Kp07dzbMT61d\nuzajR49m3bp1zJo1S3NXRUQqOO/MTGMcEeGmTCCtWTNDnL1tm5syEREREUdyyT6stiIiIpg8eTKT\nJ08Gzm+L4Ovra936RkREKgdfu32yfWvWdFMmYOrRgx/++INdwC7Ax9eXKLdlIyIiIo7i0ieshQkO\nDi60WLVYLCxfvtw6z1VERCoWv5wcQ+zOgjX8jjsYBDwFzAd+OnLEbbmIiIiI47i9YLWXm5vL3Llz\nad++PUOHDiUuLs7dKYmISCH8c3ONce3absoEWrdubZhKcuLECeLj492Wj4iIiDhGhSlY09LSeOed\nd2jevDm33347e/bscXdKIiJSjECz2RjXq+emTMDHx4c2bdoYju3evdtN2YiIiIijOK1gPXDgAHfe\neSeNGzfG19eXBg0aMGLECH7//XdDu8zMTN544w0aN27Mww8/zD///ANAUFAQDz/8MK1atXJWiiIi\nUkYWs5kgi8VwLMiNBStA+/btDfGuXbvclImIiIg4ilMWXVq9ejVDhw4lPT3deuzkyZMsWbKE5cuX\ns3DhQm688UZ27tzJzTffzP79+63t6tSpw6RJk7j//vsJCwtzRnoiIlJOmYmJ+NvE2YBvSIi70gHg\nsssuM8QqWEVERCo/hxesGRkZ3HrrrdZi1cvLi8aNG3PmzBmSk5PJy8vjnnvuoVmzZvTt25eEhAQA\nWrZsyeOPP864cePw9fV1dFoiIuJAqbGxhoI11WQivMjWrtG+fXtMQDPgMqDZL7+4OSMREREpL4cP\nCf722285efIkAM8++yypqakcPnyYpKQkVq1aRYMGDUhMTKRfv34kJCQQHh7O7Nmz2bdvHxMmTFCx\nKiJSCaTbLYiX5unppkz+X4fISJKBw8C3wIPHjmG2WxhKREREKheHF6zr1q0DYNiwYbz00kuGArRv\n3768//77AMTHxxMREcHmzZu5++678awAf+yIiEjJZJ45Y4gzvFy+rXcBddq2JctkssaBQPTate5L\nSERERMrN4QXrkQt7340ePbrQ88OHDycwMBCABx98kObNmzs6BRERcbLM06eNsc2WMu5i8vDgn9BQ\nw7HYH390UzYiIiLiCA4vWFNSUgBo3LhxkW2aNGkCQNeuXR3dvYiIuMCJxo2pxfn5oh2A9+1W6HWX\nZLvfPZmbN7spExEREXEEhxesuRfmC/n5+RXZJv9cRESEo7sXEREXSE5P5yxwDNgJxNev7+aMzvO4\n4gpDHKQ9vUVERCo1p+3DKiIiVVf+aJp8wcHBbsrEqMGoUYa41blz5GVnuykbERERKS8VrCIiUmrJ\nycmGuKIUrE0HDuSszcJLIcChxYvdl5CIiIiUi9OWdezVqxceHoXXwxkZGRdtk2/9+vVcYTfES0RE\n3Mv+CWtISIibMjEyeXhwuG5dasbGWo/Fff01l4wd68asREREpKycVrDmF6XlbZOXl+eIdERExIEq\n6pBggMwuXWDJEmvs88cfbsxGREREysPhBWvPnj2pW7euw+5Xo0YNh91LREQco9WOHUwFUi58RZbg\nA0hXqX3DDYaCtXlsLBazGdNFRvSIiIhIxePwgnX69OmOvqWIiFQwlx0+THebeGNcnNtysddq9GhS\n77iDoAtxbbOZ47/+SuN+/dyal4iIiJSePm4WEZFS887MNMbh4W7KpCAvPz8OhoUZjv3zxRduykZE\nRETKQwWriIiUmk9WljGuYPtqJ19+ufHAhg3uSURERETKRQWriIiUmp/d3qa+NWu6KZPChQ4daoj9\no6PdlImIiIiUhwpWEREpNb/cXGNcq5abMilcq9tu42OTiTuA5kCXzExOnTrl5qxERESktFSwiohI\nqQXYbTkWWK+emzIpXGDt2szq0oXPgKMXjm3QsGAREZFKRwWriIiUWpDFYogDHbidmaP07NnTEK9f\nv95NmYiIiEhZqWAVEZFSyUlPx98mzgMCKtgcVihYsOoJq4iISOWjglVEREol4fBhQ5xkMmHyqHi/\nTnr06GGI//zzT5KTk92UjYiIiJRFxfsLQ0REKrSE/fuNsbe3mzIpXs2aNWnTpo01NpvN/K5hwSIi\nIpWKClYRESmV1CNHDHGyv38RLd2v19VX0w14BfgL8J0yxc0ZiYiISGl4uTsBERGpXDKOHzfGISFu\nyuTixtWowVU28fFdu7CYzRVyCLOIiIgUpN/YIiJSKrkxMYY4JyzMTZlc3KUPP0yWTdw4N5ejy5a5\nLR8REREpHRWsIiJSOnFxhtBSu7abErm44Pr12VWrluFY9HvvuSkbERERKS0VrCIiUiqLmzThKuA6\nYCKQcNVVxV/gZhn9+hnimps2uSkTERERKS0VrCIiUiqHkpP5Hfge+BDw6tLFzRkVr+WjjxriS1NT\nOb1rl5uyERERkdJQwSoiIqUSZzckuE6dOm7KpGTqde7M3oAAw7EDb7zhpmxERESkNFSwiohIqZw+\nfdoQV/SCFeC03bBl3x9/dFMmIiIiUhoqWEVEpMTMZnOlLFjr33uvIb7s9GnS7L4PERERqXhUsIqI\nSInFx8eTl5dnjUNCQvDz83NjRiXT8oYbiPb6/63H/YE906e7LyEREREpERWsIiJSYuf++otHgVuA\nvkDXCrwHqy2ThwdH27UzHMtevNhN2YiIiEhJeV28iYiIyHnpW7fypk28MyHBbbmUVshtt8HOnda4\nzZEj5GVn4+nj48asREREpDh6wioiIiWW8fffhjg9ONg9iZTBpffdR6LJZI1rWizsfv99N2YkIiIi\nF6OCVURESiwnJsYQZ1eSIcEA3gEB/NWokeFYkgpWERGRCk0Fq4iIlJjl1CljXKuWmzIpG9/x4wFY\nC9wO3BAdTVJSkltzEhERkaKpYBURkRLzPHfOEHvUq+emTMrmiieeoFfdukQBc4H4zEwWLFjg5qxE\nRESkKCpYRUSkxPwSEw2xr90Q24rO08eHPhMmGI7NmTPHTdmIiIjIxahgFRGREgtMSzPEAU2buimT\nsrvzzjsx2Sy+tGXLFnbv3u3GjERERKQoKlhFRKTEamRlGeLQVq3clEnZNWnShH79+hmO6SmriIhI\nxaSCVURESsRiNhORl2c4FtG2rZuyKZ+7777bEC/4/HOyUlPdlI2IiIgURQWriIiUSNLx4/jaxKlA\nYO3a7kqnXK677jrCw8O5BHgd2JWQwLZnn3V3WiIiImJHBauIiJRI/L59hvicl5ebMik/X19fZrdt\nyz7gcaA24DtvnpuzEhEREXsqWEVEpESSDx0yxn5+bsrEMdo99JAhviI+nmM//uimbERERKQwKlhF\nRKRE0v/+2xCnBQe7JxEHaTVyJH8FBlpjDyBm0iT3JSQiIiIFqGAVEZESyYmONsTZNWq4JxEHSrrr\nLkPc7dAhYjZudFM2IiIiYk8Fq4iIlMiWunUZCTwATAWi27d3c0bl1/WNNzjm7W2NvYEj993nvoRE\nRETEQAWriIiUyMHsbL4G3geeB5J69XJzRuXn6eNDzC23GI513b2b07t2uSkjERERsaWCVURESiQu\nLs4Q16lTx02ZOFbX//2PE56e1tgP2HvPPe5LSERERKxUsIqISIlU1YLVJyiIwzfcYDjWacsWEo4c\ncVNGIiIikq/ybqLnAKmpqaxevZqYmBh8fX1p2bIlPXr0wMPDMXV8Tk4OO3fu5MSJE8TGxmKxWKhb\nty5du3alfv36DulDRMRVqmrBCnDlzJmc/vprapvNAAQDa+6+m6g1a9yal4iISHVXLQvWnJwcXnzx\nRd58803S09MN5yIjI5kxYwYjRowo8/0XL17MrFmz2LRpU4H7A5hMJgYMGMDbb79N69aty9yPiIir\nWCyWKl2w+oeH88fAgdRescJ67Iq1azm7bx8127RxY2YiIiLVm8lisVjcnYQrWSwWRo8ezVdffQVA\nmzZt6NWrF2lpaSxfvpyEhAQAPvnkE+64444y9XHHHXfw2WefAeDn50ePHj1o0aIFJpOJzZs3s337\ndgACAgL48ccfufrqq8v/jdlISEggPDwcgPj4eMLCwhx6fxGpflLOnOGe2rWJA+KAJF9fTmRkYDKZ\n3J2aw6ScPEluZCRhNr8W119yCT337XNjViIiImVTVWqCaveEdebMmdZi9fHHH+e1116z/sGVkJDA\noEGD+OOPP5g4cSI9e/akefPmZeqnSZMmPPHEE4wbN45Am43pAb799lvGjBlDeno6o0eP5sCBAwQF\nBZXvGxMRcaL4XbtYaBPH5OZWqWIVILh+fdYMG0bUkiXWYz3272fvokW0vekmN2YmIiJSfVWrRZdy\ncnKYOnUqAJ06deLVV181/MEVFhbGggUL8Pb2Jjs7mxdffLFM/YwfP559+/Zx//33FyhWAa6//npr\nHidPnuTbb78tUz8iIq6SdPCgMfbzc1MmztV9/nyO+vgA558k3wVMePttqtlgJBERkQqjWhWsv/76\nK6dOnQJg0qRJhS6u1KRJE4YNGwbAN998Q1ZWVqn76dGjB34X+WNu1KhR1tdbt24tdR8i4l7Zqanu\nTsGl0o8dM8RphXwYVxX4BAVxbsoU3gBaAZ8BG3/7jS+++MLNmYmIiFRP1apg/emnn6yv+/fvX2S7\na6+9FoCUlBQ2bdrklFwCAgKsr9PS0pzSh4g4XuyWLRzy88McHMyarl3dnY7LZEdHG+KsGjXck4gL\ndHn2WdYPH06yzbEnn3yS1Gr2IYWIiEhFUK0K1j179gBQs2bNYreVufzyy62v//rrL6fkYvtUtUmT\nJk7pQ0Qc7+/rr6dlVhZ+QNTmzeybN8/dKbmEOTbWEOfVrOmmTFzjrbfewufC0GA4P32jrNNERERE\npOyqVcF67MKQtoYNGxbbzvb8MbthcI7y1ltvWV/nD0EujYSEhCK/EhMTHZipiORLPXWKq06cMBxL\n/PxzN2XjWh5nzhhiU926bsrENZo3b87jjz9uOPbGG284bdSNiIiIFK5aFazJyecHeIWEhBTbLjg4\nuMA1jjR79mx+/fVXAG644QbDE92SCg8PL/KrWbNmjk5ZRIBdr75a4Jhl/343ZOJ6Phe2/MrnHRnp\npkxc55lnnqFBgwbWuI7ZTMzAgaScPOnGrERERKqXalWwZmRkAODr61tsO9sFk/KvcZTNmzfzr3/9\nC4A6derw/vvvO/T+IuI8v23eXOBYpN1Q2aoqwG7+ZkDTpm7KxHWCgoL4+OOPAbgR2A3clJLCn1FR\n7kxLRESkWqlWBWt+IZqdnV1sO9uVgS+22m9pHDp0iGHDhpGVlYWfnx9fffUVderUKdO94uPji/w6\nevSow3IWkfMyMjJ4fudOAuyON8rNJeHIEbfk5Eqhdh/eBbdo4aZMXOvaa6/ls/79WQxEXDjW89Ah\n/njmGXemJSIiUm1Uq4I1f6hvSkpKse1sV4K0HR5cHjExMfTv35/Tp0/j5eXFV199Rc+ePct8v7Cw\nsCK/alTh1TtF3OWnn34iLS2NDOBPu3NHFy1yQ0auk5OeTr3cXMOxmu3buykb1xv15ZccsVmACaD5\nq69yetcuN2UkIiJSfVSrgrVx48bA+eKxONE22zfkX1MecXFx9OvXj+PHj+Ph4cG8efMYOnRoue8r\nIq7z9ddfW19vsTuXcmFOelUVvWYNtuVarIcHoY0auS0fV/OPiCD744/JsTlW02LhZK9e1W4/XhER\nEVerVgVru3btgPMF5Bm7FS9t2W5lk39NWcXHx9O/f38OHDiAyWRizpw5jB49ulz3FBHXys7OZsmS\nJdZ4q915/wtbZlVVZ9auNcSxoaFuysR92txyCxsv7NGdr0NSEr9feSUWs9lNWYmIiFR91apg7du3\nr/X1r8U8EVm1ahUA/v7+dO/evcz9JScnM2DAAHbv3g3A+++/zx133FHm+4mIe/z6668kJSVZ4x2e\nnobzjU6fdnVKLpWxfbshTrnI1mBVVc+lS9lhN+Wi1759rBs71j0JiYiIVAPVqmAdMGAAYWFhAEWu\nzhsXF8e3334LwJAhQwgMDCxTX+np6QwePJitW88/i5k+fTr33ntvme4lIu711//+R32b+LKxY8m0\nieuZzZyyK+qqEu/Dhw2xqW1bN2XiXp4+PjT6/Xf+8fIyHO+xcCE73nzTTVmJiIhUbdWqYPXz8+Pp\np58GYN26dbz++uuG8xkZGdx+++1kZGTg4eHBlClTCr3PJ598whtvvMGMGTMKPZ+VlcV1113Hxo0b\nAXjllVd4+OGHHfeNiIjL5GZmMm7lSk4Am4DHgJtvuIHDdh9mHV+82B3pucR0Pz9uB14BvgeCevd2\nc0buE9G6NVmLFmG7dJ8X0PiJJzj244/uSktERKTKMlksFou7k3ClnJwcBg0axC+//AJAVFQUUVFR\npKWlsXjxYo4dOwbAa6+9xhNPPFHoPS655BIOHDhAaGgoiYmJBc6PHz+e2bNnA9C6dWvuueeeYnNq\n3Lgxo0aNKsd3ZZSQkEB4eDhwfg5t/lNlESm9HW+9xRWPPWaNkwD/lBR+696d3heG+6cBPw8axHUr\nVrgnSScym80EBQUZ9qSOi4ujdu3abszK/f7497/p+sorhmMnPD2xrF1LZI8ebspKRETk/1WVmsDr\n4k2qFm9vb7777jseeeQR5syZw5o1a1izZo31fHBwMK+//joTJ04scx+HDh2yvj5w4ECRhW++vn37\nOrRgFRHHSZ471xDvbtaMq4OCSLvuOu7avZstwD6gb14e17kjQSc7fvy4oViNiIigVq1absyoYug6\nbRprtmwh6sKaBwAN8vL4u08f4jZvpk6HDu5LTkREpAqpdgUrQFBQEB999BHPP/88K1euJDo6Gl9f\nX1q1asXgwYMvOm/1rrvu4vTp0/j7+xd6fvTo0XTu3LnE+bRq1apU+YuI6zTcu9cQe40cCUDTMWP4\n5MUXrce3bt2KxWLBZDK5ND9n27dvnyFu06ZNlfsey6r3jz+y/pJL6GnzIWWTnBzm9OvHiP37qVmz\nphuzExERqRqq3ZDg6qCqPP4XcbeYjRuJvPpqa5wDZERHExIZidlsJjQ0lFSbfTiPHDlCs2bN3JCp\n87zxxhuGUSITJkxg1qxZbsyoYsnLzuaPFi3ofmH/7k+Be4DLO3bkp59+IiIiwp3piYhINVZVaoJq\nteiSiEhpHP3gA0O8NySEkMhIADw8POjUqZPh/JYtW1yWm6sU9oRV/p+njw+d9+5lc+3avAfcBeQB\n27dvp2fPnkRfKGRFRESkbFSwiogUwWf1akOccOWVhrhLly6GuCoWrE3XrOE6oBXgCbStplvaFMcn\nKIj2hw7xTZ8+2A5Z2rdvH927dy9Q9IuIiEjJqWAVESlEbmYmbU6eNByrfdtthriqF6wWs5lJR4/y\nLXAASAfaaYhrofxCQliydCl9+vQxHI+JieHqq69m27JlbspMRESkclPBKiJSiL2ffEKoTXzWZOKS\nsWMNbbp06sRg4HlgGfDJ+vXkZWe7MEvnivvzT8N/gyyg/hVXuCudCi8wMJAVK1Zwww03GI7fGB9P\nm2HD2HjffW7KTEREpPJSwSoiUoj4L780xAcaNcLDy7iwepOmTfnMZOI/wBCgmcXCn2++6bIcne2k\nzZYtAP8EBmLy0K+N4vj5+bFo0SLr1mjXAx8AAUCPmTNZe8UV5KSnuzNFERGRSkV/eYiIFKLm9u2G\n2NK/f4E2Jg8PDjRubDiW++67Ts3LlVI2bzbECXXruimTysXT05MPPviANx98kC84P/c3X+8//+Sv\n+vU589df7kpPRESkUlHBKiJiJ+HIEdqkpRmOtXzggULbhjz9tCHucuoUMRs2OC03VzLZLRaU27Kl\nmzKpfEwmE4++8w5bx48n0+5ch6QkzO3bs/Xll92Sm4iISGWiglVExM6+GTMMT8UO+PlRp0OHQtte\nOn48+/z9rbEHcPixx5yan6uEnDhhiAPstvGRi7v6ww85NncuMZ6ehuN1zGY6P/ccay+/nIz4eDdl\nJyIiUvGpYBURsZP+00+G+NRllxXZ1uThwdnRow3H2m/ZUiWKkMjkZENcx24FXCmZNrfeit/u3eyo\nUaPAud67dnGyXj32fv656xMTERGpBFSwiojYsFgs3JGYyOXAk8AvQJBdQWqv85tvEm8yWeNwi4Wt\njz/u1DydLf7QIWpa/n9X0UwgsmdP9yVUydVs04bLYmNZc/XV5Nmda56dTetx41jbsSOpp065JT8R\nEZGKSgWriIiNvXv3cuLkSXYBrwPDAwK49F//KvYa//BwdnXubDhWc8ECLGaz8xJ1spiffzbEx/38\n8PTxcVM2VYOXnx9R69ezd+ZM/rFbcdoT6L1jB4mRkWyeMsU9CYqIiFRAKlhFRGwsWLDAEEdFReHr\n63vR65q/8Qa25WmbjAz2zJ7t4OxcJ3HTJkN8rlYtN2VS9Vw2cSI1jh1jfYsWBc5F5uWR+uKLDBs6\nlAMHDrghOxERkYpFBauIyAUZGRnMnDnTcGzYsGElurZhr15sqVPHcCx52jSH5eZqlr17DXF2IcWV\nlF1IZCQ9Dx1iywsvGBZkMgOPAMuWL+fSSy/l0UcfJSEhwW15ioiIuJsKVhGRC+bPn8/Zs2etcY0a\nNbj11ltLfL3npEmG+Krjx9k3b57D8nOl4L//NsS+RaySLOXTZcoUwk6eZE3nzuQCs4FdF87l5uYy\nffp0WrRoweuvv056erobMxUREXEPFawiIoDFbOa3l17CZHNs/PjxBAUFlfgeHZ96iiM28zw9APPE\nieRlZzsuURdIPXWKdnZP9eoNGuSmbKq+wNq1idqyhcNffMHSLl0KnI+Pj+fJJ59kUc2arBs1iiy7\n1ZtFRESqMhWsIiLAtldfZc7x4+wF7gWCPTz410UWW7Ln4eVFwuTJhmPt0tNZd/fdjkvUBXZ8/LHh\nl8NxLy8a9+3rtnyqi0vGjGHJH3+wcOFCGjdubDjXEbgjI4NeixdzLjycNdddpxWFRUSkWlDBKiIC\nWKZPB+AS4ANgSaNGNGrUqNT36fzcc/zWoAEA2cBLwNglS4iLi3NYrs722dGj1ARGAZ8Cezt1wuSh\nXxeuYDKZuOmmm9i3bx8vv/wywcHBAEy1aVM/L4+o778np3591vTqxdl9+9yTrIiIiAvoLxARqfaO\nLFtGlzNnDMdq/vvfZb5fk2+/ZYWnJ+2BycCp5GSefPLJ8iXpImazmRUrVpAMLAbuBLxefNHNWVU/\n/v7+/Pvf/+bYsWNMe+ABogppE2axELV+PcFt27KxaVN2vf9+pd5KSUREpDAqWEWk2jthV0zuCQri\n0vHjy3y/el26sP+117DdlOTzzz9n7dq1Zb6nq+zYsYPY2FhrHBQURK9evdyYUfUWERHBMzNmkL57\nN2s7dCCjkDa+QI+//6b9Aw9wOCCAtTffTHJMjKtTFRERcQoVrCJSrZ3dt4/OdkMqk++6q9z3nTRp\nEpdddpnh2O23325YhbgiWr58uSG+9tprS7QPrThXrUsvpfeOHaTt3cuanj1JMJkKbdcyK4veCxfi\n0bAh69q04cDChS7OVERExLFUsIpItbb3+usJsIlPeHpy5auvlvu+3t7efPDBB4Zj//zzD6Nvuonc\nrKxy399Zli1bZoiHDBnipkykMDXbtCFq3Tq8T55kzYgRHPfyKrRdENBr/34O3XwzV1xxBW+88QYx\neuoqIiKVkApWEam2/vrkE64+cMBw7PCwYXj5+Tnk/j169OChhx6yxn7AHatXs6FHD4fc39FOxcSw\nZcsWw7HBgwe7KRspTlDdukR99x0NMzLYNm0av9erR14h7T4A/vzzT5544gkaNWpEVFQUH374IfHx\n8a5OWUREpExMFovF4u4kxLESEhIIDw8Hzu/fFxYW5uaMRCoec24u+2rUoF1amvXYER8fGp47h08p\n9l69mJycHPr168ff69bxLee3JwHYeP/99HjvPYf14wgb7rqLRp98wjJgGZDSqRPrt251d1pSQrFb\ntnDg8ce5ZMMG6prN/A00BwpbhmmIpyeTa9Uib9Qo2j3+OKFlWBFbREQqtqpSE+gJq4hUSxvHjzcU\nqwCJL7zg0GIVzg8N/mrRIpZ5e1uLVYAr3n+fvZ995tC+ysvrxx9pBNwPrACmXthSRSqHel26ELV2\nLREpKfzx5JN80b49FLEd0di8PLqeOkX3//0P/8aN2RYRwdqRI4nZsMHFWYuIiBRPT1iroKryaYqI\nsyQeO0ZO8+bUsvnx91uDBlzlxDl+ez/7jGZ33IHtYOMEk4lTc+fS5pZbnNZvSWUlJ5MdGoptibpv\n7lza3Hqr23KS8ouNjWXRokXMnz/fOtw7EIi78L+FOejnx8mOHal55520veMOPIqYJysiIhVbVakJ\nVLBWQVXlH6eIs6y9/HJ679pljdOB+A0biHTy3NIN99zD1XPmGI4lmEzEzZ/PJWPGOLXvi9n+6qt0\nfPppa3zKw4PaWVkqVqqQQ4cO8eWXX3J65kxm2GxdVJyzJhMHGzYk75praH7vvdTv2tXJWYqIiKNU\nlZpABWsVVFX+cYo4w59vv037Rx4xzIdYc801RP3yi0v6X9O9O1G//WY4Fm8ycebLL2k9erRLcijM\nhmbNuPrYMWu8vlUretotSCVVg8VsZv8XXxD3zjs03rmTpjk5Jb72l6AgvrvjDvr3709UVBQhISFO\nzFRERMqjqtQEKliroKryj1PE0c6ePcuDrVvzbnw8NS8cO+7lRZ0zZ/CrUcMlOVjMZtZedRVRmzcb\njsebTMTMmEH7++93SR62DixcSMubbzYU8X/8+990fflll+cirnd05Ur+ee89amzYwGVJSXgW03Y6\n8OiF156ennTq1IkePXpwdY8eXN26NbUvvdQFGYuISElUlZpABWsVVFX+cYo4ksViYcSIESxdupT6\nwDygN/Dnq6/S8cknXZuL2czarl2JsluBNxv444476PnJJy7NZ1tEBJ1stjk55OtLs+RkPH18XJqH\nuF/8oUPse+stTMuX0y46mlC784OAHwq5rhlwBPjb25uYRo2w9OhBg9GjaTpwIKYiFn4SERHnqio1\ngQrWKqiq/OMUcaQZM2YwadIka+wBzBwzhvFffOGWfCxmM2uvvJKobdsKnFvTsSNXb9zosP1gi7P1\n5Zfp/NxzhmNb/vMfujz/vNP7lootNzOTfZ9/zrkFCwjbto3mycnU4fycb3u3A4WteX3OZOJwnTpk\nXHEF4UOH0nzkSAJr13Zu4iIiAlSdmkAFaxVUVf5xijjKzp076dq1K1lZWdZjXbt2Zf369Xh7e7st\nL4vZzNq+fYlas6bAuW3h4dT//XfqtWzptP7zsrM5HBpK68xM67EdNWrQ4dw5PRWTApJPn2bN77/z\n888/8/PPP3PAZo7zLGBCCe6RBxzz9eVUgwbkXX45Yf360WzkSIJUxIqIOFxVqQlUsFZBVeUfp4gj\nnDt3jm7dunH48GHrsZCQEP7880+aNm3qxsz+32+PPsrl06cTYHNsJXBLjRrMeO89xowZg8lkcni/\nha1arK1spKRiY2PZtGkTGzZsYODnn9M3Pp6yrCl9PXCgTRs6depk/briiisIcvCeyCIi1U1VqQlU\nsFZBVeUfp0h5ZSUns6pVKx6Mi+OozfEvv/ySm2++2W15FWbf/PmEjhtH/bw8EoBLgZMXzt144428\n//771HbgU6jUU6dIadCAemaz9djGJk3oYbNSsEhppJ0+zcF580havpygnTtpde4cJVlD+P/au+/w\nqKr0gePfmdRJL4SQkNBCk44oSG+CSBEECyJiw4K9o6urruuKuvqzrRWxgF2Kyioqsiu9CCI1lEBI\nAgkppEySSTKTzPn9EeduJplJz8wQ3s/zzMMw59x73nvnzuS+c+49Jx6oPgOyTqfjzuho5nh5Udqt\nG4YhQ4iZOJH4UaNkqiUhhKin1pITSMLaCrWWg1OIplBWK5u7dmVEcjLZwAxgCzB//nwWL17s3uCc\nyNq7l4yRI/mn0cin1cpCQkJ49NFHuffeewkICHC4fH3lJiWRPmAAfYqLtdfKgOyNG4kbMaJJ6xbC\npsJsJumbbzi9ciXe27YRm55eYwqdLCDayfJvAHdVe60IOBEYSG5cHNaePQm68EJixo0jdsgQuYxd\nCCGqaS05gSSsrVBrOTiFaIpfx461uze0FHi8b18W7dyJrwePflthsfDKq6/yxF//anfPrc2//f0J\nueoqLnr7bXwakbimpaUx8+KLeefIEQZVef3XCy5gzG+/NSFyIepWkJpK8sqV5K9bh8++faTl53NN\nQYHDur9SOZJ3fRQBqQEB5EVHY+nWjeJrr6XrkCEkJCTgLT2yQohzVGvJCSRhbYVay8EpRGNtuuUW\nRrz/vt1ryT4+hCUmEp6Q4KaoGubgwYNcf/317Kwy9c1MYMWfzzP1ehKHDKHLc8/RYcyYeq1z3759\nTJkyhbS0NKKATUB3INFgIC4pieDY2ObdCCHqwWg0snv3bnbt2qU9jhw+zBmgsX+92gLZgI+PD927\nd6dnz5507dqVwb6+dAkPJ3r4cKLPP18uLxZCtGqtJSeQhLUVai0HpxCNsfnOO7norbfwqvLaGZ2O\nwrVr6TR+vNviagyLxcLrr7/OP/7xDwrz8tgP9HBQb3dYGAXnn0/whAl0nTOH0A4dtDLjqVMs//FH\nPl66lA0bNtgt1xFY0rYtF+7aRUhcXItuixANUVRYyImvviJ3/XrUnj2EpqTQwWgkoh6nLDlAlJOy\nD4Ab/3xeCpz08+NMeDgl7duj79aNwP79aTtsGDGDB7tkWikhhGhJrSUnkIS1FWotB6cQDbXlnnsY\n8sYbdslqGXD4zTfpd8cd7gqryfLy8vjsnnu4+ZNPqOsU2grk63R4KYUPEACcD+x2UHfmzJl8+umn\n+MuJuTgLKKuV07//zqkff6Ro2za8jhwhLCOD+OJiwqqcymwERjlZxwZgZD3aMgOnfHzICQvDFBOD\n6twZ49VXE9+zJ506dSIsLKxFRu4WQojm1FpyAklYW6HWcnAK0RBbH3iAC195xW5ajXLgt4ceYug/\n/+musJpV+vbtJN18MwMOHKjX6Ks2rwH3VXvtlltu4e2338bLy8vBEkKcPZTVStbevaSvW4dx+3aO\n5efzSXk5iYmJnD592q5uOhDTyHYMVPbKQuUgaJ06daJTp05MsVrprtfj36MHof37Ez14MOEJCTII\nlBDC7VpLTiAJayvUWg5OIepr8513MuStt+yS1Qpg+z33MOy119wVVospOn2a3Y8+Svjy5XYj/TqT\nDcRSmcBfdNFF3HXXXcyZM0d6iESrl5eXR2JiIklJSRw7coSJn3xCaE4OsSZTvS4vtsmg8jPkyKfA\nnGqvmYBMHx/ygoIwRURQHhODvmNHDN27E9q7N9EXXEBIfLwktUKIFtVacgJJWFuh1nJwClEXZbWy\n4bLLGP3993avVwDb77qLYW+84Z7AXOj4mjWc+uIL2LaNdikpJJSVUf0UONXbm5Xz5zP5/vvp3r27\nW+IUwtPkHTtG+saNFOzahfngQbxPnCAkO5uYoiKiqp0abQWGOVnP5lrKalMITOvaFd/OnYmPj9ce\nsbGxxPn50a5tWyJ79pSBoYQQjdZacgJJWFuh1nJwClGbCrOZTYMHM3rPHrvXrcDW229n+Ntvuycw\nNyvOyqI0Lw9vgwGfgAC8/f3xDQpyd1hCnFWMJ0+SvnEjeb/9RtnBg6SVlvKZwcCJEyc4ceIEpaWl\nWt1TOO99rY0V8KPyyofqXgPuASxAjl5Pnr8/hcHBlEVEYI2ORt++Pf5duhDcvTvhvXrRplcvGSRK\nCFFDa8kJJGFthVrLwSmEMyaTiWUjR3Lb77/bvW4Gdp4jPatCCPdQSpGVlVWZvCYn03bpUrxSUjBk\nZRFhNNLObCawHutJB9o7KfsauKIBMVVQmdjm+vnxf4MHQ7duxMTE0K5dO6Kjo2nbti0xgYFEtWlD\nSFycXIosxDmiteQEkrC2Qq3l4BTCkePHjzNz5kz279nDt8CUP18vAI6/9BIDH3zQjdEJIc51ymql\nICWFrF27KNi/n5IjR7CmpOB9+jSBubmEFxfTzmJhD3CRk3VsAoY3sv32VCbD1T0GPEflwFE5Xl4U\n+PlRHBREWWgoFW3aoGvXDp/27TF06kRw166E9+xJWOfOePn6NjISIYS7tZacQG6MEEKcNX744Qeu\nvfZa8vPzAbgK+C/QQa+n4PPPGXjVVe4MTwgh0On1hHXuTFjnznCF435SZbWSkJLC7/n5pKWlaY+T\nJ0+SkZGB2r6dfJPJbrqe+rACmU7Kov/81x+Iq6ggzmQCkwmysuDoUYfLVAA5Oh33xseT2qEDUVFR\ntGnThjZt2hAVFUUHvZ4ORiOBHTsS3KUL4d26ERgVJT24QohmJQmrEMLjlZeX8+yzz/LMM89Q9aIQ\nE/BA9+588cEH9Bje2P4IIYRwLZ1eT5vOnWkDDBw40Gm9ktxcsvfuJT8xkeKkJMpSUuDUKbyzs/HP\nzyekqIhIs5nIP78Xs6lMMh2JdvJ6bbyANkqRmJrK7tTUGuU3Ah9UjxnI9fLC6OuLyWCgNDiY8rAw\nVGQkurZt8Y2NxRAfj9/AgUT07ElkZCTeMrCUEKIW8g0hhPBoJ9au5b833sjfTp2qUXbFFVfwwQcf\nEBwc7IbIhBCiZRkiIugwZgwdxoyptZ65qIjsffs4c+wYKwMDycjIICMjg8zMTO0RsH8/puJiAhoR\nR46T19s4ihloX1FB+5ISKCmB3FxISalR72b+l+yGhYURHh5OREQE/X19mZ+WhiU4GGtoKERE4NWm\nDd5t2+IfE0NA+/YEdehASMeOBLVrJ725QpwDJGEVQngkZbWyYc4cLvjyS24EfgWW/lmm1+t54YUX\nePDBB2UuUSHEOc83KIj2Q4fSfuhQ+tVST1mtFJ4+TW5iIsajRylOTsacloY1IwN9dja+eXkEFBYS\nUlpKuMVC6J/LNSRhra/sKs/z8/PJz88nOTmZKGBoPddhAfJ1Ooq8vSny8+M/nTqxo18/IiIiCA8P\n15LgiOBgOp4+XZnsxscT1qUL/mFhTYheCOFKkrAKITzO8R9+IH/uXEbn5WmvvU5l0loSFcWXX37J\n2LFj3RWeEEKclXR6PcGxsQTHxsL48XXWNxcVkZeUxHYvL3JycsjOzrb7N3LbNn4/dozAkhJCzGYi\nrFb86hmLsyS4IUPC+ABRShFlsYDFwpf79/PZ/v016sVSOf1QVSVAvl5PkY8PJj8/SgMCsAQGUhEc\njAoJQRcaij48nPKEBCwXXURISAihoaGEhoYSEhJCYGCg/GAqhItIwiqE8BhFp0+zc/p0hu3YQZdq\nZaHAoh49GL9+PdHRjbkbSwghREP4BgURPWBAve9/tfXg5iclYTx2DFNqKmWnTlGekQE5Oehzc/Ez\nGjEUF+Pfpg0RBQXk5ubarSOiCfHmOnndURJsAAxWK5SVVT6MRofLfg7McfC6Xq8nWSn0ej0mHx9K\nfH0p8/en3GCgPDAQa3AwhIaiCw3FOyICnzZt8I2KwhAdjSEujsDzziM0NFTu3xWiHuRTIoRwuwqz\nma333EPC4sWMsVprlBuBPTfeyDXvvy/3KwkhhIey68EdNarWuv/589/y8nLy8vK0R+n+/Wz+/XfK\ns7OxnjmDLj8fr4ICfIqLMZhMGMrKCLZYCLVaa9yPm1e9kT81JQkucPK6slqJA/QVFVBRAaWlTpPe\n6vYDHf98HhAQoPXehoSE8EhmJu0rKigPCMAaGIgKDISgIHTBwXiFheEVFoZPWBi+ERH4t2mDf1QU\nhqgoAqOjMUREyN9I0SpJwiqEcBtreTnbFy6k7b/+xQiz2WGd38PDafvdd4wcMcLF0QkhhGhp3t7e\nREVFERUVVfnCkCFw8831Wra0oICCEycoTEmh+ORJbggO5lKlyM3NJS8vT/u37fHj7Nu3j0CzuTLZ\nVYr6zi7rLGENAhqbGlZdp8lkwmQycfr0aQC6Q633ITtTAXjrdAQFBxMUFERQUBDBfz6fnZNDP6OR\nCoMBa0BAZRIcHKwlwd6hofhERPwvCW7TBkPbtgRGR+MfFiZJsHA7SViFEC5XXlrKjsceI+Lddxla\nUuKwToZez4l77+Wil16SP5ZCCCFq8A8Nxb9/f6L79wegfz2XU1YrRVlZGFNSKExJoSQ9ndKMDCyZ\nmVScOQNGIzqjEX1REfrISMYHBWE0GikoKKCgoACj0Uiok79d9VFbP2xoLWW1KQKsSmE0GjFW6+m9\nFRjWyPX20+tJrZIE2xLhscXFXJyZSbnBgAoIQAUFVfYEh4TgFRKCV3AwPqGheIeG4hsWhm9YGP4R\nEfhFRGCIicEQGoqXl1cjoxLnGklYW7lNl11G+2nT6DZ3buUlOkK4kdFoZMn773PxY48xzEmPqhnY\nMmQIF3zzDUPbtXNtgEIIIVo9nV5PULt2BLVrV9mjW4uRwMMOXjeXlnImNZXi9HRMGRmUZmVRlpWF\n5cwZKnJzUfn5UFCAvqgIr+JifE0mfMvKMJjNZPr4EOblhdFoxFrtNpimJKzONGXit3yrVUvUq7qI\nxifBVwLLAX9/fwICArTHEOChjAwsPj6U+/pS7udHhb8/yt8fFRAAAQEQGIg+MBCv4ODKR0gIPqGh\n6BMS8O7SRVtXYGAgBoMBvfzg3SrolPpztmnRauTl5RERUXnHRi6Vgw1UAMf9/MiMjaW8Vy9CRo2i\n/cSJtO3XT3qvRItSSrFjxw4++OADPv/8cwoLC/k78ES1ehXA1oQEOn7wAfF13PskhBBCnO2UUhQX\nF9v13Hr/979YsrMpP3OGioICKCyEoiJ0JhNeJhPeJSX4lJXhZzbjZ7HgX1GBoaKCDKCfk1P6/wCN\nHVc/Asf3Bv8D+Esj1zkF+MHB69OA7xq5zmeApxy8Hufnxx6zmVK9nlIvL8q8vLD4+GD+Mymu8PXF\n6uuL1d8f5ecH/v4QEIDOYKA4Lo6cwYMxGAz4+/tjMBi0R0hWFv5BQfiFheEXFoYhIgKfgMbMctyy\n7HKC3FzCwxsyDrfnkIS1FXKUsDpTCJw0GMiLisIcE4MuLg4GDMB/wgTi4uKIjo6WEexEo5zctInv\nfv6ZN1es4ODBg3Zl7YAU0O4h2hIfT7t33qHL5MmuDlMIIYRoFSwWC8XFxRQVFVFUVERhYSFFRUX4\nbt+O7tQpyvPysBYWakmwvrgYfUkJ3qWllUlwWRl+5eUYyssxWK0EKUUwlfPdVvcacE8j4xwDrHfw\n+tXAF41c56PACw5e7wicaOQ6VwEznZRlAVHVXiuncrqkUp2OMr0es5cXZi8vLH8myRXe3uQFBvLx\nsGH4+/vbPfz8/Oh35AjhJSXoDAb0AQGVPckBAXgHBWn/+gQH4xMcjG9ICH6hofiGhOAfFoZvUJDD\nDqjWkrBKJnKOCwbOKymB1NTKx/btfLJiBdf99a9A5bDt7dq1IzY2loiwMJ46dIjyoCCsISEQHo4+\nIgLvqCh8IiPxa9MGv8jIypv1o6IIiI4mMCpKenDPEcpq5cTataS8+iptN2ygl8lEFnDQQd3TwEde\nXvTs1o32//wnw6ZOdXG0QgghROvi4+NDWFgYYWFh9gVNmLe8yGx2mATr9+5l89GjVOTnYzUaoahI\nS4K9SkrwMpvxMpvxNZvxKS/Hr7wc34oKDFYr5b6+lVMJVdOU/kmTk9cDm7DO2u5SNjh4zZvK8+pg\npSpHjq6oqFHnaHY2n5044XCd64DRDQ8TgDCg1M+vRiJ8dYGzYcPOLpKwtnJbu3Shx8mTJDi5X9CR\nqpNrW61W0tPTSU9PJ5SG369gpfK+il/9/HggPt5u1DrbY1ZiIoF6vd2w7b4xMRji4wnq2JGwrl0J\n7dgRvfT0epyClBQOvfsuZd9+S6cjR+hcXk7nKuVXAH+rtky7du1YsGABly9Y8L9RIYUQQgjhcXx9\nffH19a3ZMzduXKPXuYnKy6FLSkq0UZJNJhNlaWnsOXwYS0EB5UYjFUYjFYWFqOJirEVFYDKhLylB\nV1KCvrQUL7MZb1tS3KYNPX19tXUVFxdTUlLSpCS4oQlrfZTWUubfyHXa1ltWVkZZWZnd/cZ+TVin\nJ5EMoJUbunMn4eHh5Ccnc+LbbynYsAH9/v1EnjpFvMnk8Eb8Uw5eg9ovLXZGT+WvTdayMpKSkhzW\n+QcQU8d6KoAcnY4CHx+K/P0pDg2lLCqKnVdfTWz79sTGxhITE0NsbCzBwcHodLpGRCtqo6xWUv/7\nX06uXEn5hg20S0qiW2kptQ1X0QfoCSR5ezN16lRuuukmLr30UrnMXAghhDiH6XQ6bYAkTc+eMGFC\no9b38p+PqqxWK6VFRZzJyKDkzBnKcnMx5+djzs/Hkp9fmRQXFWEtLsZaXIwymaCkBEpK0JWWYo2I\nYG779pSUlNg9zCYTqYmJ+Fqt+Fmt+CuFAajPmMctlbDW7K+u1FoSVrmHtRWq7/Xqymola+9eMtav\np3D3bipSU9FnZLA6PJy1JhOnTp0iJydHqz8Q+L2RMX0CXOekzEjjRrDLpPJeyOqCg4N50d+f83U6\nStq2RcXH49utGyF9+xJ1wQVE9ekjvbV1KC4uJjExkYMHD7J//35GfvopIzMyCGvA14UV2BcSQuJN\nNzH20UeJjo5uuYCFEEIIIdxEWa1YTCZKcnMpy8/XHhajUXtUmEyU6PWkde1KaWlpjcdFmzYRnJuL\nzmxG/+cl1V4WC17l5XiXl+NdUYHPnw9fq7UyYVYKHRDiJK5/AXf9+fxsvodVEtZWqDlvsC4rKyM9\nPZ3MzEyKU1MxbNxIeXY21txcdPn56IxGfIqK8Cktxddsxt9iwb+8HENFBYFKaZdivAMscLB+HZU3\nqTfmLtfdwPlOymobEa8MyPDx4UxQEKY2baho3x7vhAQCe/Ui8vzzaXfRRfj6N+V3rrODslrJOXiQ\nzG3byN++ndIDB3g1PJwDBw9yotr9FUuAm+qxzjJgf0QERePG0eOxx2h3vrN3SAghhBBCNAer1UpZ\nWVmNJDhvzx6GX3MNIAmr8DCeNCJYhdlMcWYmRcXFFOp02k37thv3iwsK6Prjj6gqw7Z7FxbiW1RE\nQEkJwWVlhJWXO/zl6Acqh0Z35BjQpZExBwKhMTHEx8fToUMH7dELiA0JIaxnT9r07o1fiLPfszxD\ncVYWOfv3U3DoEEWHD2M+cgR9aiqGrCwijEZizeYa92BEUznyXXV3AG86aeeonx/pPXsSMHMmve+4\ng4A2bZp1O4QQQgghRMN5Uk7QFJKwtkKt5eCsylxURF5SEgXHjlF07BimpCTSLRY2h4Rog0JlZGSQ\nnp6OpayMUsCnEe1kA22dlC3F/rLmPJ2OXB8fjAEBmEJDsURGQlgYuvBwvCIi8GnTBr+2bfGPjsZ3\n0CBCIiLw9fXFx8cHHx8fvLy8nN5ra7u0xFxUhKW4mPKSEkyZmZjS0yk5fRpzdjaHu3Ujt6iIgoIC\n8vLyOH36NKdPn2bOvn1ck5fn9PKQ2owFfnXw+hBgG5APJIeFUdCzJ4GTJtFt3jzCOnd2sIQQQggh\nhHCn1pITnNM38hmNRtatW8fJkyfx8/OjW7dujBo1Ci+v+tw27XnttGa+QUFEDxhA9IABdq9fVa2e\nUoqs06c58tNPFOzbh/nIEfizVzEsP5/osrJa78NMqyWGDtX+H64U4WYzmM2Qnw8pKU6XDaXyXt3q\n3tLpuF4pbY4znyoPX/43T6kjl1M5PUx1E3F+L0NdelOZsOp0OhISEujVqxe9e/em/3nncaJdOzqM\nHctAuf9XCCGEEEK4yDl55mk2m3n66ad55ZVXKC21H68rJiaGN954g1mzZp017Yj/0el0RMfEEH3D\nDU7rGE+e5PT27eTv2UPJoUOoEyfwPX2akNxcDimFrqQERxcexDcyJitQ6KTMr8p9vg0ViuOENaMB\n6zACGf7+5IWHU9q5M1MvuYT5l11Gjx49MBgaO2i7EEIIIYQQzeOcuyTYarVy5ZVXsnLlSgD69u3L\nqFGjKC4uZvXq1Zw5cwaAxYsXM3/+fI9vx5HW0v3vLmazmVOnTpGWlkZqaiqpqamkpaUxb9UqwoxG\nwsvKiLJa6zV8OUABlRM6O1L9MuOGGALscPD6LGA5YAayvbzI9/enKDiY0uhoVKdO+PfoQdiAAbS7\n6CJCO3ZEp2/MkFdCCCGEEMKTtZac4JxLWN98803uuqtygOeFCxfy/PPPa2X5+flMmTKFLVu24OPj\nw8GDB+natatHt+NIazk4PVmF2Uzu0aPkJSZSePQoJcnJlJ88Cfn56AoL8S4qwsdkwq+0lCKrlTkR\nERQWFmKxWLBYLJSXl1NRUcFnwDVO2jD/+SjX6bAAJV5emHx8KPX1pcxg4LsLL6SwUyfCwsIIDQ2l\nbdu2xMTEEBMaSkxYGOEJCZKMCiGEEEKco1pLTnBOJaxms5kOHTqQmZnJhRdeyPbt22sMepOSkkL3\n7t0xm81cd911LF261GPbcaa1HJytndVqpbykRBtUSVmt+AQG4hsUhLe/vySbQgghhBCi0VpLTnBO\nnRH/5z//ITMzE4C77rrL4QitHTt25LLLLgNg1apVNe499aR2xNlNr9fjGxhIYNu2hHbsSFjnzgS2\nbYtPQIAkq0IIIYQQQnCOJaw///yz9nzChAlO69nKioqK2Lp1q8e2I4QQQgghhBCt2TmVsO7fvx+A\nNm3aEBMT47Re//79ayzjie0IIYQQQgghRGt2Tk1rc+LECQDi42ufoCQuLk57npyc7JHt5OXlOS3L\nzc3Vnufn5zdovUIIIYQQQoizX9U8wGq1ui+QJjqnElaj0QhASEhIrfWqlhcWOptB073t2G6grkuX\nLl0atF4hhBBCCCFE62I0GomMjHR3GI1yTl0SXFJSAoCfn1+t9aqW25bxxHaEEEIIIYQQoi4FBQXu\nDqHRzqkeVn9/f4xGI2azudZ6ZWVldst4YjtVL/utLicnh+7duwOQlJRU795Y0Xzy8/O13u3jx48T\nFhbm3oDOMbL/3U/eA/eS/e9+8h64l+x/95P3wP1yc3Pp2rUrAO3bt3dzNI13TiWsISEhZGVl1Xn5\nbVFRkfY8ODjYI9up7zxKERERZ+2cS61FWFiYvAduJPvf/eQ9cC/Z/+4n74F7yf53P3kP3M/b++xN\n+86pS4I7duwIQFpaWq31qpZ36tTJY9sRQgghhBBCiNbsnEpYe/fuDUBWVhZZWVlO61WdYsa2jCe2\nI4QQQgghhBCt2TmVsF588cXa83Xr1jmt98svvwBgMBgYNmyYx7YjhBBCCCGEEK3ZOZWwTpgwQRuA\n6K233nJYJyMjg1WrVgEwbdo0AgICPLYdIYQQQgghhGjNzqmE1d/fn8ceewyATZs2sWjRIrtyk8nE\nvHnzKC0tRa/X8+STTzpcz+LFi3n++ed59dVXW7QdIYQQQgghhDiXnb3DRTXSfffdx9q1a/n555/5\ny1/+wg8//MCYMWMoLi5mxYoVpKamAvDSSy85va/05Zdf5vDhw4SGhnLfffe1WDtCCCGEEEIIcS7T\nKaWUu4NwNZPJxIMPPsh7772H1Wq1KwsNDeXll1/m5ptvdrp8z549tYQ1Pz+/xdoRQgghhBBCiHPZ\nOZmw2mRkZPDjjz+SlpaGn58f3bt3Z9KkSRgMhlqX+7//+z+ysrIwGAw89dRTLdaOEEIIIYQQQpzL\nzumEVQghhBBCCCGE5zqnBl0SQgghhBBCCHH2kIRVCCGEEEIIIYRHkoRVCCGEEEIIIYRHkoRVCCGE\nEEIIIYRHkoRVCCGEEEIIIYRHkoRVCCGEEEIIIYRHkoRVCCGEEEIIIYRH8nZ3AOcqk8nEd999x7Zt\n28jOziYyMpLBgwczY8YMgoKCWqzdv/zlLxw5cgSAhIQEXnjhBY+NtaUlJSWxYsUKkpKSMJvNdOjQ\ngWnTpjF48OAWa/PYsWM8+uij2KY/vuuuuxgzZozDukuWLGHNmjV1rjMyMpJ33323OcN0ifLycn75\n5RfWrVtHRkYGISEh9OnThyuvvJKoqKgmr//qq6+moqKiznrXXHMNs2bNcmus7pKZmcnXX3/NgQMH\nKCoqIiYmhosvvpjx48fj5eXVpHWvXbu23sfl0qVLCQgIaJF1eLotW7bw/fffc/LkSfz8/OjWrRtX\nXnklnTp1ava2Dh48yL///W+Sk5MpKioiPj6evn37Mm3atHp9l7syVlcpLCxk1apV7Ny5kzNnzhAV\nFcXw4cOZNm0a/v7+zdpWWVkZv/zyCxs3biQjIwN/f386derEuHHjGDJkiMNlDh06xBNPPFGv9T/7\n7LP07NmzOUN2iYMHD7Jy5UqSk5OxWq106tSJGTNm0L9//yatd/PmzbzyyisNWmbhwoVceOGFLo/V\nncrKyvjhhx/YuHEjWVlZhIWFMXDgQGbNmkVYWFiT1v3Xv/6VxMTEetd3dD5TWFjIjTfeWK/lFyxY\nwPjx4xsUoydITU1l+fLlHD58mJKSEuLi4rj00ksZMWIEOp2uWdowm82sWbOGbdu2kZ6ejsViISIi\nQvsbEBsb6zGxOqSEy61bt061b99eATUe0dHR6ocffmiRdr/88ku7tgYNGuSxsbYki8Wi7rvvPqXT\n6Rxu17Rp01ROTk6zt2u1WtXo0aPt2vrwww+d1r/zzjsdxlf90b59+2aPtaUdPHhQ9e/f3+H2BAQE\nqLfffrvJbXh5edVr//397393e6zu8Nprryl/f3+H2zVo0CB15MiRJq3/7bffrtf+B1ReXl6LrcNT\nZWRkqAkTJjjcFi8vL/XYY4+pioqKZmnr+PHj6tJLL3W67wwGg/r00089IlZX+vbbb1WbNm0cblfH\njh3Vhg0bmq2tzz//XMXExDh9D7p06aKKi4trLLdx48Z6fwY2btzYbPG6gslkUjfffLPT7ZkzZ44y\nGo2NXv/nn39e731ne6xbt84tsbrL9u3bVdeuXR1uU1hYmPriiy+atP7hw4c3aP+fd955NdaRnZ1d\n7+UXL17cpHhdzWq1qqeeekp5e3s73J4xY8aokydPNrmdb775ptbvHy8vL3Xvvfcqs9ns9lidkR5W\nF9u6dStTpkyhtLSUqKgobr/9drp3786xY8d49913ycjIYPr06fz444+MGzeu2drNycnh7rvvBsDP\nz4+ysjKPjbWl3XbbbXzwwQcAjBkzhquvvho/Pz/Wrl3LF198werVq5k6dSr//e9/m/UX9rfffpv1\n69fj7+9PaWlpvZeLiorirbfeclp+tvUqpaWlMX78eDIyMggICOCWW25h0KBBZGVl8f7773Po0CEW\nLFgAwO23397k9q699lpmzJjhtLxPnz4eE6urvPLKKzzwwANA5fbfdNNNREZGsmPHDpYsWcKuXbsY\nP34827dvJyYmpsntLVu2rNbPUmBgoEvW4SmKioqYNGkSe/bsQa/XM2/ePEaPHk1xcTGfffYZW7Zs\nYdGiRZhMJl599dUmtbV//37Gjx9PVlYWPj4+zJw5k1GjRhEeHk5ubi7bt2/n22+/JTU11e2xutKP\nP/7IzJkzqaioIC4ujttuu41OnTqRmJjIu+++S0pKCpdeeinr169n0KBBTWrrmWee4amnngKgY8eO\nzJ49m549e+Lt7U1ycjI///wzmzdvxmw21/p9/txzz9GtWzen5eedd16T4nQlpRRz5szhm2++AWDy\n5MnMmDEDvV7P6tWr+fbbb/nss884c+YM33//faOu+BgxYgRff/11rXXKysq44YYbKC8vJzo6mlGj\nRrklVnc4cOAAEydOpKCggLCwMG677Tb69OlDWlqa9hmYM2eO9r3RGM8++yw5OTm11vn+++/56KOP\nALjqqqtqrXvnnXc6vSoN4IILLmhoiG716KOP8uKLLwIwZMgQ5s6dS1BQEBs3buTjjz/m119/ZeLE\niWzZsoXQ0NBGtbFmzRpmzpyJ1WrF29ubuXPncuGFFxIQEEBSUhJLlizh9OnTvPbaa+Tl5fHxxx+7\nLdZatVgqLGooLy9XPXr0UICKiYlRqampduXp6emqY8eO2q+7paWlzdb27NmzFaBuvPFGlZCQoKD2\nHlZ3xtqS1qxZo/0adPvtt9cof++997TyZ599ttnaPXHihAoKClJ6vV4988wzWhv16WHt2LFjs8Xh\nCaZPn64A5evrq7Zt22ZXVlxcrEaMGKEA5e/vr9LS0hrdjq2HddGiRR4fqysdP35c+fr6KkCNHz++\nxmd3/fr12i+os2fPbnQ7VXtHCwsL3bYOT7Rw4UJtuz7//HO7soqKCjV37lytfNOmTY1ux2g0qvj4\neAWo+Ph4tX//fof1TCZTje94V8fqSsXFxSo2NlYBqlu3biorK8uu/NixY1rPa79+/ZrUe/zFF19o\n++emm25SJSUlDusdP37cYe9G1R7WrVu3NjoOT7Ns2TJtux5//PEa5c8995xW/s4777RYHF999ZXW\nzkMPPeTRsTa3iy66SAEqNDRUHTp0yK4sNzdX9enTRwEqMjJS5efnt1gco0aNUoDS6XTq+PHjNcqr\n9rDWds50tvntt9+07brqqqtUeXm5XfmKFSu08vvuu6/R7fTr108BysfHx+F3dFFRkd1VZPv27XNb\nrLWRhNWFql6esnTpUod1qr7p77//frO0+8033yhARUVFqTNnztQrYXVXrC3NdnlKdHS0MplMDuuM\nHTtW+xJvrkTcdjndvffea5c0n2sJ6759+7Rtf+SRRxzWOXTokHa5dlO++JqasLoyVle67bbbtEuA\nkpOTHda5++67tROIxl4aLAmrY0ajURkMBgWoyy67zGmd0NBQBahJkyY1uq17771Xe693797t0bG6\n0htvvKEdV85ua3n33Xe1Ot98802j2jEajSoqKkoBauTIkcpqtTZ4Ha01YbX9IN69e/caJ79KVV5+\naDuJjo+Pb9S+q48pU6Zo+/fAgQMeHWtzWrt2rbbd//d//+ewzvr167U6zz//fIvEcfz4ce1v6Nix\nYx3Waa0Jq+0H8cDAQJWbm+uwzpVXXqkA5efnp86cOdPgNoxGo7bvZsyY4bRe1R9uHN3m5IpY6yKj\nBLuQ7dKU4OBgp5c9TJ8+XRvEpa5LWeojLy9Pu2TxlVdeISIiwmNjbWnp6els2bIFqLxM1GAwOKw3\nf/58AAoKCli7dm2T233//fdZu3Yt8fHxPPvss01e39ms6nFi28/V9ejRgxEjRgCwfPlybYAqVzub\nYq0vq9XKqlWrABg/frzTwXJs26uUYsWKFa4K75zw73//m5KSEsD5cVX1e3fdunXk5eU1uJ2ioiKW\nLFkCwOzZsxkwYIDHxupqts92XFwckyZNcljn2muv1S5Bb+zft08//ZTs7Gyg8nLeFh2Q5Cyyb98+\nDh8+DMANN9zg8BJanU7HTTfdBFTemrF9+/ZmjyMzM5OffvoJgMGDB9OrVy+PjbW52Y5pLy8vbrjh\nBod1Ro0apV2C3lLneB9//LH2d7O+Ayu1BkVFRdqxN2vWLMLDwx3Ws33vlpWV8d133zW4Hb1er33v\n1DaoUlxcnN0y7oi1LpKwutCGDRsAGDp0KH5+fg7reHl5aSfAGzdubPIJ8P33309GRgYTJ07k2muv\n9ehYW1rVGGu7B6Jq2fr165vU5qlTp3jooYcAePPNN8/qUZWbg+24io2NrfVeLNt7cPLkSY4fP+6K\n0Go4m2Ktr8TERLKysoDaPwN9+/bVftxq6mdA2LMdVzqdzuH9cja298disWg/tDXE6tWrKSoqAmjQ\nd39VrorVlSwWC1u3bgUqT8idJZGBgYHaaLGN/Qx8/vnnAHTo0EH7Wyn+d1yB6/4WO7Js2TLKy8sB\ntISzOk+JtbnZtqt///5OExD433b9/vvv2vdJc1FKsXTpUgBCQkLqHK2/Ndm+fbs2lkltx9WIESPw\n9q4cbqgxx1VgYKA2cviOHTuc1tu2bZv2vPp9wK6KtS4y6JKLZGVlaTee1zUwgu3gMplMnDhxgs6d\nOzeqzR9//JGPP/4Yg8HA22+/7dGxusKBAwe057VtV2xsLCEhIRiNRg4ePNikNm+77TYKCgqYNWsW\n06ZNa9Q6TCYTL730EomJiZSWlhIZGcmAAQOYPHky7dq1a1J8rmZ7D+p7XEHlNAIJCQmNbvP3339n\n4cKFnDp1Ch8fH+Li4hg+fDgTJkyodXAMd8Ta0ur7GdDpdPTo0YOtW7c2+TMA8N5773H48GEKCwsJ\nDQ2ld+/eXHLJJbX+ENAS6/AEtvcgNja21oEpqh9XU6ZMaVA7thMQnU7HsGHDKCkp4ZtvvmHz5s3k\n5eURERGhTU8WHBzs1lhd6ejRo1gsFqB+n+2NGzdy8uRJCgsLne4nRyoqKti5cycAw4cPB+D48eN8\n9dVXHD58GKUUHTp0YOLEifVOZr/55hs+/fRTzpw5Q1BQEAkJCVx88cVNHhTK1er7PdSjRw90Oh1K\nqWb5HqrONriMv78/s2fPdljHU2JtTmazmaSkJKD+f9+UUiQmJtY65U9DrV+/nuTkZKByGrr6DCC5\nceNG9u3bx+nTp/H396dDhw6MHj2a0aNHn1VXMNT3uAoICCA+Pp7k5ORGH1cvvPAC06dPZ+fOnSxc\nuJC//e1vdgMY/vTTTzz55JNA5dU4559/vttirY0krC5y6tQp7Xn79u1rrVu1/OTJk41KAo1GI7fe\neisATz/9NF26dKn3sq6O1VUasl2xsbEYjUZOnjzZ6PaWLVvG999/T2hoKG+88Uaj15Odnc3DDz9c\n43VfX18WLFjA888/3+zzBbYEi8WiXR7X0OOqKZxdytSxY0feeustJk+eXKPMXbG2tMZ8tptjmx58\n8MEar+l0OmbNmsXbb79NmzZtXLIOT2B7D1r6uLLNfdi2bVsOHTrENddco50cVhUREcEbb7zBnDlz\n3BarKzXl71tDRuFNSUnBZDIB0L17d5566imee+45rUfP5u9//zsjRozgs88+Iz4+vtZ1Ops3fcSI\nESxZsoTu3bvXOz53sr0HAQEBtc7z6efnR2RkJDk5Oc1+XO3cuZP9+/cDMHPmTKc/yHhCrM0tMzNT\nOw4b+hlozoTVNjIw1P9yYNsMD9Wdd955LF68WPtxyNM19HsoOTm50cfVtGnT+O6777jnnnt48cUX\nWbJkCb1799ZGCU5KSiIgIIAHHniA559/3q2x1kYSVhcpLCzUntc1/ULV8sZegvHQQw+RlpZGv379\ntOkr6svVsbqKbbt0Op3T+1dtbNvV2G3KzMzkvvvuA2DRokWNnhokNjZW60WKj4+nrKyM3bt38/nn\nn5Obm8trr73Gnj17+Omnn/D19W1UG67i6uNKr9czePBghg8fTocOHYiMjCQzM5O1a9fy448/kpKS\nwtSpU/nwww+5/vrr3RqrqzRmu6xWKyUlJXV+ZhwJCwvjkksuoVevXnTo0AGdTkdiYiJffPEFKSkp\nLF++nN27d7N161btfviWWIcnsb0HLX1c2e4lraio4JJLLqGgoIDLLruMKVOmEBAQwO7du1m8eDG5\nublce+21WK1W5s6d65ZYXclVn+2q9/J++umnJCUlER4ezq233krfvn0xGo18++23/PTTT2zatImR\nI0eya9cuIiMja6zLYDAwbtw4BgwYQMeOHTEYDBw/fpxVq1bxxx9/sGnTJgYPHszGjRvp27dvg+J0\nh/oeV7Y6OTk5zX5cVZ26o7ZkyRNibW6e8PetuLhYGx+hR48eDB06tNb6Xl5ejBw5kiFDhtChQwfC\nwsI4efIkP/zwA+vXrycxMZExY8bw7bffOvwR2tM05j1oyv6fOHEizz77LA8//DDp6el2l7rrdDru\nuOMO7rvvPnx8fNweqzOSsLqI2WzWntuu8XamauJRn/lSq1u3bh2LFy9Gr9fz3nvv1dleda6M1ZVs\n21Wf/WHbrsZu0x133EFubi5Dhw5t9PycDz/8MK+//nqNG+Chcm6z2bNn89NPP/Hrr7+yaNEibZ4/\nT+Xq4+rw4cMOe/zvv/9+fv31V2bMmEFBQQG33XYbY8aMoWPHjm6L1VWasl0NTVinT5/ODTfc4LD3\n/+9//zv33HMP77zzDseOHeOOO+5w2BPeHOvwNPX9HmrqcWUbLMl2e8c777zDbbfdppXPnTuX22+/\nneHDh5Odnc2dd97J1KlT7XqRXBWrK7nqs23b/wBJSUnEx8ezefNmu17UBQsW8MILL/Doo4+SkpLC\nY489xnvvvWe3nj59+nDq1CmH9xk++eST2pzKBQUFXHPNNezZs8fj5wF15d9iZ+1Xvb+4tnnk3R1r\nS/CEv2/Lly/Xkpq6eldDQkJISUlx2Lv3yCOP8M033zB79mzKysqYO3cux44dq/W+XE/QmPegsfs/\nKyuL6dOns23bNvz8/LjxxhsZPnw4BoOBo0eP8tFHH/HSSy/x7rvvsmLFCiZMmOC2WGsjgy65SNVr\n8+t6I6v+oavPr3pVFRcXc8sttwCVEywPGTKkQcuD62J1Ndt2WSwWrFZrrXVt29WYbfryyy9ZuXIl\nPj4+vPfee42+r6Jjx44Ok1Wo7HVauXKlNrLba6+9RkVFRaPacRVXH1e1XZ4+ZswY7cSwrKyMN998\n0668tX8GoOW3KyYmxuml6j4+Prz11luMHj0aqDx5SUlJaZF1eBrbe9DS+7/qez1u3Di7ZNWmW7du\n2sjlRqORL774wi2xupKrPgPV78d7+eWXHV7yu3DhQu2esWXLlmmDm9iEhYXVevJ9//33azMBHDhw\nQBvN05PV97iCpv0tdmb16tWcOXMGqBz519nfWXB/rC3BE/6+2S4H9vLyYt68ebXW9fX1rfVS1Bkz\nZvDiiy8ClVc2fPjhh80WZ0tpzHvQmP2vlGLq1Kls27aNkJAQtm7dygcffMDNN9/MnDlzeOqppzhw\n4AAXX3wxhYWFTJ06tcb9p66KtS6SsLpI1fsj8vPza61bUFCgPQ8JCWlQO3/9619JTk4mLi6Of/zj\nHw1a1sZVsbpa1e2qGrcjtvKGblNBQQF33303UNlD2qdPnwZGWX8BAQHacPR5eXns2bOnxdpqDkFB\nQdqJgSccV1deeaV2Cemvv/5qV+ZpsTaXxny2/f39HV4m1FQ6nU470YbGjSrYHOtwNdt70NLHVdX3\neubMmU7rXX755dpz2+i51ddxrn8GoOHbVbUdb29vpk6d6rSu7T0oLS1l9+7dDWoHKn+ctqn+XeaJ\nbPvGaDTW+eNxY/8W18aWLOl0uhq3g1Tn7lhbgrvP8U6cOKF9V0+aNKnRt0xVNX/+fK1372z6DED9\n34PG7P+1a9fy22+/AfCXv/yFgQMH1qgTEBDABx98gJeXF2azmZdeesktsdZFLgl2kS5duqDX67Fa\nrXX2AlQt79q1a4Pa+f3334HKE25nl1mcPn0aqByx8IorrgAqr2+3DdLkqlhdrerorSkpKU5/tbZa\nrdoN4w3dprS0NG2wnj179mj7t6rMzEzt+Ztvvsm///1voHKe3LoG3aiuR48e2vOMjIwGLetqer2e\nzp07c+zYMY84rnQ6Hd27dyc7O7vGvvO0WJtL9c/AyJEjnda1bVdLblNzHL9n02cAKt+DQ4cOkZqa\nWmu9ph5X3bp145dffgEqL3t0JioqCoPBQElJid13kytjdaXqn4Ha2Mp9fHzsbhmoj44dO+Lr64vZ\nbKZt27a1XlJf9f2p/h7Ux9n4GQAoLy8nPT3dbg7IqvLy8rT755rruMrKyuLHH38EYPTo0XUOSOnO\nWFtKdHQ0wcHBFBYWuuXv29KlS5t97lXbCLXHjh07qz4DULmPaxvtvil/izdt2qQ9v+SSS5zWi4+P\np3fv3uzdu5fNmze7Jda6SMLqIgaDgYSEBI4ePVrnL6i2pDMmJsbhAAz1cejQIQ4dOlRrnby8PO2m\n96ojbLo6Vlfp16+f9nz37t0MGDDAYb19+/Zp0x40ZQCL77//vs46O3fu1KY+ePrppxucsFa9PMPT\nB12Cyvfg2LFj7Nu3j/Lycqf3Q9iOK51O16K91Lb952jfeVqszaHq8bx79+4ag+zYlJaWat8fLTmI\nS3Mcv2fjZ+D7778nPz+f5ORkp5eu244raNx70L9/f+151XuQqlNKad931efcdlWsrhQXF0d4eDh5\neXn1/vvWs2fPBo8F4e3tTa9evfjjjz9q3f9g//44m/e8NmfjZ8Bm9+7dTpPAljiuPvnkE22E3Pok\nS+6MtSX16dOHrVu31vszYDAYmjVhBYiMjGz0dH+O1Pb33NNUP64uvvhih/XS09O1udMbc1xVHfyo\ntqnJAG38guoDJrkq1rrIJcEudOmllwKVPW/p6ekO65w5c0ab3LcxI5397W9/4+uvv671YZu7s0uX\nLtpr1e9vckWsrjZq1CjtuvoffvjBab01a9Zozxu6XR06dKhz/z/++ONa/TvvvFN7vbZeEGeq/nrm\n6b/qwv+OK5PJ5PTyTaWUdh/W4MGDW+yHEKPRyL59+wDH+86TYm0u7du31/74VD3Oq/vll1+0k+iW\n/Gw3x/F7tn4GoH7fQ5GRkY0ai2Dy5Mna/fN79+51Wi8xMVE7ga8+h7CrYnU123Zt2rQJo9HosE5y\ncjKHDx8GGv8ZsM1Hm5OT4/TvKNi/P42Zx/ls+wxMnDhRGxiqvn+Lqx6LTWEbHTg4ONjhFVDVuTPW\nlmSLMTU11W6ezapKS0u1y2ur7oem2LBhA8eOHQPg2muvbbbkMiUlpdFXxrnDwIEDtUuhazuuqpY1\n5nuobdu22vO6etNt055VH23fVbHWSQmX2blzpwIUoB588EGHdZ588kmtzvr161skjoSEBAWoQYMG\neXysze2GG25QgPLx8VFHjhypUV5UVKQ6dOigANW3b98WiWHNmjXafvvwww8bvZ49e/YoX19fBag+\nffo0X4AtKCcnRwUFBSlAXXLJJQ7rfPXVV9r+efPNN1sslvvuu09r51//+pdHx9qcXnrpJS3m1atX\n1yi3Wq1qzJgxClBhYWGqoKCgReLIyspSsbGxClBBQUGqsLDQLetwtYqKCu07uHv37qq0tLRGnT/+\n+EPp9XoFqLvvvrvRbY0fP14BqkOHDqq4uNhhnTvvvFM7HtatW+e2WF3pxx9/1LZ50aJFDuvccccd\nClA6nU7t27evUe0cOnRIeXl5KUA98cQTDuvk5uaqNm3aKEB169atwW2UlZWpIUOGaNvT2FhdberU\nqQpQwcHBKj09vUZ5Tk6OioiIUIAaO3Zss7S5a9cubT/Nnz/fo2NtaUlJScrb21sB6vrrr3dY5/XX\nX9f214oVK5ql3RtvvFFb5x9//NEs61RKqdmzZ2vrXb58ebOttyU99NBDWsw7duyoUW42m1WfPn0U\noOLj45XFYmlwGxs2bNDamDt3rtN6Vb8T77zzTrfEWhdJWF3syiuvVIDy8vJSH330kV3ZF198oXx8\nfBSgJk+e7HD51NRUNWvWLDVr1iz19ttvNyqG+iSszRGrJzpx4oQyGAxaQpqSkqKVGY1GNX36dO1D\n+f333ztcx7vvvqu9B8nJyQ2OoT4J66+//qoWL16sMjIyapRZrVa1YsUK1bZt21oTD0/1t7/9TYv7\n8ccfV+Xl5VrZli1bVGRkpAJU165dVVlZmcN12Pb/Y4895rD8//7v/9TPP/+szGZzjbKcnBx11113\naTF07drV4Yl4c8XqaUpKSrQfZaKjo9Vvv/2mlZnNZvXggw9q2/ziiy86XMeaNWu092DDhg01yg8f\nPqxefvlldfToUYfLb9q0SfXs2VNr5/nnn2+RdXiqL7/8Uov7mmuusUsmjx49qrp3766dIDv6DlBK\nqXvuuUfNmjVLzZs3z2k7O3fu1BKmKVOmqDNnzmhlFRUV6rXXXlM6nU4BavTo0S0WqycaO3asApS/\nv79auXKl9rrValXvvPOOtl+c7d/9+/drn4FPPvnEaTu33367ApS3t7dasmSJXdnp06e1H4cA9dln\nn9mVFxcXq6efflr9/vvvDtd96NAhbTvqOiH1NHv27NESpqFDh6rMzEytLCcnR9suvV6vtm7d6nAd\nL730kvYeVD22nbn77ru1fbV582aXxuqJFixYoO2PV199VVmtVq3shx9+UIGBgQpQgwcPtiuzycvL\n0/b/Cy+8UGd7xcXFKjg4WAFq4MCB9Y7z2WefVRs2bLD7+2tz6tQpde2112rb4SxWT5STk6PCw8MV\noLp06aIOHTqklZlMJnX99ddr27V06VKH6/j888+192Dv3r01yq1Wqxo0aJC2nieffLLG+c66detU\nVFSUdr5/8ODBFom1qSRhdbH8/HzVu3dv7Y3t3r27mjJlijrvvPPsTqCzs7MdLr9v3z6t3s0339yo\nGOqbsDY1Vk+1fPlyrUfA19dXjR49Wk2cOFGFhoZq2+Xs13CllLrtttu0ert3725w+/VJWN9++23t\n1/0OHTqowYMHq2nTpqkJEyao6OhobXkvLy/16quvNjgGdyovL1dTpkzRtqF9+/Zq8uTJ6oILLtBe\nCwsLU/v373e6Dlu9IUOGOCzv37+/AlRgYKDq2bOnGjNmjLrsssvUkCFDtF5pQHXq1MlpQtRcsXqi\n33//XTtx0Ol0asiQIWry5MkqJiZG266ZM2eqiooKh8u/8cYbWr1ly5bVKN+4caNWHhMTo84//3w1\nefJkdemll6rOnTtrZYC6/fbbHZ5gNMc6PFnVHv6IiAg1adIkNXLkSO3E2NvbW61Zs8bp8j169FCA\nCg0NrbWdxYsXa8lXQECAGjdunJo6daqKi4uz+2531HPUXLF6ooyMDNWpUydtu/r06aOmTJmi/X0E\n1IABA5z22v/3v//V6i1cuNBpOyUlJWrkyJFa3c6dO6upU6eq0aNHK39/f+31hx9+uMayeXl5Wnlk\nZKTq16+fmjhxopo6darq3bu39r4CasKECcpkMjXb/nGFxYsXa/EbDAY1fvx4dfHFF2uJki2RcmbW\nrFlavbS0tFrbMpvNWk929+7dXR6rJzKZTGro0KF2x+aUKVNUv379tNdiY2PtftivKiMjQ6s3ffr0\nOttbunSpVv/111+vd5y2c56QkBDVq1cvNW7cOHXZZZepQYMGad9Bts/w2fSjmVKVyaLtnMTLy0uN\nGDFCTZo0SfsxHFC33Xab0+Uff/xxrd7atWsd1jly5Ihq166dVi88PFxNmDBBTZs2Tfs7YvvB5d13\n322xWJtKElY3MBqNasGCBSogIMDupMvf31/ddNNNKjc31+myrkxYmxqrJ9u8ebNd0lH1C7u2X8uV\nck3CmpiYqBYsWKC6dOlSI0Zboj1jxgyHl2acDSwWi/r73/9u90Vn+xKcMmVKnT3XdSWsy5YtU1On\nTrU7maj6iI2NVU888YQyGo0tHqunOnr0qJo0aZL2443tERUVpRYtWuTw12ybuhLWrKws9cgjj6i+\nffs63P96vV6NHj261isDmmMdnu7999/XerurPoYOHWrX8+1IfRNWpSqTq6q/stsegYGB6p577lH5\n+fktGqunysnJUfPmzVN+fn522xQUFKTuvvtuVVRU5HTZ+iasSlVetvvkk0/W+A4B1Hnnnae+/PJL\nh8uZzWb1zDPPqGHDhmk95dUf/fr1U4sXLz7rfrCx+emnnxx+xnv27Km+++67WpdtSMK6cuVKra6z\ny8BbMlZPVVJSoh566CEVEhJit00+Pj7q6quvVqdPn3a6bEMT1nHjxmnnLzk5OfWO8c0331QTJkyw\n+4Gn+nnbokWLnF4p5en++OMPux+1bI+4uDj11ltv1bpsfRJWpZTKzMxUt956q8NzIp1Op8aOHevw\naqnmjLWpdEr9Oba0cLni4mL27t1LTk4OERER9OvXj+Dg4FqXMRqN/PzzzwB07tyZQYMGNbjdH3/8\nkaKiIiIiIhg3blyLxXo2OHHiBElJSVgsFjp06EDv3r3rXGb37t3aoAETJkyoc+S16jIzM9m4cSMA\nF154YZ3TJZw+fZqTJ09y+vRpvLy8aNu2LX369GnUaJKexmKxsHfvXjIyMggODqZXr141bvh3ZPny\n5UDlIC9jx451Ws9sNpOSkkJGRgZ5eXkEBQXRpUsXp6OdtkSsni4zM5PExESKioqIjY2lX79+dY6I\neuzYMW10ycGDB9c6YFhubi5paWlkZGRQXl5OZGQkffv2JSgoqN4xNsc6PJVSiv3795OWloafnx/d\nunWr1wBsP//8M0ajEV9fXy677LJ6tZWSkkJSUhImk4no6GgGDBjQoEFPGhurpysoKGD//v3k5ubS\npk0b+vfvT0BAQK3LZGdna4Ox9ezZs14jhJeXl7N3717S09Px9fWla9eudU6rYlNYWEhqaioZGRmY\nTCbCw8M577zz7Eb4P5slJSVx/PhxlFJ06tTJbqoeZ7Zt26YNtDNlypRapw7as2cPR48eBWDcuHFE\nRES4NFZPV1payt69e8nMzCQsLIw+ffo4nfrPpqysjNWrVwOVA/oNHTq01vorV67EarU26Nyzeoy2\nv+cFBQWEhobStWtXpyM3n21OnjzJkSNHKCkpIS4ujr59+2rzwTtz4MABEhMTgcqBRasOsuSIxWLh\nwIEDZGRkYLFYiIiIoE+fPtoIwS0Za1NJwiqEEEIIIYQQwiPJtDZCCCGEEEIIITySJKxCCCGEEEII\nITySJKxCCCGEEEIIITySJKxCCCGEEEIIITySJKxCCCGEEEIIITySJKxCCCGEEEIIITySJKxCCCGE\nEEIIITySJKxCCCGEEEIIITySJKxCCCGEEEIIITySJKxCCCGEEEIIITySJKxCCCGEEEIIITySJKxC\nCCGEEEIIITySJKxCCCGEEEIIITySJKxCCCGEEEIIITySJKxCCCGEEEIIITySJKxCCCFEI5SXlzN/\n/nzmz5/Ppk2b3B2Oxzhz5oy2X/744w+Xtfv9998zf/58lixZ4rI2W0JiYiLz58/n8ccfd3coQgjh\nEXRKKeXuIIQQQrjfgw8+SEFBAZ07d671ZNloNPLAAw8AoNPp+Ne//oWfn5/T+n/961/JyMigbdu2\nPPfcc80et7uUlpZiMBgAWLx4MfPnz3dzRJ7hxIkTdO7cGYBVq1YxY8aMFm+zuLiY7t27k56ezqZN\nmxg+fHiLt9lSKioq6NWrF0eOHHHZ/hNCCE8mPaxCCCGAyp6dJUuW8I9//AOz2ey03qZNm1iyZAlL\nlizh/fffZ8eOHU7rFhYWsmjRIpYsWcKxY8daImzhAkVFRVqv6bZt29wdTg0vvvgi6enpXHLJJWd1\nsgrg5eXFk08+CcAjjzyCxWJxc0RCCOFekrAKIYQAYPTo0QCUlJTw22+/Oa23YcOGWv9f1ebNm6mo\nqLBbvzj7lJaWaj9SJCUluTscO1lZWbz00ksAPPHEE26Opnlcc801JCQkcPToUd577z13hyOEEG4l\nCasQQgjAPqGsLQldv349AMOGDbP7vyNV1yMJq2gJS5YswWQy0bVrV0aMGOHucJqFXq9n7ty5APzr\nX/9yczRCCOFekrAKIYQA4IILLiAoKAhwnoQWFxeza9cuAO0+1y1btlBeXu6wvm09UVFR9OrVq7lD\nFuc4q9Wq9UBed911bo6medm259ChQ/z3v/91czRCCOE+3u4OQAghhGfw9vZm2LBh/Pzzz2zZsoWK\nigq8vLzs6mzZsgWLxUJMTAyTJ08mOjqazMxMdu7cyUUXXWRXt6SkhJ07dwIwatQodDqdXbnJZOLX\nX3/lyJEjnDp1ipKSEqKiohg8eDDjx4/H19fXYZzvvPMOO3fuJDQ0lJdffrnWbSosLOSBBx5AKcXM\nmTOZPHmyw3p79+5l3bp1pKWlYTabiYmJYfTo0c3aY9eYNs6cOcPChQsBuOuuuxgwYAAWi4WVK1ey\ne/duTCYT3bp14/LLLycuLq7OGE6fPs2qVas4fPgwvr6+9OrVi6uuuoqAgAD++OMPrTfvn//8J+Hh\n4QD85S9/IS0tTVvHkiVL+PXXX+3WO3/+/Brvf1VNibk269at48SJEwBcddVVTus1x350tI6SkhKW\nL1/Ovn37ABg4cCBXXnkl3t72p1eHDh1i9erVpKamEhYWxuTJkxk6dGit25aQkMCgQYPYtWsX77//\nPmPHjq3XPhFCiFZHCSGEEH/6xz/+oQAFqB07dtQof+KJJxSgrr76aqWUUrNmzVKAeuGFF2rUXbdu\nnbau119/3a7s2muvVf7+/lp59Ud8fLz6+eefHcb40UcfafV27txZ6/Z88MEHWt0//vijRvmhQ4fU\nmDFjnMYxaNAglZiY6HDdJSUlWr3Fixc7jaEpbSQnJ2v1Vq1apfbu3as6depUYx0Gg0F98MEHte6L\n1157TRkMhhrLtmnTRm3YsEGtWrVKey0tLU1bLiEhwWnstseyZctaJOa6PPLIIwpQERERtdZrjpiq\nr2Pbtm2qXbt2NdbRq1cvlZ6erpRSqry8XC1YsEDpdLoa9W699VZltVprjfvuu+9WgIqOjm7cDhJC\niFZAeliFEEJoqt/HeuGFF9qV2y7xHTlypPbvihUrWL9+PY888ohd3druX920aRMBAQFMnz6djh07\nEhsbi8lkIjExka+++oq0tDQmT57M+vXrtXtlbWbNmsWdd95JcXExS5cuZdCgQU63Z+nSpQD079+f\n/v3725Xt2rWLiy++mPz8fAICApg1axYDBw7E19eX/fv388knn7Br1y6GDh3Kb7/9RteuXWvdd440\nZxtpaWncfPPNhIaG8thjjxEXF0dqairvv/++Nvdpz549Hfbcvf7669x7770AxMTEMHfuXDp16sSp\nU6f4/PPPueSSS3jmmWcctrto0SJOnTrF/fffD8DNN99cozfVWW9hU2KuD9vxOGTIkHov0xwxJScn\nM2/ePOLi4rj99tuJiopi9+7dfPTRRxw8eJAZM2awfft2brvtNj788EOuvPJKhg8fTllZGZ999hl/\n/PEH7733Hn369OHuu+922s7QoUN54403yMzMJDExkfPOO6/+O0cIIVoLd2fMQgghPEdZWZkKCAhQ\ngJo2bZpdWUlJifLz81OA2rt3r1JKqV27dilAhYSEqIqKCrv6tl7FiIiIGj1JW7duVRaLxWEMJ06c\nUH369NF6Hx257rrrFKCioqKcriclJUXr2Xr55ZftyoqLi7Uetv79+9v1KFaNo0uXLgpQo0aNqlFe\nVw9rc7RRtVcvJCREzZw5U5WWltrVOX78uAoJCVGAmjJlSo11HD9+XOvNHjJkiMrPz7crN5lM6tJL\nL1VBQUEOe1iVUio7O9thb6ojzRFzfVgsFuXt7a0A9dhjj7V4TFXXERwcrObMmVPj2Pvkk0+0Og8+\n+KDy9/dX69ats6tTVlamBg8erADVvn37Gp+bqhITE7X1LVmypK5dIoQQrZIkrEIIIeyMHz9eASo8\nPNzuZPrXX3+tkYCWl5er4OBgBahdu3ZpdcvKyrTLT2fMmNHgGDZt2qSdqDu6XPaXX37RylevXu1w\nHc8++6wClJeXlzp9+rRd2WuvvaYA5efnp5KTk53GUfWy5uqXFNeVsDZHG1WTpOjoaFVYWOhwHQ8/\n/LAClK+vrzKbzQ7LvLy8VFJSksPls7OztfexORPWxsZcH+np6Vo71X+QaImYqq4jJiZGFRcXO1xH\n586dtXpPP/20wzrLly/X6uzZs8dp3Dk5OVq9Z599ttZtFEKI1kpGCRZCCGHHdvluXl6eNpgM/O/y\nyxEjRmgDKHl5eTmc3mbHjh2UlJTYra8hhg0bRkBAAIDDOWHHjh1LfHw88L/LfqtbtmwZAJdccgnR\n0dF2ZV9//TUAEydOpFOnTk7jGDt2rDb4UPWBhurS3G1cc8012ijO1Y0aNQoAs9nM8ePH7crWrFmj\ntZOQkOBw+TZt2nD55Zc7bbuxGhtzfWRnZ2vPIyMjXRrTNddcox2f1Q0ePFh7ftNNN9VZJzEx0Wk7\n4eHh6PWVp2pVt1cIIc4lcg+rEEIIO9XvY7Xd+2lLSG0n9TYjR47kp59+Yv369dp9jvWZf1Upxfbt\n29mxYwepqakUFRXZTY+jlAIgIyOjxrK2eSoXLVrEd999R35+PmFhYVr59u3bOXz4MADz5s2rsbwt\nCU5JSeH222+3a8/2r+251WoF4OTJkw63w5nmbmPAgAFOy6om5Pn5+dpzq9XKwYMHAWrcj1zd4MGD\nnSb/jdWYmOurqKhIex4YGOjSmKrfD11VmzZtgMpk0/ajSnVRUVH1akev1+Pv74/JZKKwsNBpPSGE\naM0kYRVCCGFnyJAh+Pv7U1payvr167n77ruxWCxs27YNcJywAmzcuBGlFDqdTktuQ0NDHZ7c/+c/\n/2HBggUcOXKkznicnahff/31LFq0iLKyMr7++mtuueUWrcyWeIWGhjJ9+nS75QoKCigrKwMqp5rZ\nu3dvnTGAfYJUl5ZoIzQ01GmZj4+P9txisdjFYUuGqyZJjtRV3hiNibm+IiIitOcNSXibI6aQkBCn\nZbYpbepTp652LBYLJpMJsN9eIYQ4l0jCKoQQwo6fnx9Dhgxh/fr1bNy4EajsLTSZTAQFBXH++efb\n1R88eDB+fn7k5uayf/9+zjvvPLZs2QJUJrO2SxptduzYwaRJk7BYLBgMBi655BL69u1LVFQU/v7+\n2uXGDz74IEaj0a43sqoePXowZMgQtm/fztKlS7WE1WKx8OWXXwKVc3P6+/vbLVd1PtjLL7/c6dys\n1TVkhFZXtFEfVfd9RUVFrXXrKvc0VRPs3NxcN0bScs6cOaM9b4kfFIQQ4mwgCasQQogaxowZw/r1\n68nKyiIxMVHrMR02bBheXl52df39/bngggvYvHkz69evp6SkROspHDNmTI11P/vss1gsFtq3b8/W\nrVudXjZ5zz331BnnvHnz2L59O5s3b+b48eN06dKFf//739qJ/vXXX19jmZCQEAICAjCZTERFRTF/\n/vw622koV7RR3zj8/PwoKysjPT291rqnTp1yUVTNIyIiguDgYAoLCzl9+rS7w2kRVberY8eOboxE\nCCHcRwZdEkIIUUP1+1ht96RWvxzYxnZZcNW61ddjY7u3c+7cuU6T1X379mmDNtVm9uzZ+Pr6opTS\nBlmyXQ6ckJDA8OHDHS5nGyhqzZo1dvfNNidXtFEXnU7HwIEDAbTecmeqvm/VVe8l9wQ6nU57f3fu\n3OnmaFrGjh07tOe2z5gQQpxrPO8vkBBCCLe76KKL8PX1BSrvN928eTNQd8K6fv16rTc2ODhYS5aq\nst1TWdsgMs8991y94oyIiGDatGlA5ajAZ86c4YcffgDguuuuc7rcjTfeCEBaWhqvvfZavdpqKFe0\nUR8zZ84EKpO6TZs2Oaxz6NAhbTRhR6qOqms0Gps3wCaw/SCya9cut/0o0JJs94137dqV2NhYN0cj\nhBDuIQmrEEKIGgwGgzb1xqpVqygsLMTf399uOo6qhg8fjl6vJysri59++gmonP6m+uXDgHYP7LJl\ny+x6kABMJhP3338/v/zyi9OpR6qzjQJ87Ngx7r33XsxmMzqdrtaEdfbs2VqSvXDhQp577jmnPboH\nDhzgpZdeanBC5Io26mP+/Pm0bdsWqJyOZc+ePXblx44d4/LLL6d9+/ZO1+Hr60uHDh2Ayh8wPIXt\n3mCTycSuXbvcHE3zs/WK1/ceaCGEaI0kYRVCCOGQrffKNoqpbXAlR0JDQ+nXr59dfWfT2Tz++ON4\ne3tTWFjIRRddxPDhw7nuuuuYPn06cXFxvPHGGyxZsqTeU5VMnjxZG5Dm008/BSqT5S5dujhdRq/X\ns2LFCi644AIqKip4/PHHiYmJYdKkSVx//fXMnDmTYcOG0b59e/r06cPDDz/c4GTSFW3UR3h4OB99\n9BE+Pj6cPHmSQYMGMXr0aObNm8eECRPo1asXJSUlPP3003axV3fFFVcAaNt0ww03MH/+fObPn6/1\nBLpav379GDJkCPC/97612LFjB0lJSQDceuutbo5GCCHcRxJWIYQQDlUfMMnZ5cA21e+xczTgElQm\nk6tWrSIuLg6lFFu2bOGTTz7hu+++IzIykjVr1nDZZZfVO05vb2/mzJlj95qjwZaqi4qKYtOmTSxa\ntIiYmBgKCgr46aefWLp0KatWrWLr1q2kp6fTt29fHn74YbspT+rLFW3Ux6WXXsratWvp2bMnFRUV\nbNiwgWXLlvHLL78wfPhwNm/ejMFg0Oo76t3+29/+xvTp09HpdOzatYuPP/6YJUuWsGTJEi2xcocF\nCxYA8PnnnzdqehxPZbsne+TIkfTu3dvN0QghhPvolLP5AoQQQpzTSkpK7Hqtxo0bV2uv5aFDh+zu\nkbzhhhvs5pusrry8nB07dnD8+HF8fX3p0qULgwYN0qaE+eyzzzCZTAwcOJBBgwbVGmtqaio///yz\n9v+rr76a4ODgOrfRRinFwYMHOXLkCEajkfDwcKKjo+ncubN2OW11FRUVfPjhh0BlUtGjR49mb6Oo\nqIgvvvgCgAkTJjgdKTY3N5eVK1cCMG3aNKKjo53GsWvXLo4cOYKvry+9e/emZ8+eALz66qvcf//9\n+Pn5UVJSYjc1T1UnT55k9+7d5Obmagni2LFjSUhIaLGYa1NaWkpCQgLp6el89dVXXHnllTXqNEdM\n9V3Htm3b2L9/P8HBwVx99dUO6yilWLJkCVA5OFevXr3syk0mEx07diQnJ4dVq1YxY8aMWvaAEEK0\nbpKwCiGEEIIrrrhCu9zXNpLz2eKjjz7ixhtvpE+fPuzdu9dpsn22ePHFF1m4cCGjRo3SBjETQohz\nlVwSLIQQQpzjjhw5wurVq4Gzc4CfefPmMXDgQPbv389XX33l7nCapKioiH/+85/odDpeeeUVd4cj\nhBBu5/xaLSGEEEK0CgcOHCAjI4Nx48bVGFDp999/5+qrr8ZsNhMYGMgtt9zipigbT6/X88knn7Bl\nyxYiIiLcHU6T5OTksGjRIqKiorQRtYUQ4lwmlwQLIYQQrdw333zD5ZdfTmRkJD179iQuLg69Xk9i\nYiJ79uxBKYVer+ejjz6qdTogIYQQwtUkYRVCCCFauaSkJO68807+85//OJw6Z+DAgbz44otcfPHF\nbohOCCGEcE4SViGEEOIcUVBQwKFDh8jIyNBGKu7Xr5/TEW+FEEIId5OEVQghhBBCCCGER5JRgoUQ\nQgghhBBCeCRJWIUQQgghhBBCeCRJWIUQQgghhBBCeCRJWIUQQgghhBBCeCRJWIUQQgghhBBCeCRJ\nWIUQQgghhBBCeCRJWIUQQgghhBBCeCRJWIUQQgghhBBCeCRJWIUQQgghhBBCeCRJWIUQQgghhBBC\neCRJWIUQQgghhBBCeCRJWIUQQgghhBBCeCRJWIUQQgghhBBCeCRJWIUQQgghhBBCeCRJWIUQQggh\nhBBCeCRJWIUQQgghhBBCeCRJWIUQQgghhBBCeCRJWIUQQgghhBBCeKT/B68BQKemW7kQAAAAAElF\nTkSuQmCC\n"
          }
        }
      ],
      "source": [
        "fig, ax = plt.subplots(1, 1, figsize=(5, 3), tight_layout=True)\n",
        "\n",
        "ax.plot(wl_range, sim_data_no_sym[\"R\"].flux, \"-k\", label=\"symmetry=(0, 0, 0)\")\n",
        "ax.plot(wl_range, sim_data_sym[\"R\"].flux, \"--r\", label=\"symmetry=(-1, 1, 0)\")\n",
        "ax.set_xlabel(\"Wavelength (nm)\")\n",
        "ax.set_ylabel(\"Reflectance\")\n",
        "ax.set_ylim(0, 1)\n",
        "ax.set_xlim(wl_range[0], wl_range[-1])\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "id": "6b862eff"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Symmetry Example 2: Straight Dielectric Waveguide\n",
        "\n",
        "Let’s look at a simple dielectric waveguide and observe how to use the\n",
        "symmetry parameters to reduce computation and select between the\n",
        "transverse electric (TE) and transverse magnetic (TM) propagating modes\n",
        "with specific symmetries."
      ],
      "id": "d11b03f4-020c-4e12-9b15-c1f0fb5a7f81"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Simulation parameters.\n",
        "wl = 1  # Wavelength (um).\n",
        "bw = 0.1  # Simulation bandwidth (um).\n",
        "\n",
        "# Geometry.\n",
        "wg_length = 10\n",
        "wg_height = 0.5\n",
        "wg_width = 2\n",
        "\n",
        "# Material.\n",
        "wg_n = 2\n",
        "\n",
        "# Simulation size.\n",
        "size_x = wg_length\n",
        "size_y = wg_width + 2 * wl\n",
        "size_z = wg_height + 2 * wl\n",
        "\n",
        "# Source and Monitor frequencies.\n",
        "wl_range = np.linspace(wl - bw / 2, wl + bw / 2, 101)\n",
        "freq = td.C_0 / wl\n",
        "freq_range = td.C_0 / wl_range\n",
        "freqw = 0.5 * (freq_range[0] - freq_range[-1])\n",
        "run_time = 1e-12"
      ],
      "id": "1e68aaf4"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We will create the waveguide geometry first."
      ],
      "id": "5cefce0c-f80e-4005-9969-04a411a255bc"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "wg_medium = td.Medium(permittivity=wg_n**2)\n",
        "wg_structure = td.Structure(\n",
        "    geometry=td.Box(center=(0, 0, 0), size=(2 * wg_length, wg_width, wg_height)),\n",
        "    medium=wg_medium,\n",
        ")"
      ],
      "id": "ec244c88"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Then, we will create a function that receives a tuple containing the\n",
        "symmetry specification, build and run a\n",
        "[ModeSolver](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.mode.ModeSolver.html#tidy3d.plugins.mode.ModeSolver),\n",
        "and return a `ModeData` and the `Simulation` objects. As we are only\n",
        "interested in the mode field distribution, we will run the `ModeSolve`\n",
        "locally. However, when you need more accurate results, you can run it on\n",
        "the server side to take advantage of `subpixel averaging`."
      ],
      "id": "fd5c9f54-f2d6-4295-937d-5d7bf2c6408b"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "def build_simulation(symm=(0, 0, 0), n_modes=4):\n",
        "    # Simulation object.\n",
        "    sim = td.Simulation(\n",
        "        size=(size_x, size_y, size_z),\n",
        "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=20, wavelength=wl),\n",
        "        boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
        "        run_time=run_time,\n",
        "        structures=[wg_structure],\n",
        "        symmetry=symm,\n",
        "    )\n",
        "\n",
        "    # Mode Solver object.\n",
        "    plane = td.Box(center=(-size_x / 2 + 0.5 * wl, 0, 0), size=(0, size_y, size_z))\n",
        "\n",
        "    mode_spec = td.ModeSpec(\n",
        "        num_modes=n_modes,\n",
        "        target_neff=wg_n,\n",
        "    )\n",
        "\n",
        "    mode_solver = ModeSolver(\n",
        "        simulation=sim,\n",
        "        plane=plane,\n",
        "        mode_spec=mode_spec,\n",
        "        freqs=[freq],\n",
        "    )\n",
        "\n",
        "    mode_data = mode_solver.solve()\n",
        "\n",
        "    # Include a mode source in the simulation.\n",
        "    source_time = td.GaussianPulse(freq0=freq, fwidth=freqw)\n",
        "    mode_src = mode_solver.to_source(mode_index=0, source_time=source_time, direction=\"+\")\n",
        "    sim = sim.copy(update={\"sources\": [mode_src]})\n",
        "\n",
        "    return mode_data, sim"
      ],
      "id": "b208cb4f"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "In addition, we will define a function to plot the mode field\n",
        "distribution and draw the symmetry axis in the y- and z-directions. Red\n",
        "refers to **-1** (*odd, i.e. ‘PEC’ symmetry*) and blue to **1** (*even,\n",
        "i.e. ‘PMC’ symmetry*)."
      ],
      "id": "6872b4be-a8b7-41c3-8945-b0ff6006e7e2"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [],
      "source": [
        "def plot_modes(mode_data, mode_index=0, label=\"TE\", axis_y=\"red\", axis_z=\"blue\"):\n",
        "    fig, ax = plt.subplots(1, 4, tight_layout=True, figsize=(12, 5))\n",
        "    sx = np.asarray(\n",
        "        mode_data.Ey.isel(mode_index=mode_index, f=0, drop=True).squeeze().real.T.y\n",
        "    ).size\n",
        "    sy = np.asarray(\n",
        "        mode_data.Ey.isel(mode_index=mode_index, f=0, drop=True).squeeze().real.T.z\n",
        "    ).size\n",
        "\n",
        "    ax[0].imshow(\n",
        "        mode_data.Ey.isel(mode_index=mode_index, f=0, drop=True).squeeze().real.T,\n",
        "        cmap=\"RdBu\",\n",
        "    )\n",
        "    ax[0].axvline(x=sx / 2, color=axis_y)\n",
        "    ax[0].set_title(label + \": Ey\")\n",
        "    ax[0].axis(\"off\")\n",
        "    ax[1].imshow(\n",
        "        mode_data.Ez.isel(mode_index=mode_index, f=0, drop=True).squeeze().real.T,\n",
        "        cmap=\"RdBu\",\n",
        "    )\n",
        "    ax[1].axvline(x=sx / 2, color=axis_y)\n",
        "    ax[1].set_title(label + \": Ez\")\n",
        "    ax[1].axis(\"off\")\n",
        "    ax[2].imshow(\n",
        "        mode_data.Hy.isel(mode_index=mode_index, f=0, drop=True).squeeze().real.T,\n",
        "        cmap=\"RdBu\",\n",
        "    )\n",
        "    ax[2].axhline(y=sy / 2, color=axis_z)\n",
        "    ax[2].set_title(label + \": Hy\")\n",
        "    ax[2].axis(\"off\")\n",
        "    ax[3].imshow(\n",
        "        mode_data.Hz.isel(mode_index=mode_index, f=0, drop=True).squeeze().real.T,\n",
        "        cmap=\"RdBu\",\n",
        "    )\n",
        "    ax[3].axhline(y=sy / 2, color=axis_z)\n",
        "    ax[3].set_title(label + \": Hz\")\n",
        "    ax[3].axis(\"off\")\n",
        "    plt.show()"
      ],
      "id": "bab80eca"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Symmetry (0, 0, 0)\n",
        "\n",
        "We will create a simulation using no symmetries and take a look at the\n",
        "generated 3D model. We can observe that the structure itself has mirror\n",
        "symmetries about the `x`-, `y`-, and `z`-axis. However, the source plane\n",
        "position breaks the symmetry about the `x`-axis, and the simulation ends\n",
        "up with symmetries only about `y` and `z`.It is worth mentioning that if\n",
        "the waveguide sidewall angle is other than zero, or the top cladding and\n",
        "the substrate are not the same material, we would have symmetry about\n",
        "the `y`-axis only."
      ],
      "id": "3dd6134e-adf1-41cd-944a-fee1ca8d30b0"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "\n",
              "    <div class=\"simulation-viewer\" data-width=\"400\" data-height=\"400\" data-simulation=\"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\" ></div>\n",
              "    <script>\n",
              "        \n",
              "        /**\n",
              "        * Simulation Viewer Injector\n",
              "        *\n",
              "        * Monitors the document for elements being added in the form:\n",
              "        *\n",
              "        *    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"{...}\" />\n",
              "        *\n",
              "        * This script will then inject an iframe to the viewer application, and pass it the simulation data\n",
              "        * via the postMessage API on request. The script may be safely included multiple times, with only the\n",
              "        * configuration of the first started script (e.g. viewer URL) applying.\n",
              "        *\n",
              "        */\n",
              "        (function() {\n",
              "            const TARGET_CLASS = \"simulation-viewer\";\n",
              "            const ACTIVE_CLASS = \"simulation-viewer-active\";\n",
              "            const VIEWER_URL = \"https://tidy3d.simulation.cloud/simulation-viewer\";\n",
              "\n",
              "            class SimulationViewerInjector {\n",
              "                constructor() {\n",
              "                    for (var node of document.getElementsByClassName(TARGET_CLASS)) {\n",
              "                        this.injectViewer(node);\n",
              "                    }\n",
              "\n",
              "                    // Monitor for newly added nodes to the DOM\n",
              "                    this.observer = new MutationObserver(this.onMutations.bind(this));\n",
              "                    this.observer.observe(document.body, {childList: true, subtree: true});\n",
              "                }\n",
              "\n",
              "                onMutations(mutations) {\n",
              "                    for (var mutation of mutations) {\n",
              "                        if (mutation.type === 'childList') {\n",
              "                            /**\n",
              "                            * Have found that adding the element does not reliably trigger the mutation observer.\n",
              "                            * It may be the case that setting content with innerHTML does not trigger.\n",
              "                            *\n",
              "                            * It seems to be sufficient to re-scan the document for un-activated viewers\n",
              "                            * whenever an event occurs, as Jupyter triggers multiple events on cell evaluation.\n",
              "                            */\n",
              "                            var viewers = document.getElementsByClassName(TARGET_CLASS);\n",
              "                            for (var node of viewers) {\n",
              "                                this.injectViewer(node);\n",
              "                            }\n",
              "                        }\n",
              "                    }\n",
              "                }\n",
              "\n",
              "                injectViewer(node) {\n",
              "                    // (re-)check that this is a valid simulation container and has not already been injected\n",
              "                    if (node.classList.contains(TARGET_CLASS) && !node.classList.contains(ACTIVE_CLASS)) {\n",
              "                        // Mark node as injected, to prevent re-runs\n",
              "                        node.classList.add(ACTIVE_CLASS);\n",
              "\n",
              "                        var uuid;\n",
              "                        if (window.crypto && window.crypto.randomUUID) {\n",
              "                            uuid = window.crypto.randomUUID();\n",
              "                        } else {\n",
              "                            uuid = \"\" + Math.random();\n",
              "                        }\n",
              "\n",
              "                        var frame = document.createElement(\"iframe\");\n",
              "                        frame.width = node.dataset.width || 800;\n",
              "                        frame.height = node.dataset.height || 800;\n",
              "                        frame.style.cssText = `width:${frame.width}px;height:${frame.height}px;max-width:none;border:0;display:block`\n",
              "                        frame.src = VIEWER_URL + \"?uuid=\" + uuid;\n",
              "\n",
              "                        var postMessageToViewer;\n",
              "                        postMessageToViewer = event => {\n",
              "                            if(event.data.type === 'viewer' && event.data.uuid===uuid){\n",
              "                                frame.contentWindow.postMessage({ type: 'jupyter', uuid, value: node.dataset.simulation, fileType: 'hdf5'}, '*');\n",
              "\n",
              "                                // Run once only\n",
              "                                window.removeEventListener('message', postMessageToViewer);\n",
              "                            }\n",
              "                        };\n",
              "                        window.addEventListener(\n",
              "                            'message',\n",
              "                            postMessageToViewer,\n",
              "                            false\n",
              "                        );\n",
              "\n",
              "                        node.appendChild(frame);\n",
              "                    }\n",
              "                }\n",
              "            }\n",
              "\n",
              "            if (!window.simulationViewerInjector) {\n",
              "                window.simulationViewerInjector = new SimulationViewerInjector();\n",
              "            }\n",
              "        })();\n",
              "    \n",
              "    </script>\n",
              "    "
            ]
          }
        }
      ],
      "source": [
        "mode_data, sim = build_simulation(symm=(0, 0, 0), n_modes=4)\n",
        "sim.plot_3d(width=400, height=400)"
      ],
      "id": "3e72dcc9"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Before diving into the mode symmetries, let’s run an FDTD simulation to\n",
        "look at the simulation details, like the number of grid points and\n",
        "simulation time when no symmetry is employed. The mode source will\n",
        "inject the mode with index 0. We will set `verbose = False` to reduce\n",
        "the output information."
      ],
      "id": "e77fbd96-4a3a-48d1-aae7-d7b18c697334"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "monitor = td.FieldMonitor(center=(0, 0, 0), size=(td.inf, td.inf, 0), freqs=[freq], name=\"field\")\n",
        "sim = sim.copy(update={\"monitors\": [monitor]})\n",
        "job = web.Job(simulation=sim, task_name=\"mode_sim_no_symmetry\", verbose=False)\n",
        "sim_data = job.run(path=\"data/simulation_data.hdf5\")"
      ],
      "id": "4ba1ebd5"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The simulation has about 5.5 million grid points and took just a few\n",
        "seconds."
      ],
      "id": "1e4e2862-c74b-4a5f-928c-012e73a3d873"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[07:29:31] USER: Simulation domain Nx, Ny, Nz: [424, 146, 86]                   \n",
            "           USER: Applied symmetries: (0, 0, 0)                                  \n",
            "           USER: Number of computational grid points: 5.5482e+06.               \n",
            "           USER: Subpixel averaging method: SubpixelSpec()                      \n",
            "           USER: Number of time steps: 2.0982e+04                               \n",
            "           USER: Automatic shutoff factor: 1.00e-05                             \n",
            "           USER: Time step (s): 4.7664e-17                                      \n",
            "           USER:                                                                \n",
            "                                                                                \n",
            "[07:29:32] USER: Compute source modes time (s):     0.8055                      \n",
            "           USER: Rest of setup time (s):            0.3804                      \n",
            "[07:29:35] USER: Compute monitor modes time (s):    0.0002                      \n",
            "[07:29:37] USER: Solver time (s):                   1.8110                      \n",
            "           USER: Time-stepping speed (cells/s):     1.10e+10                    \n",
            "           USER: Loading data for monitor field                                 \n",
            "           USER: Post-processing time (s):          0.2504                      \n",
            "\n",
            " ====== SOLVER LOG ====== \n",
            "\n",
            "Processing grid and structures...\n",
            "Building FDTD update coefficients...\n",
            "Solver setup time (s):             0.1808\n",
            "\n",
            "Running solver for 20982 time steps...\n",
            "- Time step    839 / time 4.00e-14s (  4 % done), field decay: 1.00e+00\n",
            "- Time step   1678 / time 8.00e-14s (  8 % done), field decay: 1.00e+00\n",
            "- Time step   2517 / time 1.20e-13s ( 12 % done), field decay: 7.82e-01\n",
            "- Time step   3200 / time 1.53e-13s ( 15 % done), field decay: 7.74e-06\n",
            "Field decay smaller than shutoff factor, exiting solver.\n",
            "Time-stepping time (s):            1.6170\n",
            "Data write time (s):               0.0109\n"
          ]
        }
      ],
      "source": [
        "print(sim_data.log)"
      ],
      "id": "eda4b9dd"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Below we can observe the fundamental mode propagating throughout the\n",
        "waveguide."
      ],
      "id": "b720710b-c013-4b1a-ac89-b45f90682810"
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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7rY5hNnz4cNN+7DhxcWTRZoBywH85ZlJMNZuJEyea7fsykN3W1oZZs2bhj3/8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\ncqSTlMrmiSeeML+fcMIJSKVS2HrrrXH55Zf36Fzbbrs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GGwCoPAmWNDqjro2tKKzmuK6u\nrrLnsq9NrpcQQgghhAwMOIFHCCGEkAHD448/jtmzZ5u///rXv2LhwoW9LldUbB0dHVi3bl2vy2tE\nPvjgA/N7LZl6CSGEEEJI/8EJPEIIIYQMCJYvX44TTzwRXV1d2GqrrTBs2DC0t7fjU5/6FLq7u3tV\n9pZbbml+X7x4cW9NBYBe25Q0cl1jxoxBPp/vZ2sIIYQQQkhP4AQeIYQQQgYEp59+OhYsWIBcLodZ\ns2bh5z//OQDgn//8J773ve/1quwDDzzQ/P7kk09W9ZlKE31vv/12r2xKmnnz5gFwr5UQQgghhAwM\nOIFHCCGEkIbnV7/6FWbNmgUAuPjii7HvvvvixBNPxMknnwwAuOSSS/C3v/2t5vI/9KEPmSyvjz/+\neFWfefHFF7F06dLI9x577LHElHxJsHz5crz44osAgIkTJ/azNYQQQgghpKdwAo8QQgghDc0LL7yA\nr33tawCAQw45BN/61rfMe9deey0+9KEPoVgs4qSTTnLivPWUww47DADw0EMPVXV8R0cHZsyYgeXL\nlzuvv/rqq2ZiEQBWrFhRs01J8Y9//AOe5yGVSmHy5Mn9bQ4hhBBCCOkhnMAjhBBCSMOydu1aTJ8+\nHatXr8aoUaMwc+ZMpFIp835bWxtuueUWNDU14e2338Ypp5xS87m+8IUvACip51599dWKx2+22Wa4\n8847sc0222Dy5Mn43Oc+h49//OMYN24cXnnlFey2224AgHvuuQef+tSn8Je//KVm23rLH/7wBwDA\nwQcfjG233bbf7CCEEEIIIbXBCTxCCCGENCxnn302nnnmGaRSKdxwww0YPXp06Jg99tgDP/rRjwAA\nd911F6655pqazjVhwgTsscceAICZM2dWPH6PPfbAr3/9a3R2duK+++7DDTfcgLlz56KtrQ033XQT\nfvnLXyKbzaK7uxs333yz2cLa16xcuRJ33nknAOCLX/xiv9hACCGEEEJ6R7a/DSCEEEIIiWLNmjXY\na6+9cP3112PMmDH4xCc+EXvsV7/6VWy44YZYs2YNmpubzXbRnvKNb3wDn/3sZ/G73/0O559/PrLZ\n8o9KJ598Mo455hg8/PDDeP/99zFq1Ch89KMfRUtLCwDg+eefxxNPPIHtt98e48ePN5+bPn06dtll\nFwwdOrRs+ddffz0AYL/99os95hOf+ARGjBiBdDp6Xfbmm2/G6tWrse222+LII48sez5CCCGEENKY\npDzP8/rbCEIIIYSQRqBYLGKfffbB/Pnz8ctf/hKnnXZa6JjJkyfjvvvuw5QpU3D33Xf3g5XV09XV\nhe233x4LFy7E7bffjmnTpvW3SYQQQgghpAa4hZYQQgghxCedTuPKK68EUMp2u27dun62qHf8+te/\nxsKFCzFx4kRO3hFCCCGEDGC4hZYQQgghxOJjH/sYZs2ahRUrVuCDDz7AJpts0t8m1czYsWNx/fXX\n46CDDupvUwghhBBCSC/gBB4hhBBCiOL444/vbxMSYerUqf1tAiGEEEIISQBuoSWEEEIIIYQQQggh\npIFhEgtCCCGEkB5w33334c0338QWW2yBQw89tL/NIYQQQggh6wGcwCOEEEIIIYQQQgghpIHhFlpC\nCCGEEEIIIYQQQhoYTuARQgghhBBCCCGEENLAcAKPEEIIIYQQQgghhJAGhhN4hBBCCCGEEEIIIYQ0\nMJzAI4QQQgghhBBCCCGkgeEEHiGEEEIIIYQQQgghDQwn8AghhBBCCCGEEEIIaWA4gUcIIYQQQggh\nhBBCSAPDCTxCCCGEEEIIIYQQQhoYTuARQgghhBBCCCGEENLAcAKPEEIIIYQQQgghhJAGhhN4hBBC\nCCGEEEIIIYQ0MJzAI4QQQgghhBBCCCGkgeEEHiGEEEIIIYQQQgghDQwn8AghhBBCCCGEEEIIaWA4\ngUcIIYQQQgghhBBCSAPDCTxCCCGEEEIIIYQQQhoYTuARQgghhBBCCCGEENLA/H/24OUpX9Dw3wAA\nAABJRU5ErkJggg==\n"
          }
        }
      ],
      "source": [
        "fig, ax = plt.subplots(1, 1, figsize=(8, 3))\n",
        "sim_data.plot_field(\"field\", \"Ey\", f=freq, ax=ax)\n",
        "plt.show()"
      ],
      "id": "6fd853ac"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now, let’s look at the 4 modes returned by the `ModeSolver` and consider\n",
        "the vectorial nature of their fields to analyze their symmetries about\n",
        "the `y`- and `z`-directions.\n",
        "\n",
        "- **Mode TE0**: We can observe that the `Ey` field component is\n",
        "  continuous and the `Ez` one changes sign across the `y`-symmetry line,\n",
        "  thus conforming to a PEC-like condition at the `y`-symmetry axis. So,\n",
        "  this mode has symmetry **-1** (*odd, i.e. ‘PEC’ symmetry*) in the\n",
        "  `y`-direction. At the `z`-direction, the `Hy` field component changes\n",
        "  sign, whereas the `Hz` one is continuous at the `z`-symmetry line,\n",
        "  thus resulting in the symmetry value **1** (*even, i.e. ‘PMC’\n",
        "  symmetry*). In summary, this mode has symmetry `(0, -1, 1)`."
      ],
      "id": "c1bcb8d8-3bc2-4f5d-900e-78b690c2178d"
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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          }
        }
      ],
      "source": [
        "plot_modes(mode_data, mode_index=0, label=\"TE0\", axis_y=\"red\", axis_z=\"blue\")"
      ],
      "id": "22ce0cbf"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- **Mode TE1**: The `Ey` field component changes sign, and the `Ez` one\n",
        "  is continuous at the `y`-symmetry line, thus conforming to a PMC-like\n",
        "  condition at the `y`-symmetry axis. So, this mode has symmetry **1**\n",
        "  (*even, i.e. ‘PMC’ symmetry*) in the `y`-direction. At the\n",
        "  `z`-direction, the `Hy` field component changes sign, whereas the `Hz`\n",
        "  one is continuous at the `z`-symmetry line, thus also conforming to a\n",
        "  PMC-like condition. Hence, this mode has symmetry `(0, 1, 1)`."
      ],
      "id": "2664b5c4-196f-47a5-bccb-b4aab526a7d0"
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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          }
        }
      ],
      "source": [
        "plot_modes(mode_data, mode_index=1, label=\"TE1\", axis_y=\"blue\", axis_z=\"blue\")"
      ],
      "id": "660edf07"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- **Mode TM0**: The `Ey` field component changes sign, and the `Ez` one\n",
        "  is continuous at the `y`-symmetry line, thus conforming to a PMC-like\n",
        "  condition at the `y`-symmetry axis. So, this mode has symmetry **1**\n",
        "  (*even, i.e. ‘PMC’ symmetry*) in the `y`-direction. At the\n",
        "  `z`-direction, the `Hy` field component is continuous, whereas the\n",
        "  `Hz` one changes sign at the `z`-symmetry line, thus conforming to a\n",
        "  PEC-like condition. Hence, this mode has symmetry `(0, 1, -1)`."
      ],
      "id": "01309c6d-e174-430b-b0cf-9d7a4b6a0182"
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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          }
        }
      ],
      "source": [
        "plot_modes(mode_data, mode_index=2, label=\"TM0\", axis_y=\"blue\", axis_z=\"red\")"
      ],
      "id": "34491c5a"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- **Mode TM1**: The `Ey` field component is continuous, and the `Ez` one\n",
        "  changes sign at the `y`-symmetry line, thus conforming to a PEC-like\n",
        "  condition at the `y`-symmetry axis. So, this mode has symmetry **-1**\n",
        "  (*odd, i.e. ‘PEC’ symmetry*) in the `y`-direction. At the\n",
        "  `z`-direction, the `Hy` field component is continuous, whereas the\n",
        "  `Hz` one changes sign at the `z`-symmetry line, thus also conforming\n",
        "  to a PEC-like condition. Hence, this mode has symmetry `(0, -1, -1)`."
      ],
      "id": "8f73bd26-7995-4a8d-ba78-c048cdef931f"
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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          }
        }
      ],
      "source": [
        "plot_modes(mode_data, mode_index=3, label=\"TM1\", axis_y=\"red\", axis_z=\"red\")"
      ],
      "id": "296cf736"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "In summary, these waveguide modes have the following symmetries: - TE0:\n",
        "`(0, -1, 1)` - TE1: `(0, 1, 1)` - TM0: `(0, 1, -1)` - TM1: `(0, -1, -1)`\n",
        "\n",
        "Now, let’s see the modes returned by the solver when we set these\n",
        "symmetry conditions.\n",
        "\n",
        "### Symmetry (0, -1, 1)\n",
        "\n",
        "If you turn-on the “Symmetry” visualization option after plotting the\n",
        "simulation, you will see the simulation domain reduced to 1/4, the\n",
        "**-1** (*odd, i.e. ‘PEC’ symmetry*) domain region in red, and the **1**\n",
        "(*even, i.e. ‘PMC’ symmetry*) domain region in blue."
      ],
      "id": "35c2da45-f270-4704-aa5e-654023ba4394"
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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              "    <script>\n",
              "        \n",
              "        /**\n",
              "        * Simulation Viewer Injector\n",
              "        *\n",
              "        * Monitors the document for elements being added in the form:\n",
              "        *\n",
              "        *    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"{...}\" />\n",
              "        *\n",
              "        * This script will then inject an iframe to the viewer application, and pass it the simulation data\n",
              "        * via the postMessage API on request. The script may be safely included multiple times, with only the\n",
              "        * configuration of the first started script (e.g. viewer URL) applying.\n",
              "        *\n",
              "        */\n",
              "        (function() {\n",
              "            const TARGET_CLASS = \"simulation-viewer\";\n",
              "            const ACTIVE_CLASS = \"simulation-viewer-active\";\n",
              "            const VIEWER_URL = \"https://tidy3d.simulation.cloud/simulation-viewer\";\n",
              "\n",
              "            class SimulationViewerInjector {\n",
              "                constructor() {\n",
              "                    for (var node of document.getElementsByClassName(TARGET_CLASS)) {\n",
              "                        this.injectViewer(node);\n",
              "                    }\n",
              "\n",
              "                    // Monitor for newly added nodes to the DOM\n",
              "                    this.observer = new MutationObserver(this.onMutations.bind(this));\n",
              "                    this.observer.observe(document.body, {childList: true, subtree: true});\n",
              "                }\n",
              "\n",
              "                onMutations(mutations) {\n",
              "                    for (var mutation of mutations) {\n",
              "                        if (mutation.type === 'childList') {\n",
              "                            /**\n",
              "                            * Have found that adding the element does not reliably trigger the mutation observer.\n",
              "                            * It may be the case that setting content with innerHTML does not trigger.\n",
              "                            *\n",
              "                            * It seems to be sufficient to re-scan the document for un-activated viewers\n",
              "                            * whenever an event occurs, as Jupyter triggers multiple events on cell evaluation.\n",
              "                            */\n",
              "                            var viewers = document.getElementsByClassName(TARGET_CLASS);\n",
              "                            for (var node of viewers) {\n",
              "                                this.injectViewer(node);\n",
              "                            }\n",
              "                        }\n",
              "                    }\n",
              "                }\n",
              "\n",
              "                injectViewer(node) {\n",
              "                    // (re-)check that this is a valid simulation container and has not already been injected\n",
              "                    if (node.classList.contains(TARGET_CLASS) && !node.classList.contains(ACTIVE_CLASS)) {\n",
              "                        // Mark node as injected, to prevent re-runs\n",
              "                        node.classList.add(ACTIVE_CLASS);\n",
              "\n",
              "                        var uuid;\n",
              "                        if (window.crypto && window.crypto.randomUUID) {\n",
              "                            uuid = window.crypto.randomUUID();\n",
              "                        } else {\n",
              "                            uuid = \"\" + Math.random();\n",
              "                        }\n",
              "\n",
              "                        var frame = document.createElement(\"iframe\");\n",
              "                        frame.width = node.dataset.width || 800;\n",
              "                        frame.height = node.dataset.height || 800;\n",
              "                        frame.style.cssText = `width:${frame.width}px;height:${frame.height}px;max-width:none;border:0;display:block`\n",
              "                        frame.src = VIEWER_URL + \"?uuid=\" + uuid;\n",
              "\n",
              "                        var postMessageToViewer;\n",
              "                        postMessageToViewer = event => {\n",
              "                            if(event.data.type === 'viewer' && event.data.uuid===uuid){\n",
              "                                frame.contentWindow.postMessage({ type: 'jupyter', uuid, value: node.dataset.simulation, fileType: 'hdf5'}, '*');\n",
              "\n",
              "                                // Run once only\n",
              "                                window.removeEventListener('message', postMessageToViewer);\n",
              "                            }\n",
              "                        };\n",
              "                        window.addEventListener(\n",
              "                            'message',\n",
              "                            postMessageToViewer,\n",
              "                            false\n",
              "                        );\n",
              "\n",
              "                        node.appendChild(frame);\n",
              "                    }\n",
              "                }\n",
              "            }\n",
              "\n",
              "            if (!window.simulationViewerInjector) {\n",
              "                window.simulationViewerInjector = new SimulationViewerInjector();\n",
              "            }\n",
              "        })();\n",
              "    \n",
              "    </script>\n",
              "    "
            ]
          }
        }
      ],
      "source": [
        "mode_data, sim = build_simulation(symm=(0, -1, 1), n_modes=4)\n",
        "sim.plot_3d(width=400, height=400)"
      ],
      "id": "50ebb2aa"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "After plotting the mode field distributions, we can observe that all the\n",
        "modes are TE and have the same symmetry as the TE0 one, i. e., the mode\n",
        "profiles have an odd number of lobes, and all of them are even-symmetric\n",
        "across the y-axis. That is important information when designing devices\n",
        "based on modal interference."
      ],
      "id": "acd92445-1a1d-427f-828e-df6901b767f2"
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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          }
        }
      ],
      "source": [
        "for m, lab in zip(range(4), [\"TE0\", \"TE2\", \"TE4\", \"TE6\"]):\n",
        "    plot_modes(mode_data, mode_index=m, label=lab, axis_y=\"red\", axis_z=\"blue\")"
      ],
      "id": "c77e35ae"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Symmetry (0, 1, 1)\n",
        "\n",
        "In this case, all the modes share the TE1 symmetry, so they have an even\n",
        "number of lobes and the field distributions are odd-symmetric about the\n",
        "y-axis."
      ],
      "id": "1fae308d-7bdf-4dd8-a588-1bada979fbf1"
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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        {
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          }
        }
      ],
      "source": [
        "mode_data, sim = build_simulation(symm=(0, 1, 1), n_modes=4)\n",
        "for m, lab in zip(range(4), [\"TE1\", \"TE3\", \"TE5\", \"TE7\"]):\n",
        "    plot_modes(mode_data, mode_index=m, label=lab, axis_y=\"blue\", axis_z=\"blue\")"
      ],
      "id": "83fa8996"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Symmetry (0, 1, -1)\n",
        "\n",
        "In this case, all the 4 modes are TM and have the same symmetry of the\n",
        "TM0 one."
      ],
      "id": "d74d973b-513f-4669-9ee3-99c2c3cebb3a"
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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        {
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          "metadata": {},
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          }
        }
      ],
      "source": [
        "mode_data, sim = build_simulation(symm=(0, 1, -1), n_modes=4)\n",
        "for m, lab in zip(range(4), [\"TM0\", \"TM2\", \"TM4\", \"TM6\"]):\n",
        "    plot_modes(mode_data, mode_index=m, label=lab, axis_y=\"blue\", axis_z=\"red\")"
      ],
      "id": "a3f17fe8"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Symmetry (0, -1, -1)\n",
        "\n",
        "All the 4 modes share the same symmetry of the TM1 one."
      ],
      "id": "871e9409-34dd-477a-b695-f5718f035c2a"
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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          "metadata": {},
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          }
        }
      ],
      "source": [
        "mode_data, sim = build_simulation(symm=(0, -1, -1), n_modes=4)\n",
        "for m, lab in zip(range(4), [\"TM1\", \"TM3\", \"TM5\", \"TM7\"]):\n",
        "    plot_modes(mode_data, mode_index=m, label=lab, axis_y=\"red\", axis_z=\"red\")"
      ],
      "id": "4ab082ce"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To wrap up, we will propagate the TM1 mode though the waveguide using\n",
        "FDTD."
      ],
      "id": "d4d103ef-67a1-415b-8f26-cf52dc9a814b"
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [],
      "source": [
        "monitor = td.FieldMonitor(center=(0, 0, 0), size=(td.inf, td.inf, 0), freqs=[freq], name=\"field\")\n",
        "sim = sim.copy(update={\"monitors\": [monitor]})\n",
        "job = web.Job(simulation=sim, task_name=\"mode_sim_tm1_symmetry\", verbose=False)\n",
        "sim_data = job.run(path=\"data/simulation_data.hdf5\")"
      ],
      "id": "20d27bf6"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The number of grid points was reduced to 1/4, as expected due to the\n",
        "4-fold symmetry."
      ],
      "id": "53174640-fa48-41c0-a1f4-44eed3a6c505"
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[07:30:12] USER: Simulation domain Nx, Ny, Nz: [424, 146, 86]                   \n",
            "           USER: Applied symmetries: (0, -1, -1)                                \n",
            "           USER: Number of computational grid points: 1.4378e+06.               \n",
            "           USER: Subpixel averaging method: SubpixelSpec()                      \n",
            "           USER: Number of time steps: 2.0982e+04                               \n",
            "           USER: Automatic shutoff factor: 1.00e-05                             \n",
            "           USER: Time step (s): 4.7664e-17                                      \n",
            "           USER:                                                                \n",
            "                                                                                \n",
            "[07:30:13] USER: Compute source modes time (s):     0.7915                      \n",
            "           USER: Rest of setup time (s):            0.3049                      \n",
            "[07:30:14] USER: Compute monitor modes time (s):    0.0002                      \n",
            "[07:30:16] USER: Solver time (s):                   1.6210                      \n",
            "           USER: Time-stepping speed (cells/s):     3.69e+09                    \n",
            "           USER: Loading data for monitor field                                 \n",
            "           USER: Post-processing time (s):          0.2721                      \n",
            "\n",
            " ====== SOLVER LOG ====== \n",
            "\n",
            "Processing grid and structures...\n",
            "Building FDTD update coefficients...\n",
            "Solver setup time (s):             0.2126\n",
            "\n",
            "Running solver for 20982 time steps...\n",
            "- Time step    839 / time 4.00e-14s (  4 % done), field decay: 1.00e+00\n",
            "- Time step   1678 / time 8.00e-14s (  8 % done), field decay: 1.00e+00\n",
            "- Time step   2517 / time 1.20e-13s ( 12 % done), field decay: 9.32e-01\n",
            "- Time step   3357 / time 1.60e-13s ( 16 % done), field decay: 1.82e-04\n",
            "- Time step   3600 / time 1.72e-13s ( 17 % done), field decay: 3.85e-07\n",
            "Field decay smaller than shutoff factor, exiting solver.\n",
            "Time-stepping time (s):            1.4016\n",
            "Data write time (s):               0.0054\n"
          ]
        }
      ],
      "source": [
        "print(sim_data.log)"
      ],
      "id": "35ecc0fe"
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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uuw8rV67EwMCAPt3WjmHW1tam248ZJy6KRrQZwAv4L8+Mi6lmsvHGG+vrxzOR\nXSgUcOONN+LWW2/F1VdfjUcffRQDAwOYPXs2ttlmGyxevBgHH3wwMpkMenp69DPb2tpi0xxtedfD\nggULxhSnbCrEC8vn8/jxj3+M4447DldccQX++te/4tVXX0WhUMC8efNw4IEH4ogjjkB3d3diWvPm\nzUNfXx8AL1bkRD23GenVwxZbbIH9998fd955J/70pz9h1apVdU0kZzIZXHfddbj88stxzTXX4Pnn\nn0d3dzf23ntvfOlLX8Kmm24KwDt0Y8mSJSgUCjj66KMDE2Sj6ZOZTEZfO3fu3MT8bbbZZvowkFqL\nL3fccYc+FOQTn/hEYrqEEELIdMJx3VFEuCWEEEIIIYRMWW677TatXv3+97+Pr3zlK5HXHXPMMbji\niisAAPfdd19dyt2pziGHHILf//73KBQKeOGFF+pWwRJCCCHTgclfZiWEEEIIIYQ0hAMOOACLFi0C\nAFx44YVayTnTWblyJW677TYA3knXnLwjhBAy0+AEHiGEEEIIITMEx3Fw5plnAgD++c9/4oYbbpjk\nHE0M//u//4tyuYyWlhZ8/etfn+zsEEIIIQ2HE3iEEEIIIYTMIA455BDss88+AIDvfOc7mOkRc155\n5RVcfPHFAICvfOUrdcXVI4QQQqYbnMAjhBBCCCFkhrFkyRIsWLAA69atw+233z7Z2WkqF110EebM\nmYOdd94Zp5566mRnhxBCCGkKPMSCEEIIIYSQDYyZeIgFIYQQMpPJTHYGCCGEEEIIIRPLnDlzMH/+\nfABAoVCY5NwQQgghJAkq8AghhBBCCCGEEEIImcIwBh4hhBBCCCGEEEIIIVMYTuARQgghhBBCCCGE\nEDKF4QQeIYQQQgghhBBCCCFTGE7gEUIIIYQQQgghhBAyheEEHiGEEEIIIYQQQgghUxhO4BFCCCGE\nEEIIIYQ0mbVr1052Fsg0xnFd153sTJDpS7lcxgsvvAAA6OzsRCrFOWFCCCGEEEII2RCpVqvo6+sD\nAGy55ZbIZDKTnKOpQ6VSwR577IGrrroKW2211WRnh0xD2JvIuHjhhRc4+BBCCCGEEEIICfDvf/8b\nCxcunOxsTBl+/etf44EHHsBpp52Gq6++erKzQ6YhlEsRQgghhBBCCCGENImhoSF84xvfAABcc801\neOCBByY5R2Q6QgUeGRednZ3658efeALd3d2TlxlCCCGEEEIIIZPGunXrsP1b3gIg+L/ihs6PfvQj\nvPjii/r3k046Cffeey8cx5nEXJHpBmPgkXGxdu1azJo1CwDw0ssvo6enZ5JzRAghhBBCCCFkMli7\ndi0232wzAMCaNWv4/yGAN954AxtvujlQLSG10VtQXfUkABc33ngjDjnkkMnOHplGcAstIYQQQggh\nhBBCSBP4zne+A1RLQL4Tqc3fjtRG2wEATj75ZJTL5UnOHZlOcAKPEEIIIYQQQgghpME8++yzWLLk\nxwCA9Nxd4TgppDZ5G5DK4qmnnsLFF188uRkk0wpO4BFCCCGEEEIIIYQ0mNNOOw2AC6dtYzhd8wAA\nTrYFqY13AAB87ov/D+vXr5/EHJLpBCfwCCGEEEII2YBxXLfhXzOdZpTZhlamk1GGM61cWU5Tm6VL\nl+I3v/kNACC12dsDB1akNt4eyLYC5WF873vfm6wskmkGJ/AIIYQQQgghhBBCGoTrujjppJMAAE7X\nAqTaNg587qQySG+6EwDgO2edg5dffnnC80imH5zAI4QQQgghhBBCCGkQN9xwA+6//37ASSG92S6R\n1ziztgYKPUC1jNNPP32Cc0imI5zAI4QQQgghhBBCCGkApVIJp5xyCgAgNXs7OPnOyOscJ4X0ZrsC\nAC6++Jd4/PHHJyyPZHrCCTxCCCGEEEIIIYSQBvCLX/wCzz77LJDKIrXp22pe63RsDqd9MwAuvva1\nr01I/sj0xXFdRqQkY2ft2rWYNWsWAOCll19GT0/PJOeIELIhMBHBlF0j0PB0Z7KDT0/HspzsMhOm\nU9lNlTITWHaNg2XZOFiWzWG6lOt0KNPxluXatWux+WabAQDWrFmzwf1/2NfXh67ZmwDlYaTm7oL0\nJjsm3uMOrkb5mZsAAH/4wx/wnve8p9nZJNMUKvAIIYQQQgghhBBCxsl5550HlIeBbBtSc95S1z1O\n62w4PW8CAHz1q19FtVptZhbJNIYTeIQQQgghhBBCCCHj4MUXX8TZ55wHAEjP3RlOKlP3vem5OwNO\nGn/7299w1VVXNSuLZJrDCTxCCCGEEEIIIYSQcfCtb30LcCtAYZZW1NWLk2vXir3TTjsNw8PDzcgi\nmebUPyVMCCEbKFMpXslUj/EylcrKZKqXGzD1ym4ql9lUKythKpcZMHXLLYqpXpYmU71cp1NZClO9\nTE2mS/myTJvLdCnf6Vi204V//OMfuPTSSwEA6c13hTOGsk5tsiOqq5/BypUrsWTJEnz1q19tdDbJ\nNIcKPEIIIYQQQgghhJAxIifIOh2bI9Wx+ZjScNI5fWrt1075BlavXt2o7JEZAifwCCGEEEIIIYQQ\nQsbAXXfdhTvuuAMAkN5s13GllZq9HZDrAKpFnHXWWY3IHplBcAKPEEIIIYQQQgghZJRUq1W91dWZ\ntQ2cllnjSs9JpZHebBcAwA/P/xH+/e9/jzuPZObAGHhkxjFdYlBMB2ZCnIyp0h6mcllOlTKKg2WX\nzFQto6lSPqNhqpZlFNOtfKdT2QrTqYynY/kK06Wcp2MZT5eyBaZP+U6nMhWmS9lOR66++mo88sgj\ngJNGetOdGpKm07UATuscuINv4Fvf+hZ+/etfNyRdMv2hAo8QQgghhBBCCCFklDz11FMAAKdrHpxc\nW0PSdBwHqY3+AwDw5JNPNiRNMjPgBB4hhBBCCCGEEELIWHHSjU0vxakaEoZbaAkhhBBCCCGEEELG\niOM4cFKNm8RzHE7gkTCcwCPTnukYh2KqMh3iY0xGfU/FctkQ2v1El/tklelUbF9jZTq1y+lW7tOp\nbKczLOfmwzKeWGSsY7k3jij/wfIlaPAEHjiBRyJgqyCEEEIIIYQQQgghZApDBR4hhBBCCCGEEELI\nWGn0FlrGwCMRcAKPEEIIIYQQQgghZIw4YAw80nzYKgghhBBCCCGEEEIImcJQgUcahuO6gQCuzQ4Q\nzmCx42eqBXFvRp1ONRsbRb12TcV+0sw64UEUk8NUbGfAzKqX6Rg0fTqWf1yep3JZT5dynsplWC9R\nNkz18p9O5V5vXqdSmU+18p1KZbPBkXLgpBt4iAW30JIIOIFHCCGEEEIIIYQQMkYcpJBq4BbaFLfQ\nkgjYKgghhBBCCCGEEEIImcJQgUcIIYQQQgghhBAyVhp8Ci230JIoOIFHmsZUiwlBfCY7PkYj28Zk\n2zJVmSr9rxH1M1VsiWJDb39TuW4AP38zrZ6mernPRKQNTaWyn27tup78TqXynSmMpZ1M1XqY7DY/\nVcvFxM7jZJcZIaSxcAKPEEIIIYQQQgghZIw4DVbgOYyBRyLgBB4hhBBCCCGEEELIWHEcOA3c9soJ\nPBIFWwUhhBBCCCGEEEIIIVMYKvAIIQ1louKDMKbH1KIZ9TGVY83Y9jYjr1O5jU/luoliNPmdyuU+\n3ZhJsZimW5ufrkzE2EqSYbzCINPZVvq/iYSHWJDmwwk8QgghhBBCCCGEkDHCGHhkImCrIIQQQggh\nhBBCCCFkCkMFHiGEEEIIIYQQQshYoQKPTACcwCOENITpHB+EjJ2ZHC9lsm2bSfHDphMs9+YxHj8x\n2fUgz59sXzfZ5TDRTJVyj2JDq4sNkYlud2NpU43Mo6TFtk3I1IUTeDG89NJL+NOf/oSlS5fipZde\nwiuvvALXdbHpppvine98Jz760Y9iiy22aNjzhoaGcNVVV+GWW27Biy++iHw+j2222QZHHnkk3vve\n905YGoQQQgghhBBCCKkfx3HgpHmIBWkunMCL4Etf+hJ+9KMfxX5+00034fTTT8e3vvUtfOMb3xj3\n8x577DEcdthheOaZZwJ/v++++3DJJZdg8eLF+PWvf43W1tampkEIIYQQQgghhJBR0ugttJzAIxGw\nVUSwbt06AEBLSwuOOeYYXH755Vi6dCmWLVuGCy64APPnz0epVMI3v/nNcU/gvfrqqzjggAPwzDPP\noL29Heeeey6WLl2Ku+++Gx//+McBADfccAOOOuqopqZBCCGEEEIIIYQQQqYmVOBFUCgUcMopp+Ck\nk07C7NmzA5+94x3vwDHHHIM999wTTzzxBM4991x87GMfw7bbbjumZ33961/HSy+9hHQ6jTvuuAN7\n7LGH/my//fbDFltsgbPOOgs33ngjfve73+E///M/m5IGIYQQQgghhBBCxgIPsSDNh60iggsuuADn\nnHNOaPJO6OnpwXnnnQcAqFQquOGGG8b0nNdffx2/+tWvAAAf+9jHAhNvwumnn465c+cCAL773e82\nJQ1CxoPjulMyuHQjERtH8zXTcR1nxgY5HottE1EeG1L7ikLKuNZXM9gQ+3ct6qmHZtTLhl4PM3nM\nrYcNzf6xvHdMdP+YyDqZ6Pqf6DFmPPY1o2w2xDG2ETiOg1Qq3bAvTuCRKNgqIsjlconXvPOd79Q/\n/+tf/xrTc2688UZUKhUAwEc/+tHIa7LZLI488kgAwLJly/Dyyy83PA1CCCGEEEIIIYQQMnXhBN4Y\n6e3t1T/XM+EXxf333w8AyGQygQlBmz333BMA4Louli1b1vA0CCGEEEIIIYQQMkbUIRaN++JUDQnD\nGHhj5Oabb9Y/v+1tbxtTGk8++SQAYPPNN0dLS0vsddtss03onkamQQghhBBCCCGEkLHhOKmGxsAD\nt9CSCDiBNwZ6e3tx7rnnAgA6Oztx6KGHjimdV199FQCw2Wab1bxO4teZ9zQyjSTWrl0b+5mc2Eum\nPs2KHSLpzqRYGY2wRdLYkGL2THdYV7WZ6D4+lvqYqPFoQ+rfU9nGDakems14+sxElr/5rMl472hm\nrE2y4cGxixAyWjiBN0pc18Wxxx6rJ8G+/e1vY9asWWNKq7+/HwBqKucAoLW1NXRPI9NIYqz2EUII\nIYQQQgghMx+eQkuaD1vFKDnzzDPx29/+FgBwwAEH4Etf+tKY05LDJ9Lp2h3d/LxcLjc8DUIIIYQQ\nQgghhBAydaECbxT8/Oc/x7e//W0AwE477YRrrrkGzjikz21tbVi3bh2GhoZqXmd+3t7e3vA0kliz\nZk3sZ+vWrcNWW201qvQIIYQQQgghhJCZguM0WIHHQyxIBJzAq5Mrr7wSn//85wEAb37zm3HHHXeg\ns7NzXGn29PTgpZdeqjlBBgQn0Lq7uxueRj35JKOnnrgWjHky82GMpqlPI+uGMdjGTyNsYj2Mn+nU\nL1gPY6eRMV8F1sPUYKL6xUyMhdxMplNM6pk8tjaFBk/g8RALEgVbRR387ne/w8c//nFUq1VstdVW\nuPvuuzFnzpxxp7vtttsCAJ5//nm4NQbb5557Tv+83XbbNTwNQgghhBBCCCGEEDJ14QReAnfccQeO\nOOIIlMtlbLnllvjDH/6AzTffvCFpv+1tbwMADAwM4Iknnoi97oEHHgjd08g0CCGEEEIIIYQQMkYc\nB0463bgvbqElEbBV1ODee+/F4sWLUSwWMXfuXPzxj3/EggULGpb+wQcfrH++8cYbY6+Tz7bccsvQ\n5Fsj0iCEEEIIIYQQQsjYkBh4DfviFloSAVtFDH/9619x0EEHYWhoCBtvvDH+8Ic/YOutt27oM972\ntrdh1113BQAsWbIEa9euDV1z1113YdmyZQCAY489tilpEEIIIZON6ziMs0MIqRuOGYQQQjY0OIEX\nwT/+8Q984AMfQH9/P2bPno27774b//Ef/zHqdA455BC89a1vxR577BF7zfe//32kUim8+uqrOPjg\ng/Hvf/9bf3bHHXfgqKOOAgBsscUW+MpXvtK0NAghhBBCCCGEEDIGGqzAA7fQkgh4Cm0EX/7yl7WS\nzXEcfOQjH6l5/e67746LLroo9PdnnnkGTz/9NLq6umLv3WeffXDBBRfghBNOwF/+8hdss802mDdv\nHgYGBvDGG28AAGbNmoWbb74Z7e3tTUuDEEIIIYQQQgghY6Gxp9ByCy2JghN4EZTLZf3zqlWrsGrV\nqprXb7rppuN63vHHH48dd9wRZ555Ju655x59YmxHRwcOP/xwnHXWWZg7d27T0yCEEEIIIYQQQggh\nUw9O4EVw6aWXYmBgoO7r41Rtv//97zEyMoJ0Onkmfq+99sJdd92F4eFhvPzyy8jn89h0003rureR\naRBCCCGEEEIIIaR+HMdBKtW4uJwM8Umi4AReBAsXLmxIOttss82o7ykUCthqq63G9dxGpDEdqDdw\nseO6Tc7J2BEbpnIeNySaWQ9m2gy6TcjEwDF29HB8IuNF+hvbEplu0FcQQqY6nMAjhBBCCCGEEEII\nGSOOAziNVOA1MC0yc+AEHiGEEEIIIYQQQsiYceA0UHnsgBN4JAyPNiGEEEIIIYQQQgghZApDBR5p\nGK7jwHWcMcWPaGaclGalzTgZkwNj6pBGMRHjzkwYJ2aCDWTDYCbFXpsJNhDSKGZS356OsPzrw3HQ\n2EMsKLUiEXACjxBCCCGEEEIIIWSsNDoGHidMSQSc1yWEEEIIIYQQQgghZApDBR4hhBBCCCGEEELI\nmHEae3IsFXgkAk7gkYZTT+ynmRBDYSbYQAghZPxMRHwgxiKcGph1zDqZ+fBdjxBSL44DpBo4ZjRy\nLpDMHLiFlhBCCCGEEEIIIYSQKQwVeIQQQgghhBBCCCFjxGnwIRbcQkuioAKPEEIIIYQQQgghhJAp\nDBV4hBBCCCGEEEIIIWOmsYdYNFTNR2YMnMAjTYOBfwkhhBBCCCGEzHQcB0g1cgKvif9KV6tV/P3v\nf8f/9//9f1i5ciVeeeUVjIyMYM6cOXj729+OxYsXo6enp+70HnzwQdxwww147rnnAAALFy7E4sWL\nseuuuzbJgg0XTuARQgghhBBCCCGEzHAuu+wynHjiiVi3bl3k5xdeeCG++MUv4lvf+ha+9rWvwakx\nk9jX14dPfepTuO6660KfnX322TjiiCNw0UUXoaOjo1HZ3+DhBB4hhBBCCCGEEELIWHEAp4EnDDRL\ngbdy5Uo9efe2t70NH/zgB/HmN78ZhUIBjz76KC666CK88sorOOWUU/Daa6/hBz/4QWQ65XIZhx12\nGO6++24AwIc//GEcfPDBcF0XN954I37729/immuuQW9vL26++Wak0+nmGLSBwUMsCCGEEEIIIYQQ\nQsaMA8dp7Fez2H///XH//ffj4Ycfxv/8z//gmGOOweGHH44zzjgDjz32GHbaaScAwA9/+EMsW7Ys\nMo2LL75YT95997vfxTXXXINjjjkGH/3oR/Gb3/wGZ511FgDg9ttvx//93/81zZYNDU7gEUIIIYQQ\nQgghhMxwPve5z+Guu+7C7rvvHvn5rFmz8POf/1z/fsUVV4SucV0XZ599NgBg0aJFOOmkk0LXnHLK\nKdh+++0BQE/mkfHDCTxCCCGEEEIIIYSQMSKHWDTqq1kKvNmzZyde8/a3v13HrXvqqadCnz/wwAN4\n4YUXAACf/OQnI/OaSqXwiU98AgCwYsUKLF++fBy5JgIn8AghhBBCCCGEEELGiOMATspp4Nfk2eK6\nLorFYuznf/7zn/XPe++9d+x1++67r/75vvvua0jeNnQ4gUcIIYQQQgghhBBC8Oc//xkjIyMAoLfB\nmjzxxBP65ze/+c2x6ZifmfeQscNTaAkhhBBCCCGEEELGjKeca1xyXlqVSgVr166teWlPT0/jngvg\njDPO0D9/5CMfCX3+0ksvAfDi5RUKhdh02tvb0dHRgfXr1+t7yPjgBB4hhBBCCCGEEELIFOORRx7B\nrFmzal7jum7DnvfDH/4Q99xzDwDgv/7rv/COd7wjdM369esBAG1tbYnptbW1Yf369foeMj44gUcI\nIYQQQgghhBAyRhwHSDXw4IlGplUvd999N772ta8BALbccktceOGFkdeVSiUAQCaTPJ2UzWYBoGZM\nPVI/nMAjhBBCCCGEEEIIGSvqEItGpgcAixYt0oq4ZrJ8+XIceuihKJfL6O7uxu9///tY5V9raysA\n6Dh5tRgaGgJQn1qPJMMJPEIIUbhqpctpoAydEEIIIYQQQsZCOp1ueIw7myeffBIHHHAA1q9fj7a2\nNtxyyy1YtGhR7PXd3d0AgHXr1tVM13Vd9PX1AQC6uroald0NGk7gEUIIIYQQQgghhIyZxh5i4UzQ\nFtp//etf2H///bFq1SoUCgXceOON2GOPPWres/XWWwMABgcH8cYbb2DOnDmR17366qt66+w222zT\n2IxvoKQmOwOEEEIIIYQQQggh0xXHAVIpp2FfDd2OG8MLL7yA/fbbDy+//DJyuRx+85vfYL/99ku8\nb8cdd9Q//+1vf4u97qGHHtI/77DDDuPLLAHACTxCCCGEEEIIIYSQDYZXX30V++23H55//nlkMhlc\nddVV+OAHP1jXvQcccABSKW8q6ZZbbom97tZbbwXgbQM+4IADxp9pwgk8QgghhBBCCCGEkLHiwIHj\nNPCriXldvXo19t9/fzz77LNIpVK4/PLLceihh9Z9/6abbor3ve99AIDLLrsMr776auial19+GVdc\ncQUA4MADD8RGG23UmMxv4HACjxBCCCGEEEIIIWSsOICTauxXM+jr68P73/9+PP7443AcB5dccgmO\nPPLIUadz9tlnI5PJoLe3F4ceemhgEu/ll1/G4sWLsX79emQyGZx11lmNNGGDhodYEEIIIYQQQggh\nhMxwvvnNb2L58uUAgNmzZ+O6667DddddF3v9Jptsgl/+8pehv++00074yU9+gs9+9rNYunQpFixY\ngF133RUA8OCDD6JYLMJxHPz85z9n/LsGwgk8QgghhBBCCCGEkDEih1g0Lr3mbKIdGBjQP69atapm\nDDsAmD9/fuxnn/nMZ7DVVlvha1/7Gh5++GH85S9/0Z/tvPPO+N73vof3vOc948800XACjxBCCCGE\nEEIIIWSGc8IJJ+A///M/676+tbW15uf7778//va3v2HlypVYsWIFAGDhwoWYN2/eeLJJYuAEHiGE\nEEIIIYQQQshYcQBnGijwdtxxR+y4444NT3fevHmctJsAOIFHCCGEEEIIIYQQMmacxk66NfMYWjJt\n4Sm0hBBCCCGEEEIIIYRMYajAI4QQQgghhBBCCBkjjT7EItWkLbRkesMJPEIahOO6gd9dDrqEEEII\nIYQQMuNx0NgYeGhkWmTGwC20hBBCCCGEEEIIIYRMYajAI4QQQgghhBBCCBkjjuMg3dAttA1Liswg\nqMAjhBBCCCGEEEIIIWQKQwUeIXVgx7erh6hbxhwWz60mX+OMfT5+NOZNVRv0Y+qwZVzhCZPsmAk2\n6Ic0t01t8DboBzWvTdGG0Txocm3Qj5mi49NMsCHwmAR7mj4+TYCvmJAxFmhqu9rgbdAPmdz3QNog\nD5ka7+NN6xcNGl83VBwHDVbgUYJHwnACjxBCCCGEEEIIIWSMNHwLLffQkgg4zU4IIYQQQgghhBBC\nyBSGCjxCCCGEEEIIIYSQMeKg0VtoG5YUmUFwAo8QQgghhBBCCCFkjDAGHpkIOIFHiMFYDqsYDZJ8\n4ng8msDLcffUEYh2LObOJBv0Yxppy0ywwb6HNtR9T13vWqO1YybZEHhQbXtGY8tMsMG+hzaM4vrJ\ntkHf1DxfMWX7tnkPbajrnplgAzD93gNngg1R1zflHZaHWRAyZeEEHiGEEEIIIYQQQsgYSTkOMg1U\n4DVSzUdmDpzAI4QQQgghhBBCCBkjjd9C27CkyAyC+lhCCCGEEEIIIYQQQqYwVOCRGU+z49rFUa3x\n3BS8JZVQ3AorToUTFbciIbaGK7/XiGMhWYvLo/y11sJPrA0WzbZBsG2xLYvKZmJwWPX8SBusaybC\nBiBsRzNtCOWjyTbExpxqpA01rhGaaUMgDftvM8mGmHy6o4gFVGsMTRqj6h1jgdHbACTb0VQbIvI4\nLhvixrBm22DRTBuEKFsaYkPSGFXLDzTQBmHUfsJ4/lhsCOWjiTbUjP3VRBuiGO17R0NtqJHHibAB\nqN9XJPZtYNS+wsx2U95lJ8AGoMnv46P5n8LOu76JWp/R0PhTaCnBI2HYKwkhhBBCCCGEEEIImcJQ\ngUcIIYQQQgghhBAyRhzHQTrVOH1UikHwSAScwCOEEEIIIYQQQggZI94EHrfQkubCCTwy7ZismHaj\npRKRTRnTJdaFjlthXafjVOjvNWx2K+qmYFq1Ym/I8139e3zyNok22PFbxmKDusdNxQ9RTbFBF16M\nDUC8HWKDwnGCcUVq1UfFSrMeWybChma3qZANMS8qjbTBzCOcdCCpCbEBSO4TE2yDn6Z3cbqOF0an\nWkbgwbGJRo9PZh51H2mADXZ91LKl7jHKsgEw+oT+Q/NsiPMTQINsiOnXTbdhtL4iyoYEX9FUP2Hk\nedQ2GNTrK2w/4eUtLvcecTZ4z5U8jd+Gen2FXR/e32Kz7+W5njG2GTbov0ePseY1DbFBHjeWdyd9\nc/Ns8K/3L0zyFXX7CSD+XVYnFj3GmtfUa4M5v5LkKxryPq4TC9pgPn/SbbDfYSU0p/0HQsikwwk8\nQgghhBBCCCGEkDHiODzEgjQfTuARQgghhBBCCCGEjJGGn0JL4SOJgM2CEEIIIYQQQgghhJApDBV4\npOFIjDq3AbLf6RLvLoqyCmQRCJejfolbndFxKqpeLAo/fkg18vrgzSq2hooFpONXRMTckB8rKo8S\ncyOutMdjg45NI7bUwrZB3WPGN3KRHpMNJmmVdccJRihJWzH4bBvqssOOYyTLJGYdqng0ErdE8i62\n1GOD5Nyvh5gYKLYtiIkpN04bKs2wwc5nI2wwPhMm1AbA799JNkiK0v4dP0d+fJroenCt3+uxQfq2\nlFUawf4QaPsq/XrHqJo2WPVh9+2x2CD1IeOefO5E1UO946yRz7hxVn+uvo9mfIqzIfLasfqKOmxw\nrbxOlA11+4ooG8RXpHN12WB+FkeSnxiXDYYdseOsGmMrMX6iHhvixicg2VfUNT7VaUPcGDulbUjw\nE0D9vqIuG2L8Xb1+AjDG2VHYUK+vCPkJ4zmxvmI8NihfYY9PjbYh0Vc04X3cZKzv402zQfLuJx5/\nLQmRcpy64gjXnV5k9FuyocMJPEIIIYQQQgghhJAx0vAYeA1Mi8wcOK1OCCGEEEIIIYQQQsgUhgo8\nQgghhBBCCCGEkDHS8EMsKMAjEVCBRwghhBBCCCGEEELIFIYKvASGh4fx8MMP44EHHsArr7wCANht\nt91w6KGHNvxZr7zyCm6//Xa8+OKLyOfz2GabbXDAAQegpaVlQtMYK47rNuzQiel8eIUwXPZsMFdP\nMlYAYx0zWf4sgWYrRfV7Wf1uBo+3gs9aAZidjArijVzg86oxXy/BcksqKQmSXYmJa2vaIIG9M1JF\naesaO4ixsqWmDfJnCfib9r67mUJ0hhplg5R/OjoQtm1DwI56bVD14BjBi6uuBCf3fi9rW2rbYNoh\nWZZqsG3QwaQrpUmxQbpwNaIriw0S5zdrfZC2bAj1CwBQNsQGi5e8qv4ANxP6TA6AsG2wD0+IskGQ\n+M62DSl1TyrqAA6xo14bVD1ABegHwocE2YGwy2OwIW0H67f7RZNtkD5cqjTOBj8ufY16KKsxKi7Q\nurSXtN+G5Fo9zqo+U7VssNtWLRtSelxK8BOGHbG+IsFPAMm+YrRjrJnHRD9h5LFuG+Q241AjbYO0\nK/sAiBgbatkRsiHOT5g2xPm7GsHi48ZZaUv2GGqPsaOxIc5PmHm3fcV4bHD0gQO1/QQQ7yvibDAb\nUaKvSBqfgHhfkeAngGRfkeTrgHhfMSYbIsZZAHBT0Yd/mfYkjbPhMdZPMc5XNMKGJF/XKBvifEXd\nfsJIRHxFkp8ARu8r6rIh7n8K24YIO3Sfzqr3b2k7EQdwkDCO4xg+fPw08kAMMnPgBF4M5513Hq67\n7jr84x//QKlUCnx27LHHNnQCb3BwECeddBJ+/vOfo1oNDqRdXV34wQ9+gE9+8pNNT4MQQgghhBBC\nCCGjg4dYkImAE3gx/P73v8fy5cuRz+fxjne8A7vssgt++tOfNvw55XIZixcvxp133gkA2HPPPbHP\nPvtgYGAA119/PVauXIljjz0Wvb29+H//7/81LQ1CCCGEEEIIIYQQMjXhBF4Mp5xyCjbddFMsWrQI\n2WwWw8PDTZnAO//88/XE29lnn41TTz1Vf3bWWWfhQx/6EO6++26cdNJJeN/73oftt9++KWk0A9kG\na8vfpyJ2HhuxhXeo7CkhzZ0FWbWS4qo/Ouq5GQS34TilYe8GtaXF3Dpob0HQcne97Ua2EImU39u0\nYVok24mKSrNfVPtwRMIv5jvWFhbTBkGvNFmSfSgbnPJIsg2y/Ve2TFSzARtEwg8Y27vcoA1xWzj9\nfPo/i7xdNpmEtq6JDbKVqDjk513skK1TskVCbJB6yOSDNpo2uME8+/VQ2wbTDtuGdJINpZGwDdLe\n4myQbTiys8iwQajE2FBru529BUTtstJNyBUbrC12kfUQY4NsVZFycLNGfchnqm9UrLZjt6koG/SW\nqFTQBm2j9B17myD8/i19I9YGvbXL28oSaA6poPvW9TAOG/RWQfWBtNO0vU3QtMHedhezHUdvh8oY\nn+stU1DPQyDPuk1FbCmKG6NyuqOr7djWrp+a9VCnDZ4defV8Nc66qi25qUgbStYWr1o2ZP0BKWiD\nkS/bV4TaUoKf8J5f21ck+Ym6bFCE/ATgj1GyrcvyFUl+ImCDoMYo8RNxNph2xPmKRD9h2hDj7+Js\nAOJ9hYxLYkOcn/Dsq+3v4vxEwA652fIViWNsDRtsXxHnJ4B4X2H7iZzlJ7zn1PYV9dgQ6yvEtlS0\nnzDtiBtnk/yE+VnIhgQ/EbAjzlck+Imx2JAxiy7BV9TrJ4AaviLBT5g22L4i2U8Atq9wkuphVDZY\nfgLe7+YYmuQr6hpjY3xFkp8wr7HHV21dWm/8JnWQchweYkGaDje0x3DQQQdh1113RTbbvIFreHgY\n55xzDgBPNWdOvAFAa2srLrvsMhQKBVSrVZx55plNSYMQQgghhBBCCCFjQ06hbdRXahqIYMjEwwm8\nSeSuu+7CmjVrAACf//znI6+ZO3cuFi9eDMDb1js4ONjwNAghhBBCCCGEEELI1IUTeJPI3XffrX/e\nb7/9Yq/bf//9AQBDQ0O4//77G54GIYQQQgghhBBCxoYcYtEwBR730JIIGANvEnn88ccBABtvvDE2\n3njj2Ove+ta3Bu6RybhGpTGVaETsudEQF5+vETHx+otePJVsyp8n1zFcJKaLDm6hLqhYsYBGBtTv\nw37eysFTkVMZFbdIxT6R2Bf6GHsVg6NqzNdLrA2J0zdSltgbKsaRCn6hY9AYDqRgBZXKpJQN6hrH\nsiE10h/4HQBcIx4bAKRUGbnZFu8POe+7jidixNepWDaMqMwOl604TVZ4klzGtyGnAwF5z7XjNem4\nISqOjthg2pFogx1fSuoHZowpV+Xdu3aobMXzq4TbXVblPa/scVXMLR0nSJqUjn2i6qFo2CCx5FRc\nGlfVu9gg5V1V9QAdq86PPVVNBfMqNozY8fxq2CD1UHVVDBpVVjqejopJpOO3lHz1cEpskPqIsUHi\nGQWag8SnkdBP6rv0A+kX9dggcaTEBol1k5c2ph7ilI3Ya8WBmjZIa3RU3t1cWT3DsEL6RrYQsMHu\n2zo2UDlsg3RliQdUUG0qpWzQ/USea9pQUjGmJNaU+t21TkFPiQ2ZiHrIqHpQv9pxsqQ/DKvOXMsG\nGWcrKq8p3ZZUvaCGDTLOqrErcYwF4Oa99KrpmBhTlg1+fCMjBps1zooNeZWk47jq7wh8926u7Svq\nsSHOV1RUmSX5iSgbxFck+QnAL++UbkuDAZtCY6zYYIxDyAXjN0m/Fz8h45PtJzz7avuKJD8BGONs\njL+L8xNAsq+QehE/IfVg2hDnK5L8BACIV43zFYl+AjB8hRXrT+JoJfgJIH6ctf2E1EM6XA06fp3t\nKxL9hGFDyFeY7QxhPwEk+4okPwEAeR0/LugrkvwEUMNXJNhgxiBM8hVxfsL7LNpX+PVQn58AavgK\ny0/otmTYMJLgK+L8BBD2FRqphzr9BBAeZ0N+wpUxx39Wkq9I8hNA2FfkHHVTxbJB6iHifwr7HVbH\n7au2qgyEzCUROIyBRyYATuBNIs8//zwAYMstt6x5nfn5c8891/A0kli7dm3sZ+vWrRtVWoQQQggh\nhBBCCCFkdHACbxLp6+sDAHR0dNS8rr29Xf+8fv36hqeRxKxZs0Z1/XRgtCfjmtfXq8ZbNeitarUa\nsokOtWQmp3gVMmmVppx6JcovpTQa9uqqOtCn06gW/ZUzAHDUil+qrdP7Xf9dVmTl5MDwqr2sWvaO\neHkdLMmKn1oRVks/eUNN0VXIqM/UKqBdHMqGlFoBd5Qiobp+XbINLW2WDWoV08i7i+BJXaJG6Bsp\nKxu8lXh7Jbw16y8fduS9n0WNUEhbRmhVxXDABgCoDqg6GR4I2qBO3Uu1eX0xpeq0mgquvALGKWpa\nReh9H1CqzfXFcqQNAFBQjac956WbyotKIckGP79SF66qB1fUC8oGp+CtuKbauwM2RJ26pk95VEat\nHxm9DU5ereLbp9+F1C2GDf3KhuHBaBtyXttJdXSrvxunb1ZbAs+xT4EbKHoZ6Vc2iGrERJQVHVIP\njvfcrFKcuHL+kfRtQ4Eqigp3sNezRbUpsUFfV2gL2mCoNmwb7BN0h1TF9A4n2+D3DRmfgif5+SpC\n4yRjUYUMeAs8lSGvbkLKL6seTOVLRZS16ptWh1iKI7FhxDxB1BqjZJztglfwUg956ReOjLF+/rQN\nQ974WlHjbJwNjhpjAV9F7Vg2hNS1Ks/r9fiUbEOHqgcRtdh+wrOjtq9I8hNAvK+oqqMyk/xElA3i\nK2L9hDmGVKLHWRmfRmdD0FfYfmKopManoq8UE5V8pRoco6Q/JPoJINZXJPkJz46gr6iIWkp8h+Un\nxNeJnzDtscdZGWOlHlIQRZ7hS+XHGF+R5CcAw1d0eO+JVWWDPu1VXSdNRnyd+Il6bNB+Qp8gGj7R\nPRPjK5L8RMAG5e8crdAOjpllq08Dyb4iyU8Avq8I+Ts1PlXXr4m0wSTkK+QE3Rg/EVARJviKOD8B\n+L6iRf6kZe3eWFGvnwDifUXIT1i+Dgj7CnkPFGR8as+FbchqG3SH8PJu25DgJ0wbxFfE+QmzqRet\ndrVuKOjvkvwEEPYVMjBqNadSRNb6n8J+hxVVpz6lViuFKcWrhWyhbRQ8xIJEwQm8SWR42Bs0c7lc\nzevyeX/7oNzTyDQaxSOPPILOzs7Q30czWTZRW2hHO4FnUm8en3ndc/rmdoM29RLXov7Wpl6IWlPe\ny0ZKOVdnWDlX9RLqDvn/BNhbchzZ/qQmv1DwnK+rvlcLXp0MuX53l3/ABsv2y6f6x129MEjshZzh\njNrVJGRLVtmgXhhaVfKp4d6gLfIPzaA/CRZrQ169bLZ0RtoAACOO97Y7IDao7wPqhU1ePssx/wwA\nQKt6iZMXoTb1XX5Pq/KXehBbAKA6pF6i5aVObLAmv5xCu8q7fO/S1xbTXn+Ul395+fRtqkTaAAA5\nZUerKve2XDDv7WJDUdqS9z0VMQkpW0RC/9TIi3Srynve++62dOs0Suof5v6il8fBciVg02hsaMk5\ngd/b1e9ZmQSWejBtkPYU98+l2nYjL6NiA+C3q0rO+95vtyWxQdlUjJj8ylj/1EgbapF/OtWkZFb+\nGVP9wvvZy7urv0f/Y+aoLczahlyrYYPXJyrqu9gwZNkikxS1bJC+occjaUs5mVhVW2yGfBv0pLb6\np8YdUZOSleBWOvknQNqSa9RDVfdvr28MqlsHVLnLpEu/mvwqGhMt9hgl46z8s9+aCfaLFqccb4Pq\nIzLOxtmgx1gAyHs/63po8WwYqnj5kPFoSE+6BMfYWjYk+Qkg2Vck+gkg0Vck+YkoG8RXxPsJ/96U\nNb7avqIuG2Rssmyw/YQ9tpr2VavRviLJTwDxviLJTwBRvqJTffdssf2E2DBk9OW4cTanJ7/UGKtt\n8vPenqntKxL9BGD4Csm71If3e1H5iQHxEyq/A6X6bbD9RLuxGNeare0rEv1EwAbL30nfznrtTcbY\nfqMNDVn+zx5nk/wE4PuKnEyg6rYkNvRF2mAS8hVWv6jkLV9X9PM5VK7tK+L8hGdXbV9Rr58A4n2F\n7ScGZEw12pCMSeIr7EnIlDX5ZS7otmaCNuQR9BX1+gnTBj1GxfiJwbL/Ti3+TmxYPxz0d0l+Aqjx\nP4VtQ43/Kex3WLdjtfe9tcfLh9pa6xoLibUQgQkhpPFwAm8SaWlpQV9fH0ZGRmpeZ37e0hJcTWtE\nGknIKbdRrFu3DltttVXN+80Jr/FMnE0VxIakibxVg95Lr7x8AoCE/cio5cpqxkpD4lUoVUWlzyv7\nar//T6dbKgZucbJqokL9PSUvplmRhFhqGgAj1eA/ZOvUC8OQeoEbURdnUsEXN8CPS5J2PBtsVYJj\nqSpkFb+iVsIBwB2xlBXqHzOtSBCVjrbBUK+p77KSLP8oiw0D6gVObJCVyRbjha0sMWdS3j95eWtB\nUVZeU+rFxix/+bmqJl3gqn8I1Aqro1bC090qrYwoEvyXbmk6JTf4Irpu2HuuvEiX7OBMAPKqPZWV\npCWT9mwIK/AkJlCUDeuCNogqKS1KyKCiVysSqv4LqzytYq3o96l66NOqiijll1e/naJKkHoQ9Zos\nH4sN6h/Iar+xaqxUCbE2qJdQrYBLZfW9rpoIqyor/DhZbiDvouocKYf/YRIbJI5WCl4ZZVQ96PCW\nbtAGwP+HrNrnqRLcYVEljARtUP9Yim2pLmMsyQXzJGOLtmEk2LejbMiosawtH3wVEMWFVJ1uuxXD\nz2i1lGeDVhHak195sUH9U2TEjHMqQV8kKuGRsm2DZ7/5T5nUWV6NUS25YCfOqvrWvUIqpGqoJtQ4\nK/VQHVQ22GOssiFlTCilezIBex2tmvLahaiJ5J8xqYdBox7K1jgrviLWT5g+J8FXJPkJIMJXKMRX\nxPmJ/mK8DeIrQn7C9nUwfEVJlEbrPFtENRXnJwxlXkrWRSxfYfuJQctPAMm+QvxEJsZPeDZE+4ok\nPwEY/k7+IP/8u+ofaMtPyETLmiG/DYsNZet9RPsJ+ee/RdlgTpJJV4zxFVVpU1Le1hjrpWv7CtUv\njMUGwI/nJZPy0qeBeH+X5CcAwM1avkLboMbYBD8BGL6iGvQVbta7tpoN+glT3KV9tvqjPc7G+Yms\n4a911Wm1tvJ3SX7CsEP7Ch0DVI0lWWuMlTh3xguh9Ik4f2f7CVMRlOgrhqQeLD9h2qDyGucrnGqw\nLUnWTQWe1EOfNYFn+4lh5SeqRn8RX+EnZ/mKOv2EaUPassH2ExUjJrVMXIt6sM/yFUl+AgAyagVd\n26X9XVCpXfN/CvXenW73BtW0moyvREwYk3gcNFiBxyB4JAJO4E0inZ2deO211xJXKczP7a2yjUgj\niZ6enlFdTwghhBBCCCGEbCjwEAsyEdSngyVNYcGCBQCAF154oeZ1L774ov554cKFDU+DEEIIIYQQ\nQgghhExdqMCbRN761rfirrvuwqpVq/DKK69g7ty5kdc98sgjgXsanQYZG0lbaVcPeLL0krE9TXaG\ntmoZvyQWDPwr8XNE5l7pXa3TqAwFt0unW1QAcpHoq61SjoobAh1M2s+n7ETrGwluK+pVW3NkK4us\nInUUjO0mCMr4K1lJV7ayKBtU7BnZDlVdt8p//mBwa1Qq56Wf1jbIdlQVTNfYfqq3RlUk5on3Wa/a\nQqG3DKotLWkrJhPgl7uO6ZKx1jLU3gS3GKwHAKiquigPqlgusu0mow4A6BwJ/C7bECIPsVA/yBYi\nyftaVQ8jpfDWhfZCcKuExENpyXplKNtCM7LtQbanGVuYtQ39KtC32qoh9ZApKhvU9h85XATGVgq9\nvcuyQephrfo+WAzbIO3fVffmrHhlevup3kKrtkUNGbEI1XaW8oCyoai2sql2mWlXeVbbczKtfuw1\nx9oSItvP+q0tm3p7lLHtTsjLNruqbGFWsY1UGfoB4qO20Ko4Wf3edpZyn2wrUu1fDRSZ1uD2p2D8\nsmC8UdnKPGRtFZT6ENvMgP15e6ugbJVSf/d3FMnWLn/bmx6bVD1U1qttd6oexIZ0IRifVeLreBmQ\nOnEDeSta27GlP4gN5rWyRa1L1YNsM5L4QP5WqeD2NABwVSDvimzvEpusMTbTqrZ2GXuWJOYUjJh+\ngD+22tux16h6WG9sHSypi6UexFeIn5AxqxrlYirBcbaq62FtpA3iJwI2qJijvq9Q9aC3lnt/Fj+x\ndjC4TQ2I9xUhPyH704yYsNpXqHhMenuX+p7kJ4AIX4FgyIii5SfM7adysEgtXwH48bJabT8B6C1q\ntq+I8xNiAwCk9Zb5oK+oxBxiYY+xQNjfCXF+ojWbhY1sZbZ9hWx7FD9h+zogwlfIOKvGPf1qo9qS\nbAtcb4ypcb4iyU8EbJBxVm0tr+htwLX9BBDhK1pUbMJqcLeK7ScAv3/H+QqxIc5PAFG+QoUfSfAT\nQA1fIdtRVcw1GczFhiGjvUh/XjPkld2QdbhLnJ8A/Fh+cb5CbIjzE1E2SM60r7B8nT5MxAipIDbY\nviLJTwB+3NQ4X2H7iUrv2pANQshXqBAqtp8wt7+O6P4d7StsG8RPSGxCAGivWL7C/p8ixk8Avq+Q\nepDMpTq83VdO28w7yLCZ8BALMhFQgTeJvO9979M/33XXXbHXyWft7e3YfffdG54GIYQQQgghhBBC\nxoZsoW3UF2PgkSiowJtE3vOe92DjjTfG66+/jh//+Mf46Ec/qo8cF55//nncdNNNAIDFixejUCg0\nPA3SHF5Z562ijrT5K61ZtVo5XJYVTrUKLCe/q5VwURqJEmFkjR/DsDQgJ5GpVbk2rz6lVqsqqHRG\njo+3FAkAMCKnnsqqpTpwY9X6YiB/WbUiN2yc9iUrl51lb/jodoORvbUNEnBZrYSPrPPVU8U+OXVT\nrei2eKuUeQm4rVZeM12zoS7U97qOCiQtyi+1krxGqUNWrfdWEyVoeVr1h64Wvx6kj3SqlcxywZK4\nKGWInNRlKvCG13o/F3tFlaACoKu0CqJSkFPhRGVhqtdUkckCsqy8yurx673DkTYA/uq4/E1OGewq\nWMO5KEOGlarCOAW4uM5rTyPr1Kqsqm+xIS8r/Dl1KpxeiQ0r8HwVobJBrRq/3ufZLSoX0wZRJciL\nSVvJe26pYtkg6lF1cl3FqIdSr2pXvUrpqZ4jyoq8simnFDrV9i59L9pElRq0QatDVFpviA1KVVEx\n1LYdtkpHPbdHtTN/JVw9ywjaLaqE8jrPhuG1fZE2ZDu8/liQ4P2mDR1BpYeoI+RkTVEkSD2Iuta0\nQZRF1apXRhL4fpacfqcDYqs2bRwAoW3oXQcAGFE2lJWKw5FTSTu9vlzIqHJp9VUtTsfsaBtUPYi6\nZVW/l2bvoKFeU/2sIAG93WCw8u6C1IMKkK7GDcdQb1W0wkXVwyrvu4yxYkO2zStD03OmlB0pCYrv\nBsts2GpLa/q9NNYMhG2QcbbUoQLcKz8xWPLu7ZSTQ43g/fY4K6op8RVJfgIwDtzp3EjZIKeyer/a\nfmK18hNrDRvifEWcn3CMsTzOVwyvDtaD7ScC9RDjK8RPSN+WtiS+Dgj7O9tXZCw/0VWIOMVClGaW\nrxhesw5A2NeljXGjoJ6rfUWnUrqoNqTHJ/WDfZgIEO8rbD8hB0FUjMITtbYcsKFtGJB68FQ6pfXK\nh1h+AvB9hag5U12q71gKvDg/AcT7io6CKFKDfqJS9Z9vq9dEzSnvUOInhtesD9hgKvBCvqJTxX1O\n8BOAr7yL8xVxfmKW8T4ivkKXmTo0RNpSnJ8w7dC+IqcOq5EDYKpBlZg8SxSpgO8rVqv+YPuKOD8B\nABtZZybEqQjj/ARg+go1zqqxxG3v9i6oBG2oaF/n2yDlbvuKOD9hHswmim8ZmeSdWas5RUWolHcj\na1XbGvAVwravaFEHBLkJfgLwladxvsK2oaTaZdawoVsrbq33cVFCWn5CfB0AlC0FXosoImerg1+k\nTvXYSu0PIZMNJ/CayA9+8AO8/vrraGlpwemnnx76PJfL4Vvf+ha+8IUv4MEHH8Rpp52Gc845R3/e\n29uLo446CsViEdlsFt/61reakgYhhBBCCCGEEELGRgrBBetGpDcRrFixAn/961/x/PPPw3VdzJs3\nD0cdddSo0hgeHsYf//hHPPfccwC8mPvvfve7KRxqApzAi+HBBx/Eb3/7W/17pVIJfHbKKafo3/P5\nPM4444xQGr/4xS/w9NNPo6urK3ICDwA+97nP4Q9/+ANuuOEGnHvuubjllluwzz77YGBgADfddBNW\nr/biqPz0pz/F1ltv3bQ0yNiJi4X3+nq1Amn8XeLSDLfm1GeSiFrxs2ICibpoaLWvwCsPqNVwFVSm\nPKxWvtUqaapdKd0qwSPuzdhXEjtE1GuiRHitT6kG1d9lha9kBOwQ9dRGrd4KclnbJzHwgqv5JRW3\nxVzxK64fDNiQUfGZdCyUThXrpTQSKB/AXyUV1YSoQ0R5J+U+qFaYZfVeYjUBQGves2FQqXQqVt3J\nSnhFxSIsr/fVgyNr+9X3vto2qHoQBZzZPuRHUbjouGUqbuLq/uBqvimhHykrtaKqmy5RGhkxVVTG\n1PNVfKn1hopQqXRkJbmilD4ZlZarYrJIPBdRwIUfAkhoK1F8iDpnlbLBVE0JXart6Lg0og5pV/Ec\n9Wp+UIEnsWg8G7y8jyg1YVkpIdJZLy2J1yQx2NI9Q/pexw2q13R8JqUeWqvqQVbz1ynVTtXoQ0Oq\nL6e14kupCMtB9YxWVQz7z3fVir4oKkRxZNsgapFMi1cP6aK/4p+q2DYoVYLEUxRVrV7ND9sg/UAQ\npVFRFYiubVEEjRg2qHZVlDFK9W9ZzU+putWqHRXzKdXlp2HbIFkTZYUoQcQGqRcgHD9OkPhlMsb5\n5kYoIYe9/i2qtSFRfvUPBW2QuH6G8iSlxqi0XQ+iDil6zxNVhbQlsaWmDeInypafMEjyFbafqBQ9\ntV3KeJb4ioxSy4jyxLUULuInpG+LnwDifYVvgxpLEWGEKjvxFcU+GVu9fNl+ItuuYval/H9p0u1e\nbCnfVwTbrjxfxqc1/YYCr7+2r5D+0SO+LqIiHKWssX2FjE/SP2w/YdqR6VAKRIknZ6nXxE9IDLzV\n6/02FOcrbD8hyuAoG7Sv0EpnqYf+gA22nzDJir9TdYmuoPLfjpVqqjjjfMWgKveM6oeiqi1HmCAq\nQqgxUnxFkp8AfF+RUSpVGdvi/ES/EavP93fRvsL2E53KhqJhhB0/zlXquSQ/YdoR8hVdygYr3qv4\nCTN+XJKvkH4gNpi7GaR/ubKNRGwoSfw4Vf7KBttPALV8hdfu/DFWxeisyrPDMfBsXxE3xppxncWG\nqig7HfEVwfhx2tdZfiJgg/IV6bzqf50Dlg0e5jun2BHnK2wbpA7bjV0Xvr+LVs1XpR7WSZsyd8So\nPKq2lM6reI3qnSXlBtsQSaaRcevsXXWN5LrrrsP//d//4YEHHsCqVasCn+2zzz51T+C5roslS5bg\nm9/8Jvr6+gKfdXV14X/+539w/PHHN9WWDQ1O4MXwyCOP4Lzzzov87B//+Af+8Y9/6N/b2toiJ/Dq\nIZVK4eqrr8bpp5+O888/H48++igeffRR/fncuXOxZMkSHHbYYU1NgxBCCCGEEEIIITObW265Bbfe\neisAYMGCBdhtt91w6623or+/P+HOICeddBJ+8IMf6HTe//73w3Vd3H777Vi5ciW++MUv4sUXX8S5\n557bcBs2VDiBF8Nuu+0W2Ipai1wuF/n3r3zlK1i9enWidDSXy+Gcc87BKaecgj/+8Y944YUXkM/n\nse2222LvvfdGOh0Rd6UJaTQLdwrNuE9kXvrU6pm5EtPT5rWVOFWCrPhpRYKKs2bGwBtZH1TWVYuy\nKu6t7BdmqdV8tQLqn0LrI8/3FS7eim+vynNZKZGigqd2q9VXUSvJSqJjr/iJukWtWporfsMqPqAo\nvbLt3j0ZtfJX2EjFDZI4LuYJrupHOV2wz4rjt1aVT6kYVFWYyiNR6WyqFF+x6rWitwIpShDAWEle\no9QhqhyyrSr2l1qJzXd5yhCJRRiIgadqoygxpiSOn1bged+LSlXhwK+Hslp+blGrsRt3qrYkC6/q\nOll5rypVRanftMGri6G1Sp03rFa+JcaTinGS71Y2FEWhEz5JV9RaA8qWNVY8xeJQ+ARXOUG4VSnv\n5F6t/NLKVDnhcVjZ4K94S7saXNUfbYNCYrBlDfVY6BTaalBxJLFnVik159CAyofRi4rWqvicTq8t\nFbV6Rq3wi3qt6D9fTnUcUf17eI1Sqw4qFY2cxisx0pQyJLvRgE4jJTbIYXOiXpO+bceEVL+b/WCk\nqOInqTbbX/Keo5UNdmymkj/2iEqnqPu3973Ur9Rq6oQ8rZ4SG4b9dpipRsc2kuf3DQVt6DfUU2LH\nsCqrnHreRiqOXNFWJIgCr+grP0SlM6LHWaVS6JO+rGwQZUjBV08VpD5F6axjGzkBG3pV+5e+3Wco\n8CpKhTOUCY6zs1Tc1OF2y0+YsUATfEWcn0gbp1/G+Qq7b8f5CSDeV8xSKvPhjpgxFmFfUVyv+oMa\nn2w/odVThhJS+4qiVQ/q8+FytJ8AgDV9tf2dKI2G2kWhE7ZBq4RFkap8hVbrxPgJAEgpxUu+x7tX\nxln75Hhpy4M6jp+hXlM2iL8TXyF+QnydqLwrwVcO754YX1G0xljfBuNfByfo79KqLcnJ8fI4aUtD\nOmatXw4SP640HByXbT+xqWpLJVO9ZsfAGxYbvO+ivKvlJ6SfF/s8lWpmo6FAudhx/Ezllz/ORvuK\nJD/hpa/6pLQlpSaVE4zj/AQQ9hW5Dk+lKv4uEzpx3ftuxvGzfcWAGsPFBvETBdVeZxsx+OQ9TMfx\ns9TaokzVNlh+wrQhzlfYNlQjYuBp1bzlK5L8BAAMl0X5H+0r3KH+gA22nwDifUVOfLa1I8YUwkp7\nsncviK+w/YRy15g1aKgIO6z3cVFTq7akx6e+oOoc8H2FxPGTE8vb1D2wyp/UxnGAdAP/1WzmGRYf\n/vCHcfjhh+Md73gH5syZAwDYYostRjWBd8cdd+jJuyOPPBKXXXYZsuq082KxiKOPPhq/+c1vcN55\n5+G9730v9ttvv8YbsgHCCbwYdtxxR+y4447jSuPTn/70qK7v6urC4sWLx/XMRqRBCCGEEEIIIYSQ\nmceBBx447jS+8Y1vAAA222wzXHzxxXryDvDERZdccgnuu+8+vPbaa/jmN7/JCbwGwQk80jBcx4Hr\nOKFYcBsKdiy8IbXCmDLi9chKp45XIQu5oqwoB+PpyGr+8Dpf8aBVCaI0KHmrZdk2b8VEVm0zlqoi\nKuZGv1J4rVMrf0Pqe1nUGmqpzzx0ap1SEY6Uo1UJogzBiKjXhgL5BoCh1UMBGypKPZApeDYUVEyO\nrFo9DMaPC66Ciw2yAjusvhfVKnFUzIU+dY8ov2xVgmOdQmsq8ERRMbRGxThU5VAaVvFDVIzAkopB\n5ccRCcfxK8oJiapdiEpnWK0ejyjFiWmDrIr3tQRPUCxWrIoQRYJSdcjKKwCM9Ho/D75hnS6o8p6x\nbMirukwHVITKBuukSmnjQ8qWkcHgyjMAVKpeG1qjYrjIvcVyjA26LfkKNFnJl7YkbShtnXBbUKoh\nreZEvLJCFC596oS8QdUfpE25Zh2K4lA9T9qhKBJc+4THol8OUq5FdQrw4Brv9/JQ8BRaWdXPdXn1\n1VqMVxHKir5Wr6n8iPJuUCkTTBsqSp2zVqkTpB2GlZDBWISmDcPKhmHVH4qqrOx+l1OxgNqG/DqE\nHQNPn36q4vgp5dfAYLBvA0BFYoopdc4aVWb2GOubK8G4DPXYoFJ2qv49KDaoshIb/JiQ/qp0mxqj\nTVWi+bwRSwnZp/I+ZChPKtLvMtJmvXoQ1Z5tg2MoQEUdYvuKIdWWRB0S5yc8+y1foZDTSbWKUNlg\n+wkg7CtSqs32tlu+Tsdm8p/j6hh4Ss1p+YqQn1Bp5dr8saysToLMSt+w1WuVaD8BACPS3keCNgi9\nWjFvxYQ0kDhprh6jpB6i/USl6Ct/Mi1Bn+1a/s5XEVr+ejBsQ5Sv8GzwrhWVd9lwdn54yGhfIX4i\nztcBQErF2BO7xVeYpw0D/rjYPxKuh2GxYTA4dlTUgNCrFH8Si9FUr1W10llUU0FfMbxO2RDjJwDf\nV+S7VT0oX2G/y0pbEj8BAGsHlcI5xleIn+hVvs72E4Cp7A6Os1rNGeMngLCvyKs+1KJPoQ3Wg8RP\nGzLVa6rcRXk3NBD0FeIntK/r8ncV6fhxtq9QY4r2db3RfgIIt9lCt+rf4m+sGGwVy9cBvkrY9hVx\nfqLfOMm5WLXjpQZ9hfgJrUi1/IRpg95V0qGUt+InxNdJmRpNS3yFxLFcb/kK20+sFT/R7tsQjvmq\nHqfeO2SMkTij4icAU3EualqlzB4JjqnYQP+3Gy0OnMjdS2NObwrtYrP55z//iYceeggAcOyxx6Kt\nrS10TUdHB/77v/8b5557LpYuXYrnnnsOCxYsmOCczjx4FjQhhBBCCCGEEELIGPG20DoN+2rmFtrx\ncuedd+qf3/ve98ZeZ3521113NTVPGwpU4JGGM5Vi3k0msgKYyfjz5P0jMaoEhaxaVobV6qVW4Bnq\ntbUSF0iloZbycp0q9ouopqwTXM0nyalWcuqVxFoT5ZfE08koZY5jKBP6hy0bbMOVAq887D1fTrgy\nVYQhG1RsnVxnLmC/rB6aq/l2TB1ZtRxWq9KiDhGb0ipuSMqwQRQMQxGqBC8D3r2iSCgZCjytDlEr\nmFWVRq7dy3tOqTZKShnin3pplJQVY0pODh1Uq8jD8n1AVJzhPtWr4mTJ6YrlkBRSpECqXQz6bUjq\nYrjX+1tFlVVGxXzKtniuoVVskLhGEbFQREUoK9rrh4JqipHBcugeYb06ZVDsL1tt1Y+n4+VD1DYA\nUFRxk8SWsnpuWk79UzGHWvolnqK/4myrEvwYU8HYkMNa1RmOHyfFOzhkKyusfmEpQwC/bUjsmRFl\nQ1H1P32idNar91YVk8k8ydZWJdgxpqSNa3XLkKzmGypClUQ2Xw7YLyckarWe1EOEem1EtSHpFyOq\nXsQGGTvEBhkXACCr+pkTOjk0qPwaHpS+bSjwVB5zBaV4UOoQPaapOg7FGTXUZnISoiiNtA3qu2Mp\nsrQyBH5bzJVFWSFjujoFVPrFiPSHYJsC/Nhr2ZxSiShfISolOQ3VPiXb+1DFDVPjvfYVvcF+Idh+\nAjDqomLb4P2qlV8xfgII+4p0RlSExUAaUbHXtOJcjfe2r4j1E+3+Fh2xv6B8RSpGvSZ+QmKUeT9H\n+wqodxgZB4ZEPRd1gmuMr4jzE5URv99m1DhbUiphURqnpW+Ln6gGbRgwFHhig/Rv8RXyvXfQ8hNu\nyGP747ql/EryE4Cv1hZfIeWQilM5K/v7jTh+cb5Cxp91A97zotRrtg3Sv6V/ahti/AQApJXCrHVQ\n1Npy6rplQyXo6wCgV+U5zlfIvVpVJWOsrZg3n6fVa0EbbD8BRPgKFV8VWgkZLFNfbe63Q2lXI6rP\n2r7C9xOlkP1JvkL7Ce2vg78HbfDGjpZZXvurDA8H0hL88Slsg+0rbD+RU0o8UXd76Yj/CcYateuh\nuN7ydcZ7ue0rWlQfqigfYyrwgeAriB/zVfpB0FeE/IQoIY1YhKLi065C+rk6Jbs8JOpapXLuNXf1\niM/2bBCfLn056r2PEAB47LHH9M+1wo4tWrQo8h4ydjiBRwghhBBCCCGEEDJGHAQPLxwvKXUwUaVS\nwdq1a2te29PT07Dn1sNzzz0HwNsm29XVFXvd7Nmz0dLSgqGhIaxYsWKCcjez4QQeIU1iWMVGyeb9\nWCuySliyYkzp2EZqNV8rQ5RCx1wt61ercnoBb52srBXUvZZ6TV1mrvhpdYhS0xVFcaRWwsvDKjZP\n1kvTPPVPVl+1skKrElQMkLLYoFRTogTq9VcrbRtksbDQHVzx1Oq1QAw873tJq0MkBpBaRVbPK6nY\nI6mMt3pvxiIcEjWGWsG0VQkSr6Sq0i6Zyq/1Ei9GlZXKUJus9KrTzUTxZp/wCIRPedSqIYl9JcqQ\nAa8ezNXdVNo77W9E3SOKBn1wqD4NTtkgq8jGCa7DavV7SH0fVMqCdlG4KHWfxE2BnNxpKiG1siKo\nXivqelC2rPdPH/bpCFw7KPbHxPHz1UZ+PUjcFrGlXxVAq46nKOoWpYApGqvlVn1LHYo6QtScI1pN\nqGLCBPLn1UOhJagiFEWCFn5pRYKhWgqpEtTpf2oVPaPqNKPGDlHoiFLJy4utrFAKPFWWQ7oelA2i\nkCmXjLu8eCU5pQTqt9RrfkigsA0yzpQk9pOyoV/Fh0oNSzw/iZGlxoMhvx5krBAqljpEK26kPoz6\nr5RU/6p6bSmXl9hGcTHwqqFnamWFxJxUY6mM09Lr5HS+ohn7TY3RKAeVFRVLHeIrhGWMNU6hHVEq\njYI67TmXUvcEbYCtqoBf/9ImtJpT9Qt7jO0Um40YfNKuXFutbY2x2gZRGRkKMO0r8t5pz2krxpQo\nQqPEa1otJWrt/qDiK8lPAH47FKWJVhE6Vgw87SfCyi/bV4gNAzI+aZVz2AhdD0PBMSrJTwDJvqKq\nx1jVLyQGnBEDTVRTxQFvjHKsWIRDyl7xE1HqNVHziuJJVFNaRRjjJ4B4X6HV01YsQv0OZCjAbF/h\n6Ni73vgkp9OKn4iM42crznU9qDYV4ycA31eUB6xYhJbyqBihXiuNJPkK5SeUMl/sN9tSSK1txY/T\n74FqjO0z4tflVb1mLTWn9KmcvMuokrL9NeAr/0fk/aPftiHaT3hlUttXlLXaPOjr+o04guLvtK9Q\nMfj0TgzLT0j9Dxk29Cf4O7fqjU/iJ4YMG+R93Fb+V0VFOCRtKNpPAGFfIb5d+4mK6XeDO2KGLF8x\notWEqhwsPyH/U6w3xzJ9GnDtXT36vWld+H+KrMp763qJrxp+/ybJNOsU2kceeQSzZs2qea07wXXV\n1+edZtzZ2ZlwpTfJNzQ0hPWR/xOQ0cIYeIQQQgghhBBCCCEkkaEhFbIpn0+8Vq6Re8j4oAKPkAYj\nMQDLA97KRLHFP3VuyFKv2WslrrXiV7ROVgWAtaXg6nwKXpptA6LIsFb8dHwp/2kl62Q+WXktDXp5\nFlVFNauUgMbgLMovWcH0T9IVFaEV16g/qBABgD6lQijpVWgvrYK1aukrEszVau/nEWs1XFQJxcH1\nAVtSaaWqMI42l9X/EVUPoRh4yiitDDGUN7KCuU7Vg9ggSRSUnXr1UtRrEXFEZBVeylLUIaK8Kw4o\nubyhHkxlvLrQqoQ4dYilIiwbqhlRh6wtejZIWYoteYmFGKPQAQxlRVnqIRgnqygxV4Z6Q3aL0qU4\n7NkiypKyFTcIeiVcreYP+avGI/1ig3fvkKqAkYqcxienrxUD5eClGx3baEApAXxVhWq7/aoeDBmr\nqESKI7mg/faJdiIbMuL4iCpQ6mGdKM5Ue8zLCbp9oq5RdThiqL3KwRV9yZq0h5JWRKo2pWyoGkoA\nV5WDqEMGrNhrIfWaofoTxY1Wtij7pU3Jan7L+uA4UDWUF/YptFL/IyW7HpQSdtBvSxVVnq4yPFdQ\nSsiR6LYkKudq2a+HsqVKEMWV2CAr39mBoNoQACqqPcmp25K+FNmIGh9kzBdbxC8AQLmo4jMqhUmx\nsFEgHzXjx2m1trSRoOJrraGSAoCMUqa0GuOw+ArfBg9/jFU2FMUGVS+Dhg3iK0Qlm1enlEvdaQVe\nUKkdtEEUeEo9sz5ogz+0ede3Gv6wpFVTQV+hPYvlJ0pGDLo4X5FRNsgYK3UZijMKv+4qRRm7lQ0J\nfgIAWgbk1HnbZwfjimm1ufRlUwGllHe2r6jbTwDGKbRiQ3Dcsf2EmURBxiZR/pei/Z1WTWkVod+X\n4nyF43hKo2KntxNA+4kIlYmo5SrFoIpf6sH2E6Wq3w4LWvGulEd6jLKVkN7vw0bfSvIV2k8MWX7C\nbAhaNS/S1+B7kKi5bD8B+L6ipS/4riJ1GVZqe99HDBVfcSToK0pqjKpaqrGS5Se89CyfLb4ipK71\n0upV9/YZZSi+Qny2qIR99ZplgxtfD9oG5SvET4gt+dagEhLw1Wt2/DgpO/ETpf5oPwHE+wrdL0Jx\nRv1dLfIeOmztxCiptqR9nfiJlqCvA4Bi3GnA5Wg/MWjEoJRxNqv+d+mwyp8x8EZHs06hXbRoEe65\n556GpdsICgVvbC5aMR6jGBkZCdxDxgcn8AghhBBCCCGEEELGiOM0NgaeJJVOpyc8xl0SsnW2nm2x\n/f3eYl9HR0dT87ShwC20hBBCCCGEEEIIISSR+fPnAwB6e3trTuKtWbMGg4PejoMFCxZMRNZmPFTg\nEdIkikrCnx3u1n8ry1YUO7C3lrsr6brefqq2HRhbBWTrgfxFtlD0DAS3LIhkPyX7AowdG7I1q6i3\n20Vvoa2obTjZVv90odKwFwxYbz/VgdXTgefKFrOi2tprbvvo1VthvN9Fbd49HNxS5Qf+NYK3q+96\na5S17VFskO9O2hvmMioAsGevF5R52CpL/Yxy0IayETRctsDYNgjdErTc2lLkmNtPXQkorbZuWtu7\npPy1DY6/1lLKeeVfHGkJ5Mfe3qWfobZdlgbNwOeeXX1q28WIurXiehUhW1hKw7I9TLZ/hLcuyXam\nwdA2O6/9m1sGtQ1qq1ppxKsTfZiItb1LDpuojgS3p5l5lP4wJNt/VVH16G2X5UA5eAla27uUDSOq\nPGSLWnmoP2CDaxwiINvtSp0dyv7oLWo6mLux5bWst7WqAwdU3+jTW2i969qLqj70tii/Dl2rvsUG\nadOylVkC9Mt4FNhSpbbblUa6AfhbJWW7W9Xe5F80tp9ah1jIGCU2ZFSfblHRnDuHrL4NhLZGVXU9\nqLFF+sWwssFoS7IVVsqhVPRsGNGHiVSibTAColeLwa2CsrVfbJBeJ2Ns10DU9lMvPVlvr1jbgPWY\nL1uYzG3ARXXAimob4ivEbkkjagettCe9dVP1B92WrLFNtkeZNpRlG7Da7qb7m+z6Vu0gzk8A8b6i\nVFR+Qm8/DW+hdWXbo+5vQV9h26D9hDEOhLYK2gdxqO3gtp8w7YjzFUVlg9+3EcbeBqzKtz/BTwBA\nz4C1VVC2Icn7gLrO7tuBbcBq26ntK2w/IYeqRG4DFp9dlHAL6uCLwTg/4RvTLaEKhqQ9ButBnlbU\n20/VNkDDBvEVpf51ARvKmYKy16sP3aci95SrtqJsKOmQGtF+wtxCK31C2p+kEbfFfyiw/TS4Nd72\nFbafkDqs2Ac2Af72Sr39VLa0yhjrPXfAsF/7imHVv+1x1vYTEQdxyHgjW8pDvkL7Cc+GIcMPlexO\nIT5S3mUtP2H7OsD3Fe3qh05VH/L+Zftr7SeMetDvf8MqPIVsAzZCJgBAcdhTMI2Y9stBHFaz0gdA\nDAW3J9t+AvB9RavaMi1tqhLTlszDJqRNlMV3yzubev+w/URppCdgM+D7zFDPqMj/FHLITrAeAL9v\nZJURco3Oe+gNmdTCQXMOsZiKbL/99vrnxx57DLvvvnvkdY899ljkPWTsUIFHCCGEEEIIIYQQQhLZ\nf//99c9333137HXmZ+Y9ZOxQgUdIkygPD6jv/f7fSrKSLIFsow9PEGVIeTisXpPVcEFWYGXFWVZt\n7eDFpjCorFZ/KyW1Gq1WLSWvsnqZyXur+KJE8mzwYh4MJ9ogK7DhFb/15WBgb1mBFVWVVhqVgzYA\nxsED6jmVclAdInkVG9K5fMA2ACgXvRVMX5VgL72qtEdExeUrPvrV6uuAKkMJgCyrIVqtIqv5Vn0B\n/iqprF5rlYoE71c2yHcJ5g0AmRalCivNUffGHAChA5PLynTYBvk+VJG69KwYkHIZDAbEdtzwSqwo\nCfxVZFF+qXoYDCvwUtl84FpRhGobdAmpfJWDSh0zj1IP0r5a1Aq42CZ2u0Y9hAJ7V4Ir+qKa0krI\nIa8vy6EPgBEk3lJL+fUgylfVlgzVQkWpT4bU9z6tOPJ+z4tqTWxQ9VAxD+KIs0HqTvIjNgx46goz\nMLkoXfTBI2JL1VrNl3o3D7GIVU15aUhg8nbp61ZwdSCoSjRtGLb6Q0WNT6Uhvy1VikFlhSgspe5E\nPeYbEXEQx1BQPdRfrgS+p5VqraWslJCDpv2irIg+TESrCEui5h1UafjbTMrqb1kZy0bUOFzsCNhg\nB1c37YjzFbafaFH1UTLUxLoubPWu+q7btCi/LD8BxPuKctFT4g3bh1iY/sJSfpUGgopU6dui+BI/\nUR4M16F9qIutwCtr5ZehQEzwFeInSnp8rHEAhJSV7g+1/QRgHM6i6q6qysOxnqP7RTHYt00bbF+R\naRtU18ao/mH6CsvfWX3a9hOitgeAIfG/qk60ItXyFW7V6tuGAsr2d0Ja2pS6Vt8bULMHf6ho/1MO\n5Nn2E6W0XxODqnyl7iqqjbh2PWgbjPYn402Mr7D9hFY5mwdzWTbYbWnYUt71GfUvvqLbsqFaDtaH\nb4OMj+YBEKJ0jvYVokzVimhjR0hZKy2D/ds/kE0phC0lpGmD+Arts4eC40LcQRyBQyykb+i21KfS\nsP2EOqjEeB/V7+OWDfpAttC7bNBPAL6v0O1r0LLB9nVGR5T38ZANylfYfkJU9eWyH0usZCvwpB6s\nw8ykbM1DRMRX5JXSsqjfN6ydF1HjHwnhwGloDLwUpq4E7z/+4z+w44474h//+Ad++ctf4mtf+1ro\nRNqhoSFceumlAICdd94ZW2+99WRkdcZBBR4hhBBCCCGEEELIGHEcIJ1yGvbVyBNtm8EZZ5wBAHj+\n+efxxS9+EVVjQbBareLzn/88XnzxxcC1ZPxQgUdIk9DxK5SqAvBX/fSKvh0+RFbLZAV2UFaR/ZWv\nEUstJp/pVXxR+ogyxIqnA/irdVodohUu/SofogwQJcKgvrecZIMcW2+t+A0ZK76DVvwXWR2X1XxZ\n6dWrlhHKL1GniIrQV68MBWyQVVNZxfSusVQJtgJP6kHyM2zEwLNW8m0bZDVfVi+18itKvaYfp54j\nKiKl3qyWRkL3iB06bo3EK7OTFyWk1MdQlA0SJyz490G1Ei7PqOpVfMMxWzaMWMoKHbclwgb5zI71\nFVZCSllKTBjDBq0iVO1RrlU2DVY8lUhlWPJjrIBL2bhii1JWlEXVqWzRMWj8tiNohW1JtfNQrK+g\nDaYSQtqG9F3pG2JDSdkm9VHR9WCoquy4QMoGrTjSStAaNqh6kL5TKXWrNGzll2pjFdMGpSixlJDa\nBss23S5MG6w+Ib8Na1VtUN1SNsZSsV/sk34h6YcUR/IObMYiHAkqD6U+/DFWbFAxuYYiYq8pGyR5\nqYei1ZZ8G/x60OOs2CDXlDfyvtdSfkm/Hpa4Zd7vAyEboP4u+TDrMKhw0fEa1ef+GGsrIX2lVJyv\n0MpoGZ8ibJDxXasIR0TZImNpsH0MaYVmOH6Xrw4N3qNtKMtY4pe/VkvF+Aq5R6spo2KvSd8oimpF\njT9avRbtJzz7VJ6Ggyo+K2mtcrL9hPe8YXWvpTSSfl+21GsRNkgcSXl+xeoPtp8wlUd6nJX+XbLq\nwbJB/ETJUCTH+TvdH0rBPh0ZP64q7wxBtXZ/jJ+AkYavmlLtUewLxV7zvkcpv6Rd2eOs9nW2n6jR\nlvx3qOh6KBl9qWLZp2MRavWUNcaqW834cdJGpH/bNmhfVxxW1xv2V6LV2vrdSdug3vGqVj3A76P9\n1vuWXw+2DcrHGH+XchAfUbbUnGVRD8v7U4QNoQByuh6CfkLaVHCMDdaD9tk6jp/4CRkP/Tt1XES9\nA0CptUXpbPk6qR9Txapj4Nk2iLpWqwjVu4eRgREdFzJYV/qdxeoHZOawcuVKXHnllYG/yWEUK1eu\nxLnnnhv47Itf/CLa2toCf/vP//xPfPrTn8ZFF12Eiy66CMuWLcOBBx4IALj55pvx+OOPAwA+//nP\n46CDDmqWKRscnMAjhBBCCCGEEEIIGSsOGrqFtpn6u3//+9849dRTIz9bsWJF6LNPfOIToQk8APjZ\nz36GzTffHOeccw4effRRPProo/qzfD6Pr3/96zjttNMam/kNHE7gEdIk9Kr+SJR6zYobItixaNQK\n5FDEyXGCqBR0HDNZWYxapVZoVYLETSlHr+LLyptpQ0VW0OV5duKuvRIeXEWOtEFiwUnWRZEQFT9O\nVvRFnSTPkVVJU6VjICvh3j22DcG8yemI9ipyLTv8elB1W5bvosALx+3RcfxERVgaVs8Lq6V03kdk\nFdxSr7m2DZaqImBDdN3JSqx8DylDok6hrYo6S2yRU8+i68H8TF+rFV92f1BtSZ5hKB8GLcWXvkV9\nH7LVa4biIHSCq6xAS7tQ6pBKqYg4tEpHq3VkBTyoYtKqCjPmjY41F1Ta2Uhb8lWNhorQtttSh0js\nK/sUvqANQZVkRdqMrSKURxr5FIWHqIgGy7VtqAyHYzK5lt2irChr1ZSoeobVBeHxQPIuqgUZY0Mh\n8KyYhIA/VibWQzVYpt69ljpElCdykrMeW9Xfi3I6eDAWkvm3itSDnIYs90ZkS5R/OsalPik02gax\nbcSsQ1FwlMN5AswxVlSUXhlHqWqlbiTmlChiStV4GxAzRsWNsUPWGOvdq9KoBBVHtiJV1C1m3qNU\nqd7f1fgkiqe4E41hnAZsK45i+pDZxiQuXkUrzqNVU9pPSWhY850ixlf4fqK2r/P+GFRNST1IW4rz\nE4AxRo0E/b6N3afNcSnWBunbEr9Q+nbEtTLuahskbl6MnzB/1/VQDrZHKRe50h+fTOWXqKai/Z3Y\nJv1ftpeZNkjeJW5gRcfNlDE2OE6ZSE7ks7LdlmJOcC0afUjGtUqsDao/iIrNGOvjXut8ny31UNtP\neDaoNPVukuCpzL4N6vooFWFcW7LGrqCK0FbFqR/0+7io+oOq8mgb1LgzLO8Qyu+JylnbYCoQZZwV\nX6nqwao7/38K5beNfGhfYb8HWrt6Rqz/F0zkL8NWXw29l5GapDB9TqGdN28eTj755Lqvb29vj/x7\nOp3G6aefjhNOOAG33XYbVqxYAQBYuHAhDjzwQHR3dzciu8SAE3iEEEIIIYQQQgghGwBbbbVVaJvs\neOjp6cFRRx3VsPRIPJzAIxsUOsZPA+XNSZgr/lWJV2fHNpI4dbICa53SOlJDvebHr/B+12ox6+Qu\nM4WSpV6rFuNVOp4NvhJJVpBjT+arWqvINVaP9S2WLVrNJPeYJ7YpSypV67uUc0y8joqpvAjFZ7Iz\nJCvxwdXLJDsAf8Vf6qFaiY8fUtU2yPPiVTq+HUpZITF14mJM2aoK89S1+AVk9XmwHWrFWkTZ6pV2\nXe+i/IhvU/KZXKvVe7LwLWlrVYWc6FZ/PejV/JLVliKQz3Tst4rEvolXQkodidJB12XManXVUJ5V\nleJyuMZKvmeDpV4zZExujLKiqhV/qh3WUEJqhW05Wq1jn35qqmuqVhygOEu0MkSUUmacL9e2wfuu\n+4O07RpKSH2vKFyk/8WUrXkaoKi3RHEQ16Zk/DXVa2KHfUKi/lyfihns27Xw+4P3e0ipbbYt3b8t\nFWGMwsVX15rPC6oHBT9MmCi1RaGTbIMoq7QyqRLfPlwrXlaSilBUU2Y9+fUQfY/ulzqeXz31EFTp\nFOP8BGD0DVFN1bbBRPdvscF6gPZ1Vr8w/UOcr9BqbuXD/HiKyTaUtQ2JJvgqvbJlgzUOSk+vatWh\n8V4UUydaaWT1bVMRpE8st04Dlr6d5CfMa6TuopT/gK/UNuPX6fYe0zfENv1dVJQ12oeMs9qGGu9/\nQtFSqVcilP+Ar4QtRagIY/u35SfMvla2fIV+x5a2ZKnNazUpfxeJ+GH5binw9DtHhIowwVdU9DuW\nMZbHKc6rwXqQ93FbzRllg1zr2krtqDzpGLNif+0xyn/HMtphUDRoGCFtOtgfar17jFSDdcgYeKOl\nsafQOhP4/yqZPvAUWkIIIYQQQgghhBBCpjBU4BHSZMyTG32lUcwKnlY6BdUStVb8BL3iJ6uW1eAK\nrJmEKCvkkmoNxZf9uaz66VVQK2uiqpAHlupY8bNtiDt9LHCttTpbjYnjpPNtrC5XlCpOl0Nc+brB\nFWEgWVkhsej0Cq9WWYafoRfWtUqktg2AuZIvp2BKfdsVIW1JFEBGzJc61WuuHRMokL6yQfIl4Zvq\nWbXVJwYq9VolxgZ5lFbPGCv/CV3Cb0tBdYn3x2Ad+uqQYFuqpYTU+ZD4kTpeWTBjEtcosOIv6qSE\nfl3WCkRrRTzCBq2skDhRFVGAJtsg6boRyobAdeapf6pNJLUlrVqw+3akDaKElLIchfJL2emrWlWZ\n2TaYal4dP7J22vpkXVMAF6N00yckWjZUY5R6gXuVeqQap242EWWFpcBOGmXN+nJt2UYo9lrQT9TT\nH3z1mqUujrKlGuwbdlupxwbdN0Ljn8qPFmjV5yfMayoxcaWi0GOUZUstdJ/QcfziVFNBn1KPilDa\nm46vqF8touoh2JZ0vKw6VIS63emYm8F27gvypJ4QyFeCEepb0NfVEqS5WgmulLjJ1WCMs0HFuR0r\n1c+WOZbX5yuquh7CfsJozeq5yk775NwajFhtSdpKSKkNUREaam6Jk5Zgg1aZGhVQifUVwffR0fQH\nPzawvFREv0uYcfx0zN+k90BRr5l1aMetlTTdYD3Uo+aUuipb97p2HD/jV61Srkb3IRu/vgx/HPf+\nVwn2Bz3m1LLBGgfI6HAcIN3AwHWNVPORmQMn8AghhBBCCCGEEELGiIPGHjzRzEMsyPSFE3iENBjH\nXj0349XISm+c8stSVcQq9SIo6RXu5FXz0Kp4gjrEXNXUK9wJeatap6PWEU4nQkVYhy1aTZiwemyu\ngFqr4OG4IUHlmRmvJmkRVhQJeuU18rSvYDvQaqGElddAFuW034QM2eoNIFn5peOpWVKHqNPI/FNX\n66uH4L1ig/d7aL1Xn0Ibjn2UtKKv210p2Kdq52f0NsDKmz79NLQgbugstCqhdtIla5xwa0hPdJvS\n19avIpT+bccitDFt0ALPOtVrfr4M5YdKxH4/1eLVOvu2l66oCCUNuwLCGdWqxTraBhC0VbenOJWO\ntAMpp3ps0ONScIyNVE2JwmYUChcgOA7r+HEx8RR9pfYoVITaV9RWc5roGJRSpAnXm75HyspXEcao\n10bRt7V6TWLS1lBq+0qnoFqvLsWRG8xbVBs10/TbVB3eVJ+kHlSv1cJXnqn81WGDVhqVgu3RVmBX\ndJuqrW6LzJeMZda4EECnG1TTjaoe7D5t5dG16wv126HbXV39ITi41qMi1O9O9sBj50N9bL6WaD+c\n9A5VDStE4/q3qEmrWglZfz3oOnOt+pC0LV9n5iOxf2tFtP+nsIrQeneL2W1SC+3b67hJ+oTe3ZKg\nIhSFXkBNrt+zo5V47ihs0O9Oleh+QAiZfDiBRwghhBBCCCGEEDJGHAdIN3DbKw8rIFFwAo8QQggh\nhBBCCCFkjDg8hZZMAJzAI6TJBLaM1bntTLahjEayH4pHXteWmfq2qpqf+9s+g78bFwR+rRVw2kbk\n/6HdHxHbQeytUYkyf2PPhGwZqSc4ORC0ISmgtM6FvaUl6lorLXsrWy3sbaWJ20+N4kna/uxvAx3D\nVuaYgNNR6LyFwkfb14W3kIa2ilhU3Ji2FPHH0Nao0Wzv0lvn5NbofAXKsI5A0kDCFsq4e3SQ+FHY\nEFNnUsZ2WAAz/aR68NOqH70dSPfx5LujtllHXlcJ10O9oQoir7PKzm46UVvYY/NmbSkfzfbTest3\nNGEZws8aRT1YZRtpih3KoM6smdfV2lbuXSttKPjM2jcFD1Gqpx7s7ef11IceMyrRbcR+qu+vxzDG\n1vJH1vb20Wy3G41/NxmTn6ijUG176xmf9I7NUYyZ/s313RPy13WUm18PdWzDDIUqqL9i6rXbPigo\nkEbC40b3Hji6fHnPjz4sIu46k6SDOOyQBvVQsXxYeBtw5BNVHmv3De0njPwk5U3/T1HzKkmrjosI\nIZMKJ/AIIYQQQgghhBBCxoh3Cm3j0uMhFiQKTuARMoEkruRbK+CjoZnKinoUMDb1qBaSGEs5TEfG\nZOcoAwsHDlFIeJxWQjZ5KXY0KoypymjUMP499SkrwkLUJgWTrlO9FkW9TUSrOkfR1scy7gjNGDpG\no/yqWHWVpKrw0mx+sPDgQRz13TOWdtFMTFWVf7BBnUqoUbSpeurMZjRq2bpVk1Zjbto4MA7qbiMJ\nh780mvpUhMGL4t5d/DHM/9tY2kgS46nfUPuLSStwEEcjbYhRtzeDSOVcgj+uV6kdxUx4HR3V/wlT\nbNyfLnin0HIL7Uykt7cXruuiq6tr0uuFsREJIYQQQgghhBBCCLGYP38+enp60NvbO9lZoQKPNB6J\nl+Ry1WD0KN21MwbNdCNPPWoEqQbovsdSDhsMqeavvzhpln8STio95nuTVmknbIVtAtrShkaaZToh\nOKMsZme0NzSRenOSph/coHA4dtRFZL8QfzwGZXwSM6Ebjur/hCn2P8V0wXGcxp5Cy2qYElSrVQwO\nDgIA+vr60N3dPan5oZcghBBCCCGEEEIIIcTgkUceQankHeRz7733TnJuqMAjDcRx3cjTCjd0HCOa\nqajSpsqKiijcklZ8TdWC2CBmJSntRiPiSmEcBTOGVet641SYNshT4qLU6Fzoso1/hjzfr4exq7lC\nd1ppmfnQP8Z017HUg9+267dBrnUSnqfLx6iIpDymddkm50NiWeiYFmNqS3JrdL7MPjZWZWk9ygzp\nl84YoijbMT2kjKPU1JJ+vf1brqvHdt0vVOXJdzd81rKfH6ts4h4TKBcZy+ocB8w0dVuss707dVzn\naAW2et4o2km9tW0mWW8z1/2jjhvirqnHlHrNjer78c+VtlRf2t5NalwaxVhmPy/JTwQfN7rxYDQK\nrbqujSmcJD8BGP263gJOjX6MHYu9zijetez+H9fv/DHMeF69/X8cqrp63lPEBt3e07XL2ayvum1Q\n10WVj07OcrhjiRMlKYzFhyWh24eRrziFq25DY6g7XR+jau8yVtWuD/l8NGU7mt0U3HgxPhodA29c\n/xeRcVOtVvH3v/8dxx13HAAgnU7jy1/+MtLpNA444ADMmjVrUvI1oRN4zz77LB566CG8/vrreOON\nNzA8PIw5c+Zgzpw52GabbbDbbrshn89PZJYIIYQQQgghhBBCxkzDT6HlXsmms3TpUpx//vlYvnw5\n1qxZg2KxqD8bHh5GpeItHu+0005485vfjKuuugpHH300ACCXyyGbzQLwJtXz+Txmz56NnXfeGccf\nfzz23HPPpuS5qRN4ruvi7rvvxmWXXYY//vGPePnll2teXygU8M53vhMf+tCH8PGPfxw9PT3NzB4h\nE4K5mmYrK+JWaWS1LDsKBUZWrxbKc2spv4LXpNK1h4JUOuvnLSlP9gp47asj85XKWoqPCBmVlKGv\nnkpYtTRtMH42shy+R31g1pOU80jMkWTahhrLmPaKmq6PUXhqqZOkVVhfkeD/LcmGrF22NfKlr0Hy\ntTbaBnVLoorQXDVPaoZaIKXqMCCfqq1KGJXiI5MJ5S0RlZdMwi1SDxmpwzraVMruD3XEBJJ60Ora\nOPWaUYZ6LEtYHU5Z301VRVw8MlvdK/VR67BEX60nCuE6lH7ynLpVhIYCNOYmO820quRUxivjSjHq\nLnVPTJ+O9BNWvNSx2OCPDdFjZ1qX/ygUeKIOSYlCs0bGUsG+Wa/yI1IJ6WcgJl+iTMpGfh68VjfA\nQP4ir5XxxVKkit21TqXNWr7aVhzJU22ltrSlevDrI5hG5LViS8ZWpsbboBWHaUv5ZWErkkaj1PZt\nqHGR1Tb1O1Qd43JG8paVMTNY/+H8jEHGqsfwZOWy7pcZVR/1JC/vHZK3mMLS45KpItQDbm1f4b+P\n1TEOptOBtOupB+0rtA211WvmuJhWz5N32WqcDerz2sp868PU6N/HU479Q/wT0tY4k6Q8jFQRxpWv\n+MU63zm8vEmfrt0PCJkJLF++HHvvvTfK5XLN69rb27FkyRLMnz8fy5cvxzPPPAMAKBaLgQm//v5+\nrF69Gs888wyuvfZa3HHHHdh///0bnu+mTOANDQ3hwgsvxE9/+lP885//rPu+4eFh/OlPf8Kf/vQn\nnHbaaTjyyCNx0kkn4S1veUszskkIIYQQQgghhBAyLhq9hTYpxAwZHz//+c9RLpfR1taGD3/4w9hq\nq62QyfjTY7lcDltssQX2228/zJ49GwDw6KOP4s9//jOefvppDAwMoFr1g2UUi0WsWLEC119/Pfr6\n+nD++edP/Qk813Vx5ZVX4tRTT8ULL7wAwFshWLRoEXbffXe8853vxFvf+lbMmjULs2fPRj6fx+rV\nq7FmzRqsXLkSy5Ytw7Jly7B06VKsX78el156KS677DJ85jOfwRlnnIE5c+Y0MruENJS4+H8pYyBI\nJakSJMaIWnmVFXpz/Ssupo6srMkqslZVRMQA8pUV8tjaK/rmir/YkJXV+pAAIrjSLKuWeePCJOVX\nKmEFHPBXsFNaNZBgg6nAU3nMZmKld9439ZC0sSKaqF6z4qnVWk21V1xT9ahDRKVjKT9CygonuKqr\n1QVIXg2XtpTOBsshSmUmqdbblgJEqAOj0EpIQ7aQSbAhq1UVdbQl/RxL4VKHei0t9loqIsmeK/Vg\nlH9a+neSDbZqxiyoOFWCblMZZUJtRQLg2xupVjQIxPGrUyWctdULtZTBVkw6reLKFjwbSiOx90q7\nkyrTY5y+INyGJf2kehCrs2aRJ8Q2shXCph+IfY6uMxk7k5UP0kfrVYeYCoy4OFlaVaXH2GCbqoW0\nJT3u1FKbp4J9Q/q01EcpxqeatqYylmoq9AgrH3Wo12QcFlVPupYNco/YkArWR6kSr17L2D4ioR3q\nfIzCT4i9mXStelB2ZoKqqaQx1rtGPS8jY3lQzRVS+4/CBm1LRvpFsopQ2oOMsfXYoMdZnX6CQttU\noKXr8xXpdHS/AIwxCpaqVo+xiSYgL30pE2xTtrrWVmp718rYWdtXaD9h5D0xfly2fvWa7StSMe9O\nYkNARWj17zhfYavNTRvsHPr+UOVvFCrCrB5apc3UeA+0xtmkvpHSKkKj5ejYmzH1Ie+yo+kPVN6N\nDSdxKB9dcpy/ayrLly8HAPziF7/AUUcdVdc9uVwO+++/f82JuWOOOQb7778/HnzwwYbk06ahE3j/\n9V//hd/+9rcAgG222QYf/ehHccwxx2DhwoWx98ydOxdz587F9ttvjw984AMAPAXfjTfeiMsvvxx3\n3nknfvazn+HKK6/E8uXL8aY3vamRWSaEEEIIIYQQQgghGwivvvoqAGCvvfZqaLr77LMP0uk0Xn/9\ndVQqFb0g2CgaOoH33HPPYYcddsC3v/1tLF68eEynDwFAS0sLjjzySBx55JFYsWIFzjzzTFx++eVY\nu3ZtI7NLmowo0qJOMNyQSGX8g1m0WswJr4J6fwgqYLQioYZ6LWtdU5fiSNKXGCsJqoR0NmfYIKuf\nMenrleBoRUKUDUJer6LGr7TrFWRR60h+srUPwEnn/M9FaZBUD3rl31gCT1qFzUt+CqKmVMNslHpN\nlG5aiaLyWGM1X+yQdpWtpawwbTDUhrkk1ZSlXotUD2lRWFAtoON4qe9upRS6VezUtsTETdSqDaVu\nSed9B5hPsEFsTGeC7VE9MGiK9AetNFIKHFXGleJg+AFyUqVcmxQvy1Rt1KmaElWF2J3K+vUQUlaI\nSkBiLGXqV69pxZGUmVYN6IcFHwK/PJPqQWzIijLGsCGs/JI/S93Vr16TtpS2xlgbs9ykHqTPSm5s\nlXNBpWnaKuVrKyvkuaJ00GNNJvmALl8JE6wHraowbbLU2pI3+R7nJwrpcDv05bNBW/TzpT5UW6qF\n+AqtMq2hQNZlZ/kwqY9SubafMJ9jKly9NKGebymSRtGWtEJTj7FRRgTLTnyX5HGwRtxG32fL2BlU\nzevr5Pliax3qNW2DqDqdGjYIMt7k6uvb5jVpW4Fn50d910OJ4a/jfIVco1WdteLH6fFY1Xcm3Gfj\nbfC+Z3J2vECrP+hHGeOgjDtxvkKUiGKLpTKOwo9FKPWQrITy21IwBmWcfMfcfZBRY3KSr5B3LFN1\nZ4+z8r7vWO+B9SjwtL9LW33asX2dGi+MevD9nZfH8lB/5DPstgQAmZgxyn7/yFrvJ1G7YQrWzpNQ\nXEWEf81auylSxvt2pA3SloyOoMcI/fIkjVqUuNIfoL7Hv4/LZ45WNzd24mGm48Bp6MmxG/Z/0M1n\naGgIANDR0dHQdDOZDNra2tDX14eBgQF0dnY2Nv1GJrZkyRK8853vHPPEXRQLFy7EpZdeiq9//esN\nN54QQgghhBBCCCFkvHAL7fThwgsvRLFYRFtbW8PTvuiii1AsFtHS0tLwtBs6gbf77rs3MrkAW2+9\nddPSJqSZBJVfsnJoKysk1phaLc953/NqRa7VWCEcsVRZolbw1WvRagrTB8iKo451k1eDS4zyy1wt\nF0VJbEwdUYZYqqkWY8mx31q7FPtEHaJXkWvEBspaNuhVSfXdXkU2VzUzSnmSi1FWOFZMIFEkAECL\numetJSwThYtfD8HVfNMG+VGvvKqROJ20mg9jFVxWtkWVYReRtQJej3pNaqhVVrMtRULUSmwoFqGy\nIZNvBQCUBnsjbCiovOUD92bi8qVVTIYNCaoprQzJW4oEhJUV0oalX4giIVPw+kVUPUif0XWmVDQZ\nWy0hSixDeZZW7VvaUlzML1ktT0WpCGNOKpU2rRUJea+so+pBKyGVLfKcOEWCqXLKqHK1x5+41XxR\nezkBGyxlhSixtIpQ+l/8y49WQOp+EVRNhVpU2qyHoEpH2n1/Odia5POskXcnJvaabYOOyZdLVuCl\nVXvzFSaWqsJA+4qs1EPQV9h+oiVCRSiKI1j1HRqfpD5shTAQ8hVp1e91HD9JI2yCr/iS/qbqQ3xF\nkp8AjHYVc/pm2lKIZXKtxr21fYXkx7chYnzS6mmlYip43+NUzqaCW+yRegj0b+N5oTh+pj9W9thj\nVKyfiLJBqwdFJavqIeaoVLOEC9r/2qqp4L2Orgdlq9Ef4nyF/F38hLxzmHHt9OnnVqzJTF7eoepX\nfoVsEGW4ZUPWSFOr13LRPlv7CfF5ETYIOl6qtiGohKwVG7JFVOR5qy1ZPlvK0LTBV6957d7WzNvv\nVmmjnfox3oL3OFYswpYEP2Feo322nPCu2pjcIV0rqMCrrXQWPyH9P23aL+8w+k+iQFP36HrwPo3z\nE54NQcV5XIxO871Zq7bl9Oc4pXMq2Hcy5q6KuHjOMsaKAk/7geT/KWRMa6Qoh5Cpxoc//OFpmXZT\nTqElhBBCCCGEEEII2RBwkBCqYAzpEWKTHFiBEEIIIYQQQgghhBAyaUy4Aq9areLZZ5/FP//5T/T1\n9aFSqRHhF8CBBx6IWbNmTVDuCGkcmZZ277uxZUe2zoncPbQ1yt5S1OJ9b+n312B6rf0Nst2ooNKW\nrSMSvFZvzzXuydrb7FQeswUvz7KVRbZMyFYW7x61BUO2nVgm6C2tORXMPBfcLgiEt4LY27v0NpB0\neIjyt3epa2VrlMqjfC+qbVHRNqgtEarMQtuKJBCz2sJhbj+18ypbQdozsoUtaEOtoOkZvXUiuP0j\n2+LFYqiUR1R2jK0iajuhv73L2o4tJuhtQVa7gL91S74PVrytIK3Wdpe0ukdvF4zYSiE2SJvOZNWW\nRtm6ORwOJi2f+fWg2qF9oQQml23bRj1oG6ztLLZt0v7SeSPwu5SNE7ShkBUbgls3dZB1198yk5F6\nyMnWqKANdkmZ23YlT7IVUNpUqez5Q2lbejtOqxxEYh7EYW2Nkq2beuuSlFlr0AYjULx8Jlt29BYe\ne9e39Aej/qUupL23q+eNFMtePhyxQeWrRbZ8GgdxqL6ht0ap73ltg/QltZXO2Doo2x61DbI1SgKO\n21vLJe/pcD3ItiIZSwetrZttUrdG+8sU5LAWOaQmuEXRH2NVPeSC+QT87Xa2r8iqfNU6ACJui7yU\nd8hPpIPtAzC2ClqHIkiR2WOsbLUWPwH4vkLski2D2Zy1/TTKFAm3oPpmpiWr8urVbZyfaDHrUI+z\nQRukHvS2eO3rwls3Q75C10ewHqIOqNHhFnLKBrWFVvzBulK0nwD8LXnhcTZYWBlrG5zUg/e84DZ/\nvVVQ1ZG0pULcO4fxR+0rVD0k+Qkg7Cv8A2fUNTLGWgcFZcyQAjG+QsbfrAopokN/1JC3+P5Oxifv\nnvYYPxGwQW9lVnmzx1g5ZMUIJaC3Zhc8n10c7Avek5MttHIwkhWWAub2b9kGHAzd0ZqJ9hNAePu/\n9Adpj3ZbqrX9NB3jK7KqP6S1vzDe5exQBfZ27EJwG3DUFn/p53pLudgg/cF6D7TDFHiXqDqUMATW\n9njbh6SNOszGbT/VtqhxJxMMB2P6CTmURG9lFp+tw8GIr3MCNgD+e4fvsyU8R3B7vPxd+xLzIJKY\nw5vk3VHef+S9sT3jX9dfVvnQ/T3YDskocRgDbybwhz/8AaVSCdtuuy222mqrUd2z//77I1PHgVnj\nYcIm8IrFIv73f/8XP/7xj/Hyyy/Xfd+DDz7ICTxCCCGEEEIIIYRMSRzExBodc3qcwZsMDjvsMPT2\n9qKzsxNXX301PvCBD9R9z9q1a9Hd3d3U/E3IBF6lUsEhhxyCO+64YyIeR8ik4IpKRakFZEVWVvMB\nfzU8n4lZLZPA5Gq1TFQr7cZKWFfWS7+iZCuysiwrfrLy6oRWLf2fZeVSVBKyoi95dl1vdVSrjAph\nG7RayQ4SnQ6qhUQ9ZCoP5GexoU0CHLcElRiw1C1AOMC6qCQy+VzAhqpaPZZg3kEbguqQkA2iqlCq\nAqkHM+9SJ9mqtworq/gtllrMD+pt2KC+y3NFvZYriOJPKXFKRXWrf2/WUocURK0pK+GSuKXQyQRs\n8C5q06u2CPxuqwilbE29pfzkWKoEaR/S7rMjw7CRz8RevQItCgt9uIsEdQ4qUqNsEGR1XD7XyhBj\nxd1WVkjx2m1Kyro8PAAAcKuGAs+qB61A1PVgqSoMBaD0iTZVN+0Zbwm8imCAad23W6RdGCojW1kh\n9aCVkGKL15Zybd4p7hUjYL8eo/LB4OS5mJV3U+Uk6pCc+t6W9mwYkUMkHFHmiUIlOD6pTFs2eN+1\n8isndisljjGWViTAuLJBrtGqvTj1mqE2k/LMtnvf2we9MWOkqvq/qkJRt5jjgCiM42yQ/iDtQ5Qh\noq4FjEOEdD1YKkJRBkUpv/SBR9G+oj0TDBJv+wkg7Cu0D1OfizJF2yAq54Jvg+0r0pY6RPuJCOmX\nVjiLDSpvtmrK9hPZgm+DrzgPKr9spbY+3KRgqtdifIWoCSXwu4yhUcovy99Jfnwbgn7CHK9adfsO\nBrzX9WCp17Kius4Z9iu/Vi17/U98ha/WUTZk422w60Gr1xL8hGenpbDVNtgqQttPGDbE+Iqwr5O2\nVKsexJagIjXOT0TakIsO3i//lEs+AF9JKH031+qNs+IrtG1yqEYtG7TyP6hea9Ftyfu8avhh8RVi\nZ6bF8nf2+ISwilCr+UW1qfIs/ULaWMZSEXp2WP1a2yCHRigbVJtqG1IqQuMgjpANheDY5tj+Wu4z\nFWiqzmwbbD+RbWkN2BxIx5cRqkSzKh/B9w/bTwC+r7D9nfYT9oFNxqRM1lIJ6zajfEXIT0i/MFSs\nWfv9Tw8ewbLMtXv5aVlf1PfKGCUqSfHp2ldH7IQhZEOhr68PBx98MM4//3x84QtfmOzsaCakV/7q\nV7/Sk3fvec97cNxxx2HRokXo6elBKlLP79PsGUxCCCGEEEIIIYSQ8cAttDOHt771rXjsscfwxS9+\nEU8//TTOP/98pNOTv718QibwbrzxRgDA4sWL8dvf/pZHUpMNAr1Kljdi7shKZyaoEtBx6tSKn14J\nV0o0M17FYDk46d2p0sy1qdVCUelkgqoKU4Wdt2JuiBLKVLh4+ZCYbH7cJln10/HjbBVhShRYOZWv\nnLLBz3eH+rnsBuMCyQq4XvnTip8I9ZoVe01W9HOtXZ7dagU8JSuwhm0S46w1F22DrDimLXWLmdfO\njMRygfpdKQvag3WXynr2uxHjntig1RGqbLN6Fd9Tt5gr0KEVfVm1jVFzppV6zrRBlB+dJS/z2YrY\nplZitRIyGJvJVlUAvpKgTdSKaqVdVsKrrSOhe3ItXcpeUelkIm1wneDqsanAkzx2FT0bZAVc1Api\nY6hNIaysEBvknoyOBSXKkCEvP0bcHh1jShQ3ola061ni6Bir2NI2pK10DpcDt9gKvGxB4goaNljx\nNTKWAi+n8pVRyruMii8lcRYBv69oVa2oJ1PRcY1gxKDLtHg/y4p++4CnSpA+nbEUeNlWscGIgafj\nAnlIPyzYak4pa9UvACBV9NqVKAtlDNN9SOL46PEvFXgmAKRbRJ0bVAmXqkH1WqfE/WwzFYhKHWLH\nXrPiKWq1r1J+SX14eVFlIp8pXyH9QsaFUD0AWkko9ZDV4+ygynOwr4ptOdMGdW+cWjtvKfByaiwp\nWn4CCPsK7SdEbW6paoEof6fGJWX/YCXoJ7okhqxhg/gZray01GvSl/KiqjWUX+LvxFeIDb7vDiqE\nI5ebrRhTuTZpS14GbD9h+kFpTzqeovQvK26t9McWGdOMPiR9olr2+p/4u6zq26JMEhWhqfzx1dqi\nsFQq9hZbIaz8hLbB90cyDmv1Wj5YD2KtP8aKMtUohxhfIf0+J4pAGZ9TETaoJ6UsNacov9qL3tht\n+4lAuq2iQBR1rRUDT3YKmPHTxN+pfl0eGYy0QfpQmzXGAmaczqBqXpSmOZWvzpFwzPAkX+FYIgmx\noWDUYUg1b7UpqR8ZY1uNWKD5mBi88v4p/VN8XqfyE1X4Y6z4ii4ZM5VP0f7OGp+0DdkIGyxfYfsJ\n6TuiMgOMGJd2HOGs5F3GWPU+3uulafoJqUPp99IfMgUvjVC8V6NatCpTxijLV4ifkPFKxmGzD4V8\nhdiQFoVoMEZnl6FAHFES56wo8Ozy1/Es+b97PTjgKbQziTvvvBMnnHACfvOb3+DHP/4x/vWvf+Ga\na65BR0fHpOartvytQaxevRoAcNxxx3HyjhBCCCGEEEIIIYRMSVpaWnDttdfi1FNPBQDcdttteNe7\n3oWVK1dOar4mRIG3xRZbAADa2toSriRk5iAqo5wRrydnxcALTWeng6uWejXfUA0o0RQqSpXQqVUJ\nEgMuRlVhPE1WbdtU3tbJqmR7j3evnHonpyIaK/6y6heKR6NPebTi6aiVyNZWXzXRVZE4fqKsUOqQ\nDrXyJ8qQrMQPMVar5ZRHVYYdyoZVooAyFC6Av/KZF6UGgKycjKbjAiHyHlEkyIokYKyGq9PsStWg\n0kjUIVoZYqkqPHNUXCa18htSr4nSSGKumTHwCsGTKrWiISaOnx0jC/BXkrusE0Nl9VhsyLWp2Gi5\ncD1InLqcnMprKb9yrd7qlKtiM5k25JQvkGtbsjE2SDnlpG37bUjy2DngxXJJIRi3Kd+ZC1yXMmOv\n2Sv6KcsGUaSqsq7Y9QG/jrQCT9Qx1umnWnmZ89uQ37+VDWpFX58QKGo61R+yVnxL72I7xpT3vWDF\nXpPV+kp7t5efsq/2k76SF8WNjE8pW1URr14T5VdXdiiQHzmVT/q9Vhu15I2Lguq1jCPKM1GvBRWh\nZUOBl8mVVXrR6hAZ4xyrHvSYAn+szIkSMisx19ygDZaa03uuKL9EWSGKP+/P0pby0h9U2xW/AAAV\nraJrD9hZsOJlRS172nHLbF+R5Cc8+231WjAWlMRCFGVmTtmfNVWElq8QO+0xNvoUWonbFVRri2rK\n9hNSP6YSUvs7o14NE/SYIu0ia6iHRKXmqPhUtq8Q9WTsGGvcI+1aVFwFZUtnRdQ6QT/h2Wv1CX1y\nfPA5uRg/4dkQ7StEzZnLBVWEkTZY8RT1+K/Gnx516ulgOegnzGukDlMRPhsIq5xzxjtFnK8QP5HR\navP4OH6iDk9rNZ2Xj3ynVw49w0Ff12rYIL5CFG++ilAU+soGuddQfoV8hXqHkvqw/USLjAdmhwiN\nUaIEzQfy19lfDOQHiPAV7UFVKzL2GBtUQpp5y7V4pwGXR4JtSvyEr4Q0dgRYild/N4l6d9A2eN+7\nesMxcbUNlpozToEnKud8IBZhKpCG+ArbT0jfaTGVuOmgr7D9na82j/YTgO8r5J1axjKthFT1IHeY\ncU11XMgYX2H7Cb37wqgHOWU69AolKmc1Toq6Mep/CilO8ekS+y9q5wWJx0FjVXOUPU0+juPg7LPP\nxnbbbYfPfOYzePTRR7Hbbrvhpptuwm677TYpeZqQXnnggQcC8E6UJYQQQgghhBBCCJkxON4kc6O+\nuHNx6vDxj38cd911F2bPno3XXnsN++67L6677rpJycuEKPA+8pGP4Pzzz8f3v/99fOQjH8Gmm246\nEY8lE4zrOJFxvjZUcm1BZQgAtKuf5aTS0GpZaMXPWwkrdPuqle413hpeVa3+yQp41lJ+heLpmKu2\nmaCyIK/yVVQrsDp+mpwkaKjnWsSGhPhxevVY5U/yCQDdKpaL2KCvaRPFkcpHxGq+FjSpVVO9oq1W\nXkU94TjdARvMeshr1ZSs6AfXMrT9SqEiq/kAUOhSK8nKhkrFW74UZV6uQ5QYLYG0AipCbYNSNMiK\nqlp5LejneTakzNVaZadWHIkNodhrEhNGlFIFPw21Gt4x5MWjaZFYeLJ63BlUQmr1mBUTyHxuq47f\notqUsqFa7Q7d468wK+WXWhXP2Svhlnot2+6fICl5lNhrWWWLqFx1v2j37E4bCjj7pEqtDhGFi6X8\ncqtdyhZ/xV3qSBQuofhMVnwpx4g9p5Vfqq20tXvKiqw63U6Umfn2YPszVYS2wlbasCiNCqps80q9\nV62qU2jLvopQxzpUdrdL/KSYegiq15TCRZVzi2pTqfWiElEKTd23LWUIIuL4pYNtScYaaevldl/5\nJXZITDPpOx2iALPjjOpnhuOn5VU9FESt0yeKSBVrSY2/uU6/DmVskHFWWoY8TtRrovTIaWWGHz+u\nLDHXssH+YKsI7RhZph3SvsVXiFqnuxr0E74NphJUqdcywTFK1CQS30pUzmvFT7T6uykkpmJax89U\nfboQVBFGnaQr6mStdFZtJN/t2dRdHgrYIG0tH6gHS3GuY68ppbYVE1P8BADk26L9ndRVXishZYyN\nWPPWJ1XKKY9B1VScnzCvyYQU59J2lUJRfRelct5QIue1Pd1ePlQ5F9qCSkjpU7kIBZ6vdBaFZUHl\nr7afAPw+k2lTY3Mojp+ywYqBlzPjmVq+QmzQfkJOMM2H/bUvmlLPs9Rr0t7bVJ/Oqlh4Zgw021f4\ncWuD9S3xdlvNGIQJvkL8hLxjtcapzQG/f+eCPlv8RKuKEZg1YqbavkKr5mPiW9qKVMBU4HnfK+Wg\nDfqdQ31vNdVrod0kygY1psi7nCiEbT9h2iBjlLQ7ra6Ni/dqKCEL+WhfIX5C92nVptqMOhTFuVZr\nK2scHRMz+H5q+wnAUAV2itJNlG9BpbYd3xLw1XPy/8F6y1dU5BT2XLCeOox+KOMcLF/h9wfJe9BP\nAL6vSMnuBa2qVe+MCYdNErIhsffee2PZsmX44Ac/iGeeeQZHHHEEnnnmGXz961+f0HxMSK/MZrO4\n/fbbMXfuXLzjHe/Atddei5GRcGBzQgghhBBCCCGEkOmEA28yuJFfZGqx9dZbY9myZdh3333hui6+\n8Y1v4OMf/ziqRpidZjMhCjwA2HjjjbF06VIccsghOOKII5DL5TB//ny0trbWvO/KK6/EW97ylgnK\nJZnpTKRCsKVd1FT+anm7pQ6BveInsS60QsdbASt0+xPernVCYs5SJWTVSriOuSGKBMN0UTq154PK\nj/xIUImQlth0hmqhW1aUbXWIQsdmkhhZ7WKDrwCDsqGiVvQl774iwVIRmulrG5Q6RKvXREWoFBGS\nP1mhNlR0XXolOSamjlbgFZQNvvJL8lhRK/lig6hHZBVZVs+dvLLbXM0XYZmopixVQr5VTpALt9dC\na1DhIgpEWVnXq8iilhP1Woc/1koeyyMqPoxSiUisNakrrYTMefab/UeL46zV4xbVxodbgyermoh9\nsmreYtWDPMW11GuSH8BXTZWHvOekc3KSolJEyCq+UkIi79ehaykJc5byq71VbFAxYNxgnwMMRVWL\nqNciYhsBxkq4oXxSbUNWw0v9SpVg9Tuph1ynp3iS9uj9Eq1KkLKU/PSr+pADdKsVU4Gn6rs12B+k\nPHRtiwIv5/dHX6WjVva7VFsRNY9lg7S/jFGH0GOUjCVeJkWhLAqDvjZR6Pj1IP1OYhrpehAlRtpS\nOliKBMBXR0hbkX6RstQxYkO+w29DWp1gx2eSWISqLrtU2a5R9VBu80+SrJSCJ3LKGCZ+oqDjKUb4\nrUww5qn4inx3eMz0Ppd4nuGThKVtahWhelw2wU8AYV8hCugObYOcfhpG+wpVD7lOr3wL3SpOVpyf\nMNTc4l/iFOfSH9stPwEk+4qQn6gRP04rnZWvEKV2VeXd9hOevZav0GNE0LfmMkElpKlek74r/U5s\nKKi8d6jv2k9EqGnktG9HnX4p+UnyE54N0ar58Bgb9HUthoowzleIn2hVNvhjbLx6zVHjfE6NN9pP\nzFY2DCp/YZ6kqlVTSlkqviImzqgZAy7JV4ifKGh1bXB8Aoy+IXWY88oy5Cd6lJpcqc5NO2SMEiWk\npKHj+IkSUpVTi2FDl8r7kFYRen8XXyF+QmwxlV++al6U58pXZIJKSMlfcaAcvB7Ge6bevaDsFJWz\nNcaKmrcla9rg5b1f+YpK2Q3YEOcnAP99PG1HG5O+3Rp8hx0RhXbEeGD7CvETdoxO89aCFc/Z9hVJ\nfgLwVYRpXQ+poA06RqfyeRF+wpG4rZ1B3wLGwCMkRE9PD+6880589rOfxSWXXILLLrtsQp8/YRN4\nTz75JA4//HA88cQTAIBisYhnn3028b7BwcHEawghhBBCCCGEEEImi0ZOeVKAN3XJZrP45S9/iW23\n3Rannnpq5GJ/s5iQCbw33ngDe+65J9asWQMAmDdvHnbccUf09PTolc84Ntpoo4nIIiENQ1RKsopv\nnr7a3RJUVqQtZYUjp222BleRtSIhAlkBz3V58TJk1VYUGoJ5Cq0obGTVsq01uGpZUuoyUVfkDQVe\np6wox516pZQQsgIsq9qF7vBkfFWdbier5Lku71qtIrRiAgH+yq1WVqj8dKqV13LRW3FNZ9VKu8QE\nilBCahusFX1RJMhKfK7dVK95dolSWmyQ+Ciy8urbIKuY/mqxWGOr12YpdUZJKR7SUSu86jk9cmKj\nnC6ZtsZSrZoKqioCNpS8vJdVfWelXLqU3Z2tyoZwDDzJmagSxAZpU0PDnkogSiwhq+CzlBpIKytC\njUlUFYWQDfkusUGdlNfiPU9OX9XqNdWHtBLSsgPwVQkSF0eUN+uNNmMjcYOkzrQNopryH6aeb8Tv\n62hTNnh5KytlS1rFq0rJ6Z8St6wjQkXoRKsSRPHUpZRQ69pLgesqhopN4vd1WAoXScOxVvNN9Zq0\nb+mzBaUKSWWdGBtUPRR8G0LKCvU4rV5TNqxq8eIlVY34fZW8Z4fEoOpQ/aE9ND4FlSHmuCg2iDqw\nZbb3nHQ22D60qtZQser4TLmgSkGeJ/1RFMItKgZUpeo/X+IzyTgrvkL8hD7RWDUm14w0pXyFVq+p\nvLXOGlb3WPG7xE90+vHrtK8IxS1TdlvKENtPAGFfIXbafkJnx1TxitK5RZ2sHecrVJuVOK+BemgN\n+gp9kqdlg4xLncYpvEm+olPiRlknSweQcd3yFUl+wrtG2S2+Qvq3brPeN62Y12Os329KI6od6JiP\n8v6RDdjdrk9cN8pffpD4cblgf0jyE4DvK7QSMhe0wY9Z61g2+GnE+QqxQVT/ohqTMda8R6u1sxIT\nWCmhEvyEaaethNRpajVn+BTWJF8hvq6rhopQ4gRClJC5oBJN/ERJ1IMF410ixldIW7KVkKIilTHe\ny5tn79rWYuBa8RXiJ7otXwf4bdOP06nGf0vNqdW1lp8wbWidZfls8RW2yll9LxjtIM5X2DaIn+hq\nCdsQOoU2G9zV4vcLT4Fn+wnvs6Cv0DsArNOATVV1XvtspZa1fIUf71WVU6vEe82E0gjFfM3K/xSi\nIvT+Tyj2G/H7RIGslL5SV7DbEPdy1oXT4IMnWOqTw80334xyuYz29vbEa08++WT8x3/8B+68804A\nQD4fvROikUzIBN7555+PNWvWoLu7G1deeSU+8IEPTMRjCSGEEEIIIYQQQghJZM899xzV9YcccggO\nOeSQJuUmzIRM4D300EMAgNNPP52Td4QQQgghhBBCCJkxOIhRa481PUrwSAQTMoFXLnuy7912220i\nHkfIlKBdBdqeZQQNt7d3+dsOrC1qLd5WItkWWxg25O7qXgnoLls2ZetESm1Hku24rmNvD/C3T/hb\nN9X2UxXwt6Sk+hIQeyMjaLhsfZHtLKEA62pLkaNsyHd730sDQ74NynCxIdemgpPLNmAJJq3Kw9wG\nImVWCG0/9fI4orb7ZIrKbnVDtxE8XLafyraDjO0gZUuBbAM2gtfnZ3UE0pXtDRm1rUjqTIKZh7ZF\nwd8yI9uKZOumbNkaaPdsGMoEbQD87Y6yza4tbhuwbP/JBbdjm3l0VX1rGyTAtrZBtp+KDeGQB7Kt\nqE1tJ+lR250G1Xar/ogwCRL4W7bZtVnbgLUpYkNetrD42/8KszqVoWp73YiXpmwlExvE7lTEIRY6\nWL9qAFKW0pbWD3u+a73Kl3mATIvKe49qu+3aBtnCpxKX7WkRh1hIHislFWC9JXiITK5bbPDsThWM\nQ5+scpXfWrPB7TjS7teJ7YYNedWHZ+ltRaoNqwrQWzerEpjcsMHe5t8zErBB+rhssdXbsXNmPURv\nK/K3FAW3Ka8xrtXByVUA9B5ra5SMD6FDLHK+DbJ1M6/yWBlSwcn1ts/gliKzD8kYbW8DdqzxScbL\njdQhE8YZIiEbxFd0WqEWonduqu12LaqNqLaUH1DhFqzxQPyE2AqYvkKNs9Y2YPETncoG208AYV+h\n66E1aIMdLsL7owpSL/5O5bHQ49mS5CcAY4uatQ1YxsNwmAK//uN8xez2oA0SpiAiooEeS6Rviq8Q\nPyGIDRlj+2mu0/IVYoOkKddZfqKn1X+nGFZbQ21fIf3eDtsROrAJ8b4iyU+Y14ivkK2TejuoXKe3\nn4oNfj3E+QrxEz0S6iNiG7BGtuxavkK2DMb5CQAo9HQE7E7F+DvZfho4PEG20Kq+a/uKWD9hPF93\nDR32Inh4kZRxS7EUsMG0w/YVOmSEtYXWDlNg2tCj3vPWqfIVG8RPyDZV8wAIvRXY3sqsfIVsi5e2\nXlDbpTPGFtZYX1HwfrfDRUgzaQ1sA472FfogDmVDjxVqwSyL0KFNymfH+Yl03tyGGvQVcq3YIIfK\n6S3GRtOyfYXksWIdJiJ+YqOOoJ8w05CDOPTBeFkZY1U/lXARPX5YHn14lrQlVVf6fYMzSKOmkUXG\n4m8+1157LYrFIo444ghks/Ghc5L48pe/jMHBQfzwhz9ES0tL8g3jYEKOltl+++0BAGvXrp2IxxFC\nCCGEEEIIIYQQEslnPvMZfPSjH8XAwEDk51tssQXa29vR29tbM51LLrkEP//5zzEyMlLzukYwIQq8\nT3/60/jJT36Ca665Bh/84Acn4pGETDqiuNio3V9p7taHJ8iKvq2a8j6XlS+t0BnxA9E7mWC3zepA\nx961esVPreLKKrKplCsoxZEEWN+oPRhwU5QJWVFgGKuVXS2WDaLSUc9xlTIkpVQVsjJfMBQJqax3\njVsV9ZpSVnTbNogyxCgnCSgtygpZ0ZdVS7XSPqQOgpCVUVk99n727mnNhssGMFaRRUVoKD4qw16Q\nZCclK+0qEHrOK5e8Uo/IiqcfXD284q6Dk6vVYVE6llSaQ6JuMRQH7XlRVgUDe2eCC+G+ikMCxHf6\n5V/okUD3oiKsBG1Qq8dpFbzVD0weVkKKek1UCbNUex9Rag1zpV/boPqBXNsSp6xwgsqQTJuvHtJK\noqqoQySYvlI4WGpO3R+AiEMsvO9SlrKyLf2ikA2qXk0betqCitSCHUxaH8Thr8alWr26yHf3A/Db\nUEUpLKRtiY0hNScANy0r+soGVXatWpHmfT5YVCrSdNiGlnyw70g9iA265sQG8wAKZYMoIUWtUVZq\nYbFBVFVSHzIueL9YKkJRVlhqCVFCmv1AK5Cl3SlFj4xpolrSytSIgzicNlUPKm8SrD/dEjw0SA4m\nyBnjgIzRWkWoKlwUEKKE7CgEVUQmpXJwnBVf0ZkPHibiH8RhlJdSr8X5ilQ++DzbTwAR6hCxTSvw\navsJIOwrxE7bT0SJpsRXaOWX2FCUQPe1/YRpQ0opjirWARCFGD8BACWlbBEbpH2JOkz6UNxhRwEb\n8qK8UTYk+AnPXlX+bZ0qDXXggj6II6iE9BXCfhpxvkL8ne0nolSE2lfIwQNymEiCnwB8vy6+AjLO\niord8hOtlp8A4n2FKNLt8SlwEIceZ4P1IL4i3xNUQtp+AojwFapNwVJqS9mZyi/Jmygh7YOYbD/R\npscn431AfhC/JAeBJfgJIOwrtHpKvX/Yh1joMTZr2lDbV7To/q/Ua8a9ib5C7yZRajrLT5g2hHyF\n1INWOXslJfVvthdRCc9WY5St3o3zE2Y6fv8O+mz7PTDKT4gNMs6KAlSrCC21edrQVfuHBQXfO9Iq\nTdtPiIq4M8KG0BBl+Yl8xP8U4iukT4hP93deTMhUwYzBAU+hnWn09/djYGBgQk+ZTWJCFHg77LAD\nLrjgAlxxxRX48Y9/PBGPJIQQQgghhBBCCGk+jn8SbUO+OIVHIpiQafXly5dj++23x8knn4wvfelL\nuOKKK/DhD38Y2223HVpbW2veu8suu6Cjo6PmNYRMRTZWcSp6jNXyNq2ssJRfeolX1GtqRVippgoq\njiQAZNSqtKxkZlrUsfVdXd691oqfxMIIxtxQMfDUqvxspfySVUutqlArsZ2GekRWX0XpE1IRKmWQ\nrDymO7qUDRV9jazg2zbo1WOljHEywbhGgL9ymdfKiqB6RRhRZS2rqma8kE610tlqqQj9hwRtSLV3\n64/yRaUGUveKDSml8JAVzlSrUq+pVcyqsRIuJZZVeRMbutXqsKwaD1o2AH7slu6CqAjToWu8DGQD\nzxfFFADkRVmh7pUVZfndV0upelCr6eZqvlS7LIJLWYrKtNLp2SBqKhPfhuh60E1KFBDq+an2Lt+G\nWZ4NjqgFLBt0/D5lt2Mov1xR9qnf0ylLHaLae1WttrUNe3+vGOq1VqvO2i2Fi6StY2QFFHgqXll3\nR8DgqmpbEsdKVvGl/WlliFE2+ldVdjpumSrbkQ6lblH91bRBVAlaWaFsELWMfnEURVDGV2BJH811\nrg/kozwkCjxlQ1uw7kwbtIpQkdaKI9UvJH8dooT0bRb1lNigFS7ZoHrNfvd10/44IWOlqFPt8Uhs\nkN8zhopVxmhYMfDsGJ2ikpitbMgag43YkNXx44IqnVatXrP8BAyls1JvZVQ9iK+w/URWxV3MGO9T\novAJ+QptQ9BPSP5MJWScrxCFi+0nTDW1a6mEbV8R6ye6O30bVDsUpYlWnKvPk/yEaYPtK0S111pD\nReiPUUE1Xb6ntp8AgEKP8tkyzlqqeX+MDcbAmxWhhLR9Rd1+AvDVa0ql46i+mi96KsKQnzBskDht\nYkNKqwit+HHqsdKnu41YgLavkDy26NhrwfoI+WvAjzUqsSlFXZvgJwDDV8gYpWxwneAYq1XOhk+T\nvElfbssHfYXtJ6Q/ZCNskL6RVs9P8hOmHRILUvv5XHQMvIzl64BkX2ErotvMGHixvkKNFapdSFsX\nskVfER/rK/Lh9w7AUDnnjDh+qm2MqHFWdmhULAWe7SeAKF+hVLOyI0aVaZyfiLIh09Wt7FdKbfET\nTnCMBfy68N/Hg74izk90FMI22IpzrRBW/ULG/5aqH8c0M6BUi/L+1xncCWMr5Qkhk8+ETOAdd9xx\nWL58uf592bJlWLZsWV33Pvjgg9h1112blTVCCCGEEEIIIYSQMeMgZrFnrOlRgEci4MZ2MuNxJ2n0\n27gzeOoYYMY2klMOg/fo1bJWUaJ5K5GmTiUzPBS4R2I66VVLUVXoFT/1LGPrvii/JD9lWVFUGSpL\nvB4nGN8M8Ff9CrY6RBDll1rxExvMwSZd6I+0QVbNHbVq62aCqgogrPySVflixVWfO5E2BOKliDpE\nq6aiY6+JmsA1lF8ZNxiPya16K/qy4izlL6oprfgxnpGylBW+ek0pDNR1I8YqqSDl7itcJAaeXQ+i\nDFEn6xk2pCveCn5aTiq2bRDloVKTIBu9Em7a0K6UB8NllYalBImyoVPVndwraYkp0ndTSvml8wMg\nXfLUIQWJMViW+HHKbl0Pql8YMfBsO6TsdNwyVbaqSWkliEk+JXFrlA2iXktbK+yiPDKVX9JHxQZV\n7rYNEj9IbDDjx1VtdYgTVHNKG5eYHe3aJkOBp2wQRYX0JekqvhJSpWWozaSPOp09AICc+izbHgze\nK7Eg01IPphLSjguk1SFeWh0Fr12KdtdUvogdMmaJ3dKW8hIDzzqVL2CDtJEOZYMaZ0I2aAXoLOPe\n9mB6op5JWQph1aYq1mmgQFhZIddK3Ym6JUoAIWOj7t+Wr0jyE969bYG0bPVanJ/IGTHA4nyF9G35\nPeQnAF/prPp1amjQy7t8nOAngHhfIc+L8xNAsq+w/URojAW0v9MqQinfam0/ARjtry2ohNRjt7pO\nK7VVWQ4bCqg4X9FmjWXtqk+ZNqTsk7ItX5HkJwI2qHvcjPKZVvw4OcFVVFNdhWDfB8K+wvYTvnot\nHANPqzmVr0iLal4pUgtpiaeobDDV5GKDqLVzlg3qurRuS/693Vaeh42dBkC8nzCVkPap667yt0l+\nImBHjK+opoL5077O6MO2r7D9XchPGO+DUia+vxPll9rVIW277I2puh5KZgw8lZ5S3EkfEgViNXRa\nufe91VDRxfmKJD8B+GrIkK+w3+kS/ARg+Iq2oL8Lqc2N+hdfIe1cFH4yDtk2tFlxPQHfV+jGqt87\ngipCV/lrUwmeafV8hX53UtfImFaRdulEOCJCyKQwIRN4V155JQYHB8d073bbbdfg3IyNN954Ay++\n+CLy+Tzmz5+PNiOY+lR7zkTllRBCCCGEEEIIIY09eGIiJSjr1q3DypUrAQDz5s1Dd3f3BD6djIYJ\nmcDbdtttJ+IxTeGWW27BGWecgQcffFD/LZfL4aCDDsL3vvc9bLXVVlPmOROVV1Ifs1UMIlNxoVU6\nqaA6RC8ji3otp1bCOzzFh7ni7bZYq36ywtYmq8hq1TIt8eOUisNwAzkdY8obAiQsVk4UeLLiF6Fe\nk5VLWYQNHTIqchFZRVareebpuW7Bin2ZDqoYxJaqUlW4Rt4lxpgdP07ilckpcKK0sGNSmfeISsc+\n/VRUNXol3FCt6GtklV4p8iQ+mJzkp+tBlCHGSrhj26BWtkVDIfkpG/HKhJyl1mmxTlCTehb1gCgi\nIm2QkzSV0sKxYjA6WlURVIaYNtjqtc6Caju6HsLqNdsGuTcboyKsivLGVBGq79WhQqQNEj/HVoYA\nhloDQdVUi7USLiqeKBu08lBd25YLKlxC6rWMocATO1SbrYqKUGywTq71bfBj7rhW7DXJj7Tpznww\nbtewOunRbFLSV1ot5Zcen0Td4ooiwbBBVFAVTw1SlbxVfHWIaYMfm8no+9bpgiklE5axxTxlDwj2\nYbFDtz9LeSjtz29SIrMw4vipPgpV7jIOhWxQp2GL2sv7m3UKrZikHpgT9Zql0Mmb6rWYcVbGp5yl\nIgzspRFfUVBq7Q7VdkSlY/kJbYMRgzCkcLF8hahJ4/xELRvEbqmyiMOo/THR9hVqfEjyE0CEr7Di\nW8b5CSDZV0hbytsnGgdssFRTSqWjlUgjnrrF9hOmHboe5IRkR8Z0ywZRQkbYYPsKGWNlHMhn4m0Q\nlY5WTVm+Is5PeDYoX6FUlLavsP2EjJNlQzDo+4qgijDOT5gqQr97R/uKlFLcufloPwHE+4rQGBul\n5k/wFXF+IkpFqNtMxqqHGD/h3VvbV2i1uaX6b8n6z++sBn2FuePCvEfHajPGZbFD7pX+F7JB1YP4\nCVHkRdmgd5GoPmXHGU2nZXzy/xbnK5L8BOC3P79KxFeIeq0+PwFE+Iq8ulfeA7RC2L8nyVfYY6y2\nwVTg2b5CSItSO9pPAIavkJOj7fKP2HlB4nEw/bbQPvDAAzj11FPxpz/9CVWl5k6lUnj3u9+Nc889\nl6HMpiDUw9bgnHPOwUEHHYQHH3wQ+XweixYtwtZbb41isYjrr78eixYtqjuWX7OfM1F5JYQQQv5/\n9t48zpaqOv9+ztzznZBJBBkDiKIiCCKiIgoyKAp4VTQQQBQj0ZCY/FASR4ageZ2johhFVAyCyCgi\nRITI4AQoAso8ynBv9709nqneP2qtXXvvqjp1+vQ5Pdz7fD8fOH37VO3aq/awqms9e21CCCGEELJ0\n+f73v499990X1113HQDgBS94AXbddVcAwM9//nPss88++J//+Z+FrCJJYNHmwDv88MNRq9Vw1VVX\nLcj1r776apx22mkAgNe//vU4//zzsemmmwIIN+F461vfiscffxyHH3447r333o5lpt24znzVlcyO\nTWSnqLKVa0Ij+eWCH7V0d4xqljWaKt+XI+VLYO1IC0TKNs3b0vSjlpqbyTpHlU79ZVW+uDlm6qpI\nkKqXrQicRvI1L4qf28jk+pD65AOJzJct9VDVU4fk3QhsFPkTGxKUXyU5p18Pybk5pmylRZoNqjCJ\n5cBTVYW0Q84qK68KlywbVGmhEedcgg1y/b5A1UJujsR6Qg48zYOix6iaMq6EdBV4ub5IPVTQfjYT\n7tCn+ZpMX9JIv6qlivFd+fSOafS63Aw/l/W1b4PmfjHqEFVNGRukcVU1VbHUQ6p0U3VIXSPLng2a\nv89S4MHLR6P3Tu+lRsLLs7Gh4PYpk5tJrMlZOfD0vuaXuXkKA+86Gs03OQhL6TZoH9brD0t0Xvt9\ntenmZgKie6i5powN2pc9ZQhsG4xqSlTC2u9TbFDlkVEZIV3honNLs6S/z8ds0Mto/1MbVJWhc1zB\ndCb5wbpm06im5AhV6aTOsZHyS+fowCi/XGWFzvGDMqcWReVmq4x0njVjqOD1JU/dYpPlK7L8hG2/\nUU15vkKVIWl+wrbB3wVZbYj7iWguzfIVsTk2wQYzrr1co5FSO9lPhHVt7Ssy/QSsPqzKr37J55fh\nJ4AEf1fwVISen6jIHLvC2lFdVVH+HKU26JxWSfMTgKUiFF8h9yPLTwDZvsL3ExXPTwDpvsL3db6f\ncG3wfIXUp7BM1MOSR873E44Nvq+QMn1FaiWw1GsZviLNTxQTFHjmnhXcexrzE/bY1rk6zVdk+Akg\n21ek+Qkg21e06yeSbZB5IMNPAK19RZINtnpPfUXUJOorylJ2e34CiPuKprEhuR2AbF+R6ieKdjt4\nNpgceL6fCMts9TeFyZ+ovtrLo0gyyOVMftWuFNfDRbR33XUXjjvuONTrdey+++740Y9+hO233x4A\ncO+99+Ktb30r/vCHP+Dd7343XvSiFy2atGa94sMf/jDK1thQpqamWn7vHzcfzMuobDQaJjlqOzz2\n2GO4+eabTS63rbbaqoe1S+bDH/4wAOB5z3sefvSjH2FoaMh8t/fee+PCCy/Efvvth6effhpnn302\nzjzzzAW7znzVlRBCCCGEEEIIIS5LaQntRz7yEczMzGBwcBA/+clPsPXWW5vvdtppJ/zkJz/Bbrvt\nhsnJSXz0ox/d4JV455577py+n0/m5QXey1/+cvzmN7/p6NyZmfguP73mtttuw5133gkAOOWUU5wX\nYsorX/lKvOpVr8INN9yAb3/72zjjjDNm/ca9G9eZr7qS2TPs7awJRJFDk6/Cj4araktznqhqzFKt\n5HwljUQcmxql1MhnijIEsKOvGvGT3ceaUhbcF+52tFAjfUWjSvBs0EijRLGbGpFtWDZUZmeD7cH8\n/HGBqhnlMhWNOLawQduhlKJKMHmtrHxF0cml1jZom7WyQYsyecu0jcLoqEbCmwlZDtSOohdJN/Zp\nNN/YoJFfa/dP3cGy7OWYyrLBUjGqKkGDwLOxIco9F/5bo/hqk8njl4v3f2NDwW2HXOBG9JvaD+U+\nBLbyS8vVQLtG56VCBbj5FBvBLGzQnXTNgapuaWFDSZWerg0mf5GoSgI7b42/C632f93hGppPLhAb\n4nO+b4PpU340P+/mZnJtUMVjaxuMAsFW8cXyx4WfOi41N1I7NpjUm2aOzTu/R0IeP62r9vecqlWz\nbLDtiO0uCLm+N8eKDapAamVDmp+wdwHM+b7CTIyiBMvyEwk2+L4iy08A6fOs7yeiOdayP8NXZPoJ\nywZ/ns3yE0C2r8jyE4DVh1VFpzZk+Akg21f4fkKVaAVLvZQ2z2b6CesCcX/nrghI8xMtbfDyjKb5\niXZsyHvn2rlSs3yF8XUpfgJI9xX+HOv7CSDbV2T5CSDbV2T5CSDdV6T6CcuGLF+R5idCezJ8hcxP\nWX7CtUHqbvL4tfYTQLavSPMTQLavaNdP2DbE/J3xE/Fn/3Z9RZqfsMvwfUWu4I5t9RM5a0VC7Nmp\n6Konoz7Evxs3JJ555hlcdtllAIBjjjnGeXmnbLvttli9ejXOO+88/PjHP8batWuxYsWK+a4qSWBR\n58A7+uijjZRzPrn88svNz4cddljqcYceeigA4IknnujoBWU3rjNfdSWEEEIIIYQQQkgyuS7/1wuu\nuuoqNBphYKOd9wf1eh0//elPe1SbheX+++/H008/3bX/li2LbxrYbeZFgXfaaafhqaeeannMzMwM\n7rvvPlx00UV44oknsHr1anzve9+bj+rFuP322wEAw8PDLdd777nnns45s92lpRvXma+6ktmju97Z\nu0KZHdnk1bmfcyPQnao0SqlRTEtNEfhRP42Gm51si05ZJuJn70KrgU5VeogAIZBIrB9XtN/0ay4R\nDRr6KsKu2qA7muXsiLcqCnTnwHzXbdA8WQES8sjornaztSFBvaY2aI6LkkZvU2yw7fBtyPs2mEh4\nb2zQ7lQyYrbZ26Dn6Dgo+oFe3wb7+lJ3k57Bt8Xsvidl2FF872bpdfWeqjpCc2N1YoO/G7C9+6nJ\nKdOstLZB20Pvqa2m8HfmU2WFKl5KndugZUXqFs1rlGRDuXc2mPmpcxsKMRssFaH+bpY2OHbk3U7r\n58DTPqVFNzy1l43WNctPAAnzrN5LVbxkjW0g01dk+QkgfZ6NzU8Jf4Vk+YpObNB2zvITth3t2hDz\nE0C6r8iywbYjZZ7N8hOd2BDzE9YvY77CzE+d25DlJ9qxQc8x48G2IctXqJ9IyAEaXSjFV+iF/Lnd\nVvNn+IpMX4cWvqJdP2HVPTbPZsyxc7HBLi/VVxgb6r21IcNXtGNDqq9o10+0ssHbydXZhbZNX+Hb\nYE+DqX9TZNmQZIfW3R8HpC1yOXeO6EZ5vUDfHwBo+U7Af3+wevXq3lRoAVm5cuVCV2HWzMsLvLe8\n5S1tH3vGGWfgzW9+M37wgx9gn332wSmnnNLDmiXz5z//GQCwzTbbtDzO/l7Pme/rzFddCSGEEEII\nIYQQsnTRdwF9fX3YbLPNUo/bcsstUSqVUKvV+P5gEbHotpYZGhrC17/+dWy//fY444wz8P73vx+F\nQiH7xC4yOjoKAJnrvO3v9Zz5vs581HXt2rWp33ViNyGEEEIIIYQQsqGQQ3dVc1pUo9Fo+fc4kP0u\nwKbd9wf5fB4jIyN49tln+Tf/ImLRvcADgO222w7Pf/7z8eCDD+Khhx7CdtttN6/Xn5iYABC+lW5F\nf39/7Jz5vs581HU+paXBBrS5xlCpfdm5itmjxLel1GO7QdEsFfS/6fz+bwg2KE1d9pHv7RSpSzYK\nscssIRty82GD/iZ9+/a5YGzQPOjmm7nbEGhC+IQNILqJLtnIL2Ub9LOXNtjrIHswR0VLF8PPXsyx\nQNI82/oZoBN66SeA+fEV6TYAS2WeTfcTwJKxIdVPAEvFV/h+Alh686zvJ4AlaIN+9toGs7a2d34C\n6K2vmK/ncdJ7br/99sy/x52l0Rm0+/4AiN4hdPKug/SGRfkCDwAGBsJdrxZiF9pSKZzoNLljGvV6\nPXbOfF9nvupKCCGEEEIIIYSQZHKzeJG2ULT7/gCI3iFsiO8PxsbGEAQBli1bhlyXRUS9LHtRvsAL\nggAPPvggKpVK4rbGvWZoaAjPPPNM5pvmyclJ55yFuM581HXNmjWp342OjsYUknOZuHqhwFuoiTQ/\nPSY/2In/k5Mka/S4oQl4JadsQ6retGyYbZJkjRrbW8XnGnX5nUzckmAYWUncLXv8BMO9sEEDoEWr\nX+Tk7F7YoP9Wd9bwbLDt6IoNklg7p3XPssG2IyVJsiZz770NgVP3jmxI2XjFt6EpRdWtokzxKQnQ\n0zYEcOzS6pi6S3uYPtWYgw26mUd4XN3q/9omzQwb0hLRA3bibbh1jo2LDmzQRPQmQX9vbTCqEG2H\nHtpgJwava/GztMG2Q/N7F3wbmjrGa27d55BMv2kppPx5VsdGuxsChPaEn2m+InOOte1SMhLRt7LB\n9xUd2aDjPstP2HbM0gZ7I5a0eTbLBtuOtHk20090YoO9eUSGr+iODRl+oh0bNLm/GQ/pNvi+ohub\nx6T5CaCNOSpzk7F0X9HuHAtkb7yS5ifmYgOQ7Su6aUOan+iVDdpn2vUTrWxI9RNA+77Ct8F6ps36\nmyLNhiQ7dDzoOPDvfzt/U21Iq6lmTRAkP6t0XF5Y1u67747rr7++a8Xqu4B2VHX6DqGTdx2Lnec/\n//kYHR3F2rVrsXz58q6Wvc0222BsbAyjo6Nd35l2Ub7A+9a3voXJyUkce+yxGBwcnPfrb7nllnjw\nwQfx+OOPtzzO/n6LLbZYkOvMR11ns6aeEEIIIYQQQgghc6dQKHT17/Ett9wSQCjSmZmZQaVSSTxu\nYmIC69atA9DZu47FTrkcLvfvxYrPqakpAO0tU54t8/ICb2xsDLVareUxQRDg4YcfxhVXXIGzzjoL\ne+21F04//XQ888wziccvX74cxWJvqr/LLrvg//7v//D4449jYmIi9SXivffea37eddddF+Q681XX\ndsgFwZKQDc8X+alQgRfYuSc0SlYKJ8qgKINaglUaJatJmKwmoXBH+eLd47zmx5EoWUkicIH+XkUd\ndtS4Pi2fVSm06h7jRY8CW0VYkEm+WJLriA0S8eumDaVAopXF6PiiqhEa4WSbq810YIO0Q7Hi2iAy\nDo0az6gNdUu1o5HlFBs00lqSCGRZjMpZNpSMwkXqXJt2bdDIckIUz9ghNuQKofMJSn3yvShdAteG\neiNuQ73Zng0lCSPnrCm3qMqKDmwwCiNNiCQ2wNhQEBvC61al7tUEG1S90/QUFkWRTxRNO0QRYf1Z\nf2XUIZ4NaNTk+wTVVIYNyIV9qy7Xr1lFVKWD1TJs0Gh9SW2xbKh4Y8ao1szY1nExCxu8+Uk7QiPB\nBjO+pfKNDBuKXl+ybTBDM2aDPDfoWG9lgxkX2g5ig1y3kU9qh85ssO3QOcrkxVI1hdqg85OoK3KN\nhGch34YMPwHE59kZqbwO6aw5Fsj2FZl+AkifZ30/kRvQiqTa4PuKTmzIFcJzsvyEY0eqDa39BJDu\nK7JsALJ9RZafCL9Lnmez/ER4TGtfoe0yGxt8X5HlJ9qyQT5zMg5cG1r7iiw/AaT7ishPeCpCtcG2\nI22ezfITQKqvaNdPhHVM9hVpfiJfm+rchrz1TJvhK9SGmCKvhQ3GVxRdG9L8RPhzhq9I8xO2DSm+\nol0/kWiD7yf0GaoZ9fW2fUWan7Bs8H1Flg1JdmgfqogEr09VgxuxqG52BM6Kp7nSq7+rd9llFwDh\n+5c///nP2G233RKPm4/3BwvJypUr8dRTT+Guu+5quRvvbLnnnntQrVYxODiY+nJ0LszLC7wDDjgA\nv/nNb2Z1zq233ortt98+9fvbbrsNL3vZy+ZatURe+cpX4pvf/CYajQZuuukmvP71r0887oYbbgAQ\n7tCyzz77LMh15quuhBBCCCGEEEIISaDbS2hbLP+fC6985SvNzzfccEPqCzx9f+Cfs6Gw66674u67\n78Ypp5yCj370o9h+++2NQCyXy6FcLuO5z30uRkZGnPNqtRoeeeQRjI+Po2m9kK9Wq3jggQdwzjnn\nAEDqfZ0ri3IJ7UJz2GGHoVQqoVar4b//+78TX4pNT0/jwgsvBBB26E033XRBrjNfdSUdMDEKAMiX\nokhfUOp3j/F2htJI67SEx2Ykmj9tJf+qN13HUJTIp0bLmoXw+3xO/x0el3cUeBINr07IZ5jfIKiF\nvw900xMpO1e2ogfGhoFEGzRar4qQabGhaqnYql50VHNr9ImyoSI2aBKOsi09kfKNDRJJ1s9gRj69\n+5RogzpZk4NGIuHya1VTTFn3X9sky4a6KEE0calGYJNsgNRdo+JBNYzIBtbmM8YOdSzl8P43y/2O\nDaoWqTfD66mawu5DM+Z3oQ16q4pGeaB9SussikgrBJvLacKkWsc25Mt9YsOgY0NObGhI59Xo8XQj\noR00wi/HxGwouDYAllqhoHIlz4aq2jDZvg3Sp3JGrRCOe1W3VK26T6myQrqQaQcv0lqW+6Ht0B9E\nNhRLbjhc8+eY8SD3H6Y94ssDcjK+85Ww7mZ+0pxToqJqSj1qlmJT+1OsL6XYUBYb+qyxXJDcQyVV\nfgWq/BIVxcxkug2qdNN2UBuKbqRTcwOp2shWneocpTbMiE1pc2yflUjRqEJMHiLNDyQ2iJrCzLFq\nQy1SHmm/MjaUVCWV7CdspUSWr8jyE4DlKzRvkvw+bxR4rf2EbQNMX1KVlI6ZfscGBJE/zPIV/hyb\nZEMj7/o7o/DM8BNAG74iw08Aka/QvqPtkOUngMhXqI/O5VR5l2KD3H9beZQ2z6oNOpc2K6qAtJTo\nnq9Qf+ePizQ/AWT7iiw/0dIG39fp3GLnYpV+FfMV3vyU5iccGzxfEfkJJNoAZPuKLD8BpPuKND9h\nq+lVAZnmK9L8BGz1WoavSPUToSFiQ7KvMHNrhp9IsqFpdl9u7SeADF+RZIPnJ4B0X9GunwDiviLm\nJwLXTwDZviLVT8i4cPB8hSofq013jp1p8TfFQMnIBAEAJfHTRbcrkSXOK17xCmy++eZ48skn8e1v\nfxsnn3xy4nHf/va3AQBbbbUV9tprr/ms4rxw5JFH4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wmpEf36eteG2mRcgRepRVTNOSFl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X7QOQrkqIRS+9PCr2haLcO+G/21GHVIxqUqP0ydFhowMzkfDZqNcqzjmlhORXJudNzutLaq8c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          }
        }
      ],
      "source": [
        "fig, ax = plt.subplots(1, 1, figsize=(8, 3))\n",
        "sim_data.plot_field(\"field\", \"Ez\", f=freq, ax=ax)\n",
        "plt.show()"
      ],
      "id": "7f8f843c"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  }
}