{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Optimization of an S-bend with CMA-ES\n",
        "\n",
        "Note: the cost of running the entire notebook is larger than 1\n",
        "FlexCredit.\n",
        "\n",
        "[Covariance Matrix Adaptation Evolution Strategy\n",
        "(CMA-ES)](https://en.wikipedia.org/wiki/CMA-ES) is a robust evolutionary\n",
        "algorithm used for solving optimization problems in continuous domains.\n",
        "It stands out for its ability to adaptively adjust the covariance\n",
        "matrix, which guides the generation of new solution candidates, enabling\n",
        "it to efficiently navigate complex, multimodal landscapes. Its\n",
        "self-adaptive mechanism and population-based approach make it a powerful\n",
        "tool for a wide range of applications.\n",
        "\n",
        "The steps in a CMA-ES are typically as follows:\n",
        "\n",
        "1.  **Initialization**: Generate an initial population of candidate\n",
        "    solutions randomly within the search space.\n",
        "\n",
        "2.  **Initialize the parameters**: mean of the search distribution,\n",
        "    covariance matrix, step size, etc.\n",
        "\n",
        "3.  **Evaluation**: Assess each candidate in the current population\n",
        "    using the objective function to determine its fitness.\n",
        "\n",
        "4.  **Selection**: Select a subset of the best-performing candidates\n",
        "    based on their fitness scores.\n",
        "\n",
        "5.  **Update Mean**: Calculate the new mean of the selected candidates,\n",
        "    which will be used to sample the next generation.\n",
        "\n",
        "6.  **Adapt Covariance Matrix**: Update the covariance matrix based on\n",
        "    the distribution of the selected candidates. This step is crucial as\n",
        "    it adapts the sampling strategy to the shape of the objective\n",
        "    function’s landscape.\n",
        "\n",
        "7.  **Adapt Step Size**: Adjust the step size, which controls the global\n",
        "    scale of the search. This is typically done using a mechanism like\n",
        "    path length control, which helps in maintaining an appropriate\n",
        "    exploration-exploitation balance.\n",
        "\n",
        "8.  **Generate New Population**: Sample a new population of candidates\n",
        "    based on the updated mean, covariance matrix, and step size.\n",
        "\n",
        "9.  **Termination Check**: Check for termination criteria (e.g., maximum\n",
        "    number of iterations, satisfactory solution quality, or stagnation\n",
        "    in improvement).\n",
        "\n",
        "10. **Repeat**: If termination criteria are not met, return to step 2\n",
        "    with the new population and continue the process.\n",
        "\n",
        "11. **Output**: Once termination criteria are met, output the best\n",
        "    solution found or the final population depending on the specific\n",
        "    requirements of the problem.\n",
        "\n",
        "CMA-ES can be effectively applied to the design optimization of photonic\n",
        "components.\n",
        "`Yuto Miyatake, Kasidit Toprasertpong, Shinichi Takagi, and Mitsuru Takenaka, \"Design of compact and low-loss S-bends by CMA-ES,\" Opt. Express 31, 43850-43863 (2023)`\n",
        "[DOI:10.1364/OE.504866](https://doi.org/10.1364/OE.504866) demonstrates\n",
        "the optimization of a compact, low-loss waveguide S-bend using CMA-ES.\n",
        "In this notebook, we follow this work and optimize a silicon waveguide\n",
        "S-bend. We use the CMA-ES optimizer from the open-source package\n",
        "[pycma](https://github.com/CMA-ES/pycma) so we can set up the\n",
        "optimization very quickly.\n",
        "\n",
        "<img src=\"img/optimized_sbend.png\" width=\"450\" alt=\"Schematic of the S-bend\">\n",
        "\n",
        "`Tidy3D` is a powerful tool for photonic design optimization due to its\n",
        "fast speed and high throughput. Besides CMA-ES, we have demonstrated\n",
        "particle swarm optimizations of a [polarization beam\n",
        "splitter](https://www.flexcompute.com/tidy3d/examples/notebooks/ParticleSwarmOptimizedPBS/)\n",
        "and a [bullseye\n",
        "cavity](https://www.flexcompute.com/tidy3d/examples/notebooks/BullseyeCavityPSO/),\n",
        "genetic algorithm optimization of an [on-chip\n",
        "reflector](https://www.flexcompute.com/tidy3d/examples/notebooks/GeneticAlgorithmReflector/),\n",
        "and direct binary search optimization of an [optical\n",
        "switch](https://www.flexcompute.com/tidy3d/examples/notebooks/OpticalSwitchDBS/).\n",
        "Furthermore, we also have a growing list of gradient-based adjoint\n",
        "optimization examples including\n",
        "\n",
        "1.  [Mode\n",
        "    converter](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd3InverseDesign/),\n",
        "\n",
        "2.  [Waveguide\n",
        "    taper](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd5BoundaryGradients/),\n",
        "\n",
        "3.  [Metalens](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd7Metalens/),\n",
        "\n",
        "4.  [Waveguide\n",
        "    bend](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd8WaveguideBend/),\n",
        "\n",
        "5.  [Multiplexer](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd9WDM/),\n",
        "\n",
        "6.  [Y-branch](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd10YBranchLevelSet/),\n",
        "\n",
        "7.  [Light\n",
        "    extractor](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd12LightExtractor/)."
      ],
      "id": "6d6164f9-9b55-4b1e-9ae6-074602e9f49b"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import math\n",
        "\n",
        "# uncomment the following line to install cma if it's not installed in your environment already\n",
        "# pip install cma\n",
        "import cma\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tidy3d as td\n",
        "import tidy3d.web as web"
      ],
      "id": "f206a03e"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Simulation Setup\n",
        "\n",
        "The simulation wavelength range is 1500 nm to 1600 nm."
      ],
      "id": "3961c1f8-5069-40b1-aa74-a93a93d2f5a2"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "lda0 = 1.55  # central wavelength\n",
        "freq0 = td.C_0 / lda0  # central frequency\n",
        "\n",
        "n_freqs = 11\n",
        "ldas = np.linspace(1.5, 1.6, n_freqs)  # wavelength range\n",
        "freqs = td.C_0 / ldas  # frequency range\n",
        "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))  # width of the source frequency range"
      ],
      "id": "2e8f3627"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "For simplicity, we will use the silicon and oxide media directly from\n",
        "the [material\n",
        "library](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/material_library.html#)."
      ],
      "id": "0799f038-06b6-48c9-950a-4441e6d6603a"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "si = td.material_library[\"cSi\"][\"Palik_LowLoss\"]\n",
        "\n",
        "sio2 = td.material_library[\"SiO2\"][\"Palik_LowLoss\"]"
      ],
      "id": "da489429"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The input and output straight waveguides have a 430 nm width and 220 nm\n",
        "thickness. The S-bend has a size of 3.5 µm and its boundaries are\n",
        "characterized by a Bezier curve with 7 control points. Therefore, the 14\n",
        "parameters corresponding to the $xy$ coordinates of the control points\n",
        "tune the shape and width of the bend. The first and last points are\n",
        "fixed to ensure a smooth connection to the straight waveguides. The y\n",
        "coordinate of the middle control point is also fixed. Therefore, we have\n",
        "a total of 9 tunable parameters that can be optimized."
      ],
      "id": "ebeb9a36-e348-46b5-9938-122322057cbd"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "s = 3.5  # s-bend size\n",
        "w = 0.43  # straight waveguide width\n",
        "t = 0.22  # waveguide thickness\n",
        "buffer = 1.5  # buffer spacing\n",
        "inf_eff = 1e2  # effective infinity\n",
        "\n",
        "# initial values of the tunable parameters corresponding to the coordinates of the control points\n",
        "x1 = 0.7675\n",
        "x2 = 0.215\n",
        "x3 = 0.215\n",
        "x4 = 0.215\n",
        "x5 = -0.9825\n",
        "y1 = 1.535\n",
        "y2 = 0.7675\n",
        "y4 = -0.9825\n",
        "y5 = -1.965\n",
        "\n",
        "# initial parameter vector\n",
        "x0 = [x1, x2, x3, x4, x5, y1, y2, y4, y5]"
      ],
      "id": "e6744361"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We define a function `bezier_curve` that takes the coordinates of the\n",
        "control points and returns the Bezier curve. To test it, we generate the\n",
        "Bezier curve that describes the bottom curve of the S-bend with the\n",
        "initial control points defined earlier and plot them."
      ],
      "id": "1ccdd0f7-519d-4cfb-9d43-e5d7b0785df7"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
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/SB+vO6hdx3OrHBvo661h3Vroup6tdXHHpvLx9mqgLgFUF4ERAAAAAKBWDKNs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\nr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          }
        }
      ],
      "source": [
        "def bezier_curve(control_points, num_points=100):\n",
        "    t = np.linspace(0, 1, num_points)\n",
        "    curve = np.zeros((num_points, 2))\n",
        "    n = len(control_points) - 1  # degree of the Bézier curve\n",
        "\n",
        "    for i in range(n + 1):\n",
        "        binomial_coeff = math.factorial(n) / (math.factorial(i) * math.factorial(n - i))\n",
        "        term = binomial_coeff * (1 - t) ** (n - i) * t**i\n",
        "        curve += np.outer(term, control_points[i])\n",
        "\n",
        "    return curve\n",
        "\n",
        "\n",
        "# define 7 control points\n",
        "control_points = np.array(\n",
        "    [\n",
        "        [-s / 2, -s / 2 - w / 2],\n",
        "        [x5, y5],\n",
        "        [x4, y4],\n",
        "        [x3, 0],\n",
        "        [x2, y2],\n",
        "        [x1, y1],\n",
        "        [s / 2, s / 2 - w / 2],\n",
        "    ]\n",
        ")\n",
        "\n",
        "# generate Bézier curve\n",
        "curve_bottom = bezier_curve(control_points)\n",
        "\n",
        "plt.plot(curve_bottom[:, 0], curve_bottom[:, 1], label=\"Bezier Curve\")\n",
        "plt.scatter(control_points[:, 0], control_points[:, 1], color=\"red\", label=\"Control Points\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "id": "5a07ae68"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The top curve of the S-bend is simply a 180 degree rotation of the\n",
        "bottom curve. With both curves, the bend can be simply defined as a\n",
        "[PolySlab](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PolySlab.html).\n",
        "The input and output straight waveguides can be defined using Box."
      ],
      "id": "c313d574-0fbf-4a3f-9f2a-caae75fd04dc"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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shS6HnAlxcricksshR5zz99tOWZV++XPLavYNxBDKUQ2YNGmSJk6cKElKSkpS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crP8YhQM1e5ejdF89ZAvGap7gsvvJDJkyeTn58fbjBJklRnLEdqdB577DHGjx9PNBo9pJ86lxYh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\nTkSuQmCC\n"
          }
        }
      ],
      "source": [
        "vertices = np.vstack((curve_bottom, -curve_bottom))\n",
        "\n",
        "sbend = td.Structure(\n",
        "    geometry=td.PolySlab(vertices=vertices, axis=2, slab_bounds=(-t / 2, t / 2)), medium=si\n",
        ")\n",
        "\n",
        "waveguide_in = td.Structure(\n",
        "    geometry=td.Box.from_bounds(\n",
        "        rmin=(-inf_eff, -s / 2 - w / 2, -t / 2), rmax=(-s / 2, -s / 2 + w / 2, t / 2)\n",
        "    ),\n",
        "    medium=si,\n",
        ")\n",
        "\n",
        "waveguide_out = td.Structure(\n",
        "    geometry=td.Box.from_bounds(\n",
        "        rmin=(s / 2, s / 2 - w / 2, -t / 2), rmax=(inf_eff, s / 2 + w / 2, t / 2)\n",
        "    ),\n",
        "    medium=si,\n",
        ")\n",
        "\n",
        "sbend.plot(z=0)\n",
        "plt.show()"
      ],
      "id": "1c9b6093"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We will add a\n",
        "[ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html)\n",
        "to launch the TE0 mode at the input waveguide and add a\n",
        "[ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html)\n",
        "at the output waveguide to measure transmission efficiency."
      ],
      "id": "b6292ecc-bb4f-48ff-ae09-95f54f514d4d"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "# add a mode source as excitation\n",
        "mode_spec = td.ModeSpec(num_modes=1, target_neff=3.5)\n",
        "mode_source = td.ModeSource(\n",
        "    center=(-s / 2 - buffer / 2, -s / 2, 0),\n",
        "    size=(0, 4 * w, 6 * t),\n",
        "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
        "    direction=\"+\",\n",
        "    mode_spec=mode_spec,\n",
        "    mode_index=0,\n",
        ")\n",
        "\n",
        "# add a mode monitor to measure transmission at the output waveguide\n",
        "mode_monitor = td.ModeMonitor(\n",
        "    center=(s / 2 + buffer / 2, s / 2, 0),\n",
        "    size=mode_source.size,\n",
        "    freqs=freqs,\n",
        "    mode_spec=mode_spec,\n",
        "    name=\"mode\",\n",
        ")"
      ],
      "id": "73e11826"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To facilitate the optimization, we define a function `make_sim` that\n",
        "takes the design parameter vector and returns a\n",
        "[Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html)."
      ],
      "id": "35768da9-8e64-424a-9a02-066c85e2edb0"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "def make_sim(x):\n",
        "    control_points = np.array(\n",
        "        [\n",
        "            [-s / 2, -s / 2 - w / 2],\n",
        "            [x[4], x[8]],\n",
        "            [x[3], x[7]],\n",
        "            [x[2], 0],\n",
        "            [x[1], x[6]],\n",
        "            [x[0], x[5]],\n",
        "            [s / 2, s / 2 - w / 2],\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    curve_bottom = bezier_curve(control_points)\n",
        "\n",
        "    vertices = np.vstack((curve_bottom, -curve_bottom))\n",
        "\n",
        "    sbend = td.Structure(\n",
        "        geometry=td.PolySlab(vertices=vertices, axis=2, slab_bounds=(-t / 2, t / 2)), medium=si\n",
        "    )\n",
        "\n",
        "    run_time = 5e-13  # simulation run time\n",
        "\n",
        "    return td.Simulation(\n",
        "        size=(s + 2 * buffer, s + 2 * buffer, 10 * t),\n",
        "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=15, wavelength=lda0),\n",
        "        structures=[waveguide_in, waveguide_out, sbend],\n",
        "        sources=[mode_source],\n",
        "        monitors=[mode_monitor],\n",
        "        run_time=run_time,\n",
        "        boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
        "        medium=sio2,\n",
        "        symmetry=(0, 0, 1),\n",
        "    )"
      ],
      "id": "f60b16e5"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Optimization Setup\n",
        "\n",
        "The objective of the optimization is to reduce the loss at the S-bend.\n",
        "Therefore, the objective function is simply the transmission to the\n",
        "output waveguide. By default, the objective evaluation is sequential,\n",
        "i.e. in each generation the objective function is calculated for each\n",
        "population one at a time. To leverage the parallel run of Tidy3D, we\n",
        "want to evaluate the objective functions for all populations in the\n",
        "generation at once. To achieve this, we need to do some customization\n",
        "for the optimizer, which we will demonstrate later. For now, let’s\n",
        "assume that the objective function takes a 2D array of parameter vectors\n",
        "instead of a single parameter vector and returns a 1D array of objective\n",
        "function values. Each row of the 2D array corresponds to one parameter\n",
        "vector in the generation and the number of rows equals the number of\n",
        "populations."
      ],
      "id": "8ab3b8f3-e84b-43b8-934a-08889ce9f573"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "def compute_transmission(sim_data):\n",
        "    amps = sim_data[\"mode\"].amps.sel(mode_index=0, direction=\"+\").values\n",
        "    return np.abs(amps) ** 2\n",
        "\n",
        "\n",
        "def objective_function(xs):\n",
        "    sims = {}\n",
        "    for i, x in enumerate(xs):\n",
        "        sim = make_sim(x)\n",
        "        sims[f\"population_{i}\"] = sim\n",
        "\n",
        "    batch = web.Batch(simulations=sims, verbose=False)\n",
        "    batch_results = batch.run(path_dir=\"data\")\n",
        "\n",
        "    T = []\n",
        "    for i, _ in enumerate(xs):\n",
        "        sim_data = batch_results[f\"population_{i}\"]\n",
        "        T.append(-10 * np.log10(np.mean(compute_transmission(sim_data))))\n",
        "    return T"
      ],
      "id": "8d7d8ade"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Define the hyperparameters for the optimization. We use a population\n",
        "size of 10 and run the optimization for a total of 12 generations. The\n",
        "initial standard deviation is set to 0.05, which is empirically\n",
        "determined. This parameter determines the size of the explored parameter\n",
        "space initially. Too large of an initial standard deviation could lead\n",
        "to a self-intersecting\n",
        "[PolySlab](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PolySlab.html).\n",
        "For reproducibility, we also use a fixed random seed.\n",
        "\n",
        "With the hyperparameters, we define the CMA-ES optimizer."
      ],
      "id": "8d1697c3-61ba-4e91-a491-3fdfc06b4fea"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5_w,10)-aCMA-ES (mu_w=3.2,w_1=45%) in dimension 9 (seed=12345, Sat Jul 11 07:05:21 2026)"
          ]
        }
      ],
      "source": [
        "dim = len(x0)\n",
        "population_size = 10\n",
        "max_generations = 12\n",
        "sigma0 = 0.05\n",
        "random_seed = 12345\n",
        "\n",
        "es = cma.CMAEvolutionStrategy(\n",
        "    x0, sigma0, {\"popsize\": population_size, \"maxiter\": max_generations, \"seed\": random_seed}\n",
        ")"
      ],
      "id": "1fada338"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Run Optimization\n",
        "\n",
        "With the objective function and optimizer set up, we can start the\n",
        "optimization. As we mentioned previously, we need to customize the\n",
        "optimization loop slightly such that the objective functions for the\n",
        "entire population can be evaluated in parallel.\n",
        "\n",
        "We also keep track of the best objective function value and display it\n",
        "at the end of each generation. We can see that the transmission improved\n",
        "from 0.271 dB in the first generation to 0.045 dB in the last\n",
        "generation, which is a significant improvement."
      ],
      "id": "f3ee0c23-58fa-43ab-bd32-11a8d7b436de"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generation 0: Best function value = 0.271\n",
            "Generation 1: Best function value = 0.265\n",
            "Generation 2: Best function value = 0.229\n",
            "Generation 3: Best function value = 0.217\n",
            "Generation 4: Best function value = 0.173\n",
            "Generation 5: Best function value = 0.120\n",
            "Generation 6: Best function value = 0.083\n",
            "Generation 7: Best function value = 0.064\n",
            "Generation 8: Best function value = 0.064\n",
            "Generation 9: Best function value = 0.061\n",
            "Generation 10: Best function value = 0.044\n",
            "Generation 11: Best function value = 0.040"
          ]
        }
      ],
      "source": [
        "best_values = []\n",
        "\n",
        "generation = 0\n",
        "while not es.stop():\n",
        "    solutions = es.ask()\n",
        "    function_values = objective_function(solutions)\n",
        "    es.tell(solutions, function_values)\n",
        "\n",
        "    best_values.append(es.result[1])\n",
        "\n",
        "    # Print message for each generation\n",
        "    print(f\"Generation {generation}: Best function value = {es.result[1]:.3f}\")\n",
        "\n",
        "    generation += 1"
      ],
      "id": "c2aebc58"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Plot the best objective value as a function of generation."
      ],
      "id": "f59f44e9-5e67-473f-9079-bfe882151847"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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3t5NfgUImpAUAAAAA41isVqvV7BBwPnFxcQoICJAkxcbGyt/f3+REQN4Wm3he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\n/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          }
        }
      ],
      "source": [
        "plt.plot(best_values)\n",
        "plt.xlabel(\"Generation\")\n",
        "plt.ylabel(\"Best objective function\")\n",
        "plt.show()"
      ],
      "id": "5461b0f3"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Final Design\n",
        "\n",
        "After the optimization, we can inspect the optimized design more\n",
        "closely. First, extract the optimal parameter vector from the optimizer.\n",
        "Then define a\n",
        "[Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html)\n",
        "and visualize it."
      ],
      "id": "50d57b48-4121-4098-8261-b76602478417"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "\n",
              "    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" 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": [
        "x_opt = es.result[0]\n",
        "sim_opt = make_sim(x_opt)\n",
        "sim_opt.plot_3d()"
      ],
      "id": "2d6e508e"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To help visualize the field propagation, we add a\n",
        "[FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html)\n",
        "to the simulation and run it again."
      ],
      "id": "4ae037ae-31dc-4d28-a4d7-a2ffd93cde77"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">07:14:00 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'final_design'</span> with resource_id                       \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-9d26ac3b-9038-4472-b3e2-4c5cd92e38ce'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-9d26ac3b-9038-4472-b3e2-4c5cd92e38ce\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-9d26ac3b-903</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-9d26ac3b-9038-4472-b3e2-4c5cd92e38ce\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">8-4472-b3e2-4c5cd92e38ce'</span></a>.                                         \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"d7c6a22b173a40e4b200fead8f304666\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">07:14:02 UTC </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. This assumes the FDTD solver runs\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>for the full simulation time; if early shutoff is reached, the     \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed cost can be lower. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the  \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed FlexCredit cost after a simulation run.                     \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">07:14:03 UTC </span>status = queued                                                    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"614d5d19450241778086520503a4f4e0\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">07:14:22 UTC </span>starting up solver                                                 \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>running solver                                                     \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"ff0d6d760694498188ba69e9bd2db814\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">07:14:28 UTC </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">65</span>%, exiting.                            \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = postprocess                                               \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"bb1f4e2a931f48f0b38dc97edf0e6ad5\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">07:14:31 UTC </span>status = success                                                   \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">07:14:33 UTC </span>View simulation result at                                          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-9d26ac3b-9038-4472-b3e2-4c5cd92e38ce\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-9d26ac3b-903</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-9d26ac3b-9038-4472-b3e2-4c5cd92e38ce\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">8-4472-b3e2-4c5cd92e38ce'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"b81306dc4f1446ad91827aee04d80af3\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">07:14:34 UTC </span>Loading results from simulation_data.hdf5                          \n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "# add a field monitor to visualize field distribution at z=0\n",
        "field_monitor = td.FieldMonitor(\n",
        "    center=(0, 0, 0), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\"\n",
        ")\n",
        "\n",
        "sim_opt = sim_opt.copy(update={\"monitors\": [mode_monitor, field_monitor]})\n",
        "sim_data_opt = web.run(simulation=sim_opt, task_name=\"final_design\")"
      ],
      "id": "84689a25"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "After the simulation is complete, plot the transmission spectrum, which\n",
        "shows a very low loss."
      ],
      "id": "de6fc974-96b7-4cd3-adc5-8eb6e4b81024"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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WZAEAAABQ3iCrc+fO6dSpU6t1li9f3uTnqqqqlEqlJsdW7qOqqqp8EwQAAACg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\nJLn55psbnx9zzDEt1jv22GOTJC+++GI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2nH2wYautrc2kSZMyadKkPPHEE1m2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\nEREREZkEJrKIiIiIiIiIiMgk/A8t6e0eLiLm7QAAAABJRU5ErkJggg==\n"
          }
        }
      ],
      "source": [
        "T_opt = 10 * np.log10(compute_transmission(sim_data_opt))\n",
        "plt.plot(ldas * 1e3, T_opt, \"red\", linewidth=2)\n",
        "plt.ylim(-0.5, 0)\n",
        "plt.ylabel(\"Transmission (dB)\")\n",
        "plt.xlabel(\"Wavelength (nm)\")\n",
        "plt.show()"
      ],
      "id": "5899ef1e"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Lastly, plot the field intensity distribution to visualize the energy\n",
        "flow. The optimized design allows a smooth mode transition at the two\n",
        "bends such that the loss is minimal."
      ],
      "id": "6a7183b9-d202-4b20-b76e-6207264baeb1"
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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555+jubkZ48aNw9lnn21M7trW1oa77roL3377LXbddVf87ne/i/pd7c7v5N/+\n9jdj5adbb70Vu+22W9y6X375Ja6//noA4bTLzlbwmThxIlavXo3S0lJs2LABNput034QEVFqcFCD\niIiIiGI6+uij8frrryM7Oxtbt26NOUgYa1Aj03300UfGqmA33XQTbrjhhhT3iIiI4mH6CRERERHF\n1B7V4PV68a9//SvFvek/7VE5+fn5uOiii1LcGyIi6gwHNYiIiIgopr322stIffrb3/5mrMQ0kK1b\nt85I07r88svhdrtT3CMiIuoMBzWIiIiIKK67774bFosFmzdvxj//+c9Ud6fP/eUvf0EoFMKwYcMw\nb968VHeHiIgSSL+ppomIiIgobUyePBm33XYbFi9ejOXLl0MIkXarCSVLXV0dampqMGvWLFx44YVw\nOByp7hIRESXAiUKJiIiIqMcG4kShRESUORipQUREREQ9lp2djVmzZgEAdtlllxT3hoiIBhtGahAR\nERERERFRRuJEoURERERERESUkTioQUREREREREQZiYMaRERERERERJSROKhBRERERERERBmJgxpE\nRERERERElJE4qEFERERERETdsmbNmlR3gQgAl3TtttbWVmzdutX4uby8HBaLJWnth0IhVFdXw263\nw+12p6yNrgoGg9i0aRMAID8/HyYTx8mIiIiIKL3ouo7GxkYAwIgRI5L6+X0wamxsxMSJE/Hee+9h\n4sSJqe4ODXaCumXOnDkCgLFVVFQkpd1ly5aJY445RthsNqPtwsJC8bvf/U5UV1f3WxvdtW7duqjn\ngxs3bty4cePGjRu3dN7WrVvXJ5+LB5M//vGPAoA49thjU90VIsFIjW549dVXccIJJ0Ttq6ioQHl5\nea/afeyxx/Db3/4WoVAIAFBSUoLW1lY0NTUBAIqLi7FkyRJMnjy5T9voiYqKCowZMyapbRIRERER\n9ZV169Zh9OjRqe5GxtqyZQvGjx8Lr9cHAPjggw+w//77p7hXNJhxUKOLPB4PJk+ejMrKSuy66674\n6quvAPR+UOPjjz/GAQccgFAohD322ANPPvkkJk6cCCEEFi5ciN/85jeor69HeXk5vv/+ezgcjj5p\no6dqa2tRVFQU+ckEQEta20REREREySEA6ACAmpoaFBYWprY7GWzu3Ll4/PHHjZ9nzJiBzz77DJrG\n7wGUGpwAoYsuvfRSVFZW4rTTTsPMmTOT1u6VV16JUCiE4uJiLFq0yMhJ0zQNxx9/PP7zn/8AANav\nX4+//e1vfdZGT0XPoaFx48aNGzdu3Lhx45amWxjngOu57777Dv/+978BAA8//AfYbFZ8/vnneP75\n51PcMxrM+BvdBW+88QaeeuopFBQUJHVQ4Ntvv8Vnn30GALj44otjjhgfeeSR2GOPPQAAjz76aJ+0\nQURERERElMhVV10FIQRmztwd551/DC66+AQAwLXXXgufz5fi3tFgxUGNBBobG3HBBRcAAO6++24U\nFxcnre3//ve/Rvn444+PW6/92IYNG/DNN98kvQ0iIiIiIqLOvPvuu3jzzTehaRruuDP8/ei6636F\ngoI8VFRU4KGHHkpxD2mw4qBGAldccQU2b96M/fffH+ecc05S2/76668BADk5Odh5553j1ttzzz2N\n8ooVK5LeBhERERERUTy6ruOqq64CAJx55mGYOnUcAKCgIA/XXvcrAMDNN/8ZHo8nZX2kwYuDGp14\n99138dhjj8Fut+Of//xn0ie/+emnnwAAo0aN6rRtdSLS1atXJ70NIiIiIiKieP7zn//g66+/RlaW\nDTfdPDfq2EUXHY/y8lJ4PE24/fbbU9RDGsw4qBFHc3MzzjvvPADhHLEJEyYk/Rp1dXUAkHD2ZfX4\njqOfyWgjEY/HE3err6/vVltERERERJQ5vF4vrr/+egDApZfNxvDhQ6KO2+023HLLuQCA++//GzZs\n2NDvfaTBjYMacVx99dVYv349Jk6ciGuvvbZPrtHS0gIAyMrK6rRedna2UW5ubk56G4m43e6425gx\nY7rVFhERERERZY77778fmzZtQlGRE1dddVrMOqfMOQi77z4BPl/AGAAh6i8c1Ijhww8/xMMPPwxN\n0/DPf/4TNputT67TvpyUruud1guFQkbZbDYnvQ0iIiIiIqId1dTU4LbbbgYA/PGGs+B05sasZzKZ\ncMedvwUQTlX58ssv+62PRBzU2IHX68XcuXMhhMDcuXOx33779dm18vLyjGsm6tOO5ySzjUTq6uri\nbuvWretWW0RERERElBluueUWNDa2YNy4YTj//GM6rXvggdNw1FF7AQDmzZsHIUR/dJEIllR3IN38\n5z//wZo1a5Cbm4vzzz8fa9as6VBHnUdiw4YNCAaDMJvNGD16dLeuVVJSgg0bNmDbtm2d1tu6dWvU\nOcluI5GCgoJu1SciIiIiosy2du1aPPTQgwCA2247DzabNeE5t99xPhYtWoYlS5Zg0aJFOPLII/u6\nm0Qc1NhRW1sbgPC8EzNmzEhY/8ADDwQQnoizpqamW9eaNGkSPv/8c2zevBltbW1x58VYu3Zt1DnJ\nboOIiIiIiEh17bXXIhAIYq+9p+CEE/fv0jmTJ5dj7twj8eij/8NVV12Fww47DBYLv3JS32L6yQ5c\nLhfGjh3b6eZyuYz6o0aNwtixY3s0YeZee4XDs4LBIJYtWxa33tKlSwEAmqZhzz33THobRERERERE\n7T777DO88MILAIA77/wtNE3r8rl/vvE3cDiysHLlSjzxxBN91EMiSRNMduq2a665BnfeeScAoKKi\nAuXl5T1qp7KyEsOGDYOu6zj33HPx6KOPdqgTDAZRXl6OLVu2YMaMGR0GLpLRRm94PB643e7IT2YA\nXf8Hj4iIiIiofwgA4Ynz6+rqmF7dCSEE9t9/fyxduhQnnLAfXnjxpm63cdNN/4eb/vIEysrK8PPP\nP8PhcPRBT4nCGKnRhzZu3Ig1a9agoqIi5vHS0lKcccYZAIAnnngCy5cv71Dn9ttvx5YtWwAAV155\nZZ+0QUREREREBAALFy7E0qVLYbGYcdvt5/eojT/84RSUlrqxbds2zJ8/P8k9JIrGQY0+dNhhh2H8\n+PGYPn163Dq33347SktLEQwGMXPmTNx333345ptv8Mknn+DCCy/En/70JwDAkUceidmzZ/dZG0RE\nRERENLgFAgFcffXVAIALLjgW48cP71E7ubnZ+POffw0AuOuu21FVVZWsLhJ1wFlbUmzYsGFYtGgR\nTjzxRFRUVOCyyy7rUOeII47Ac889FzeXLRltEBERERHR4Pboo4/ip59+Ql5eDv54w1m9aus35xyJ\n++9/CatWbcCNN96Ihx9+OEm9JIrGQY0eKCoqwtixYwEAVmv8pY1GjRqFYDCI/Pz8TtubNm0avv32\nW/zf//0f3njjDWzatAl2ux077bQT5syZg2OPPTZhn5LRBhERERERDU5NTU248cbrAQBXXX0ahgxx\n9ao9i8WM2++4AMcfdx0effSfuPTSSzFx4sQk9JQoGicKpV7hRKFERERElP44UWgiN9xwA2655RYM\nG1aEVT8+hZycrF63KYTAoYf+AR+8vwLHHnssFi5cmISeEkXjnBpERERERESD2JYtWzB//t0AgJtu\nmpuUAQ0A0DQNd975WwDAa6+9hg8//DAp7RKpOKhBREREREQ0iP3pT3+C1+vDLruMwa/OnJnUtnff\nfQJOPfVgAOGVGHVdT2r7RBzUICIiIiIiGqS+++47/Pvf/wYA3H7HBTCbzUm/xi23ngebzYrly5fj\nhRdeSHr7NLhxUIOIiIiIiGiQuvrqqyNzX+yOWbNm9Mk1ystLcdHFJwAArr32Wvh8vj65Dg1OHNQg\nIiIiIiIahBYvXoxFixaF576464I+vdZ11/0KBQV5qKiowEMPPdSn16LBhYMaREREREREg4yu65g3\nbx4A4MwzD8PUqeP69HoFBXm47vpfAQBuvvnP8Hg8fXo9Gjw4qEFERERERDTI/Oc//8HXX3+NrCwb\n/nLTOf1yzd/97niUl5fC42nCbbfd1i/XpIGPgxpERERERESDiNfrxfXXXw8AuOTS2Rgxorhfrmu3\n23DLLecCAO6//z6sX7++X65LAxsHNYiIiIiIiAaRBx54AJs2bUJRkRNXX31av177lDkHYffdJ8Dv\nD+CPf/xjv16bBiYOahAREREREQ0StbW1uO22mwAAf7zhLDiduf16fZPJhDvv+i2AcArMl19+2a/X\np4GHgxpERERERESDxM0334yGhhaMGzcM559/TEr6cMAB03D00XsDAObNmwchREr6QQMDBzWIiIiI\niIgGgbVr1+Khhx4EANx663mw2awp68ttt58Hk8mEJUuW4I033khZPyjzcVCDiIiIiIhoELj22msR\nCATxy72m4MST9k9pXyZPLsfcuUcCAK666ioEg8GU9ocyFwc1iIiIiIiIBrhly5bhhRdeAADcdddv\noWlainsE/PnG38DhyMIPP/yAJ554ItXdoQzFQQ0iIiIiIqIB7qqrrgIAnHDCfth7751T3Juw0lI3\nrrjyVADAn/70J3i93hT3iDIRBzWIiIiIiIgGuBUrVgAALr3s5NR2ZAeXXx7uz7Zt21BVVZXi3lAm\n4qAGERERERHRIOFwZKW6C1HSrT+UeTioQUREREREREQZyZLqDhAREREREVE/0fXwli7SqS+UkRip\nQUREREREREQZiYMaRERERERERJSRmH5CREREREQ0WAiRXikf6dQXykiM1CAiIiIiIiKijMRIDSIi\nIiIiosGCE4XSAMNIDSIiIiIiIiLKSBzUICIiIiIiIqKMxPQTIiIiIiKiwUKI8JYu0qkvlJEYqUFE\nREREREREGYmDGkRERERERESUkZh+QkRERERENFgIkV4rjqRTXygjMVKDiIiIiIiIiDISBzWIiIiI\niIiIKCMx/YSIiIiIiGiw0PX0SvlIp75QRmKkBhERERERERFlJEZqEBERERERDRaM1KABhpEaRERE\nRERERJSROKhBRERERERERBmJ6SdERERERESDhRDplfKRTn2hjMRIDSIiIiIiIiLKSBzUICIiIiIi\nIqKMxPQTIiIiIiKiwYKrn9AAw0gNIiIiIiIiIspIHNQgIiIiIiIioozE9BMiIiIiIqJBQhM6NJE+\nKR/p1BfKTIzUICIiIiIiIqKMxEENIiIiIiIiIspITD8hIiIiIiIaLIRIrxVH0qkvlJEYqUFERERE\nREREGYmRGkRERERERIOFLsJbukinvlBGYqQGEREREREREWUkDmoQERERERERUUZi+gkREREREdFg\nIfT0mpwznfpCGYmRGkRERERERESUkTioQUREREREREQZieknREREREREg4Uu0ivlI536QhmJkRpE\nRERERERElJE4qEFEREREREREGYnpJ0RERERERIOF0MNbukinvlBGYqQGEREREREREWUkRmoQERER\nERENFpwolAYYRmoQERERERERUUbioAYRERERERERZSSmnxAREREREQ0WQoRTUNJFOvWFMhIjNYiI\niIiIiIgoI3FQg4iIiIiIiIgyEtNPiIiIiIiIBgtdT68VR9KpL5SRGKlBRERERERERBmJgxpERERE\nRERElJGYfkJERERERDRYCJFeKR/p1BfKSIzUICIiIiIiIqKMxEgNIiIiIiKiQULTdWhpFB2RTn2h\nzMRIDSIiIiIiIiLKSBzUICIiIiIiIqKMxPSTLhJCoLW1FQ6Ho8+v5ff7YbFYYDL1fMwpGW0QERER\nEdEAI0R4Sxfp1BfKSPzGG4MQAp9//jluu+02HHrooRg5ciTsdjtyc3NhtVrxi1/8AvPmzcOGDRuS\nds0ff/wR55xzDkpKSmC322G32zFhwgT8+c9/RnNzc7+1QURERERERJQpNCE4NLajmpoaDBkyJGqf\nxRIOagkGg8a+7OxsPPbYYzj99NN7db0XX3wRZ511FrxeLwDAarUiGAyi/aUZM2YMFi9ejPLy8j5t\noyc8Hg/cbnfkJzMALantExERERH1ngAQAgDU1dWhoKAgtd1JAafTicbGRnz50vWYPnlkqrtj0HUd\nlikXAgAqKiqS/n2FBj5GasRgMpmw33774Y477sBHH32ELVu2wOfzwefzYePGjbjjjjvgcrng9Xpx\n1lln4Ysvvujxtb7++mv86le/gtfrxcSJE7F06VK0tbWhtbUVjz76KLKzs7Fu3TocddRR8Pl8fdYG\nERERERENAkIAup5eG1EvcFAjBrfbjQ8//BBXX3019t13XwwdOhQmkwkmkwkjRozA1VdfjYULFwIA\nQqEQ/v73v/f4WldeeSV8Ph9cLhcWL16MffbZByaTCVlZWTj33HPxxBNPAAB++OEHPPzww33WBhER\nEREREVGm4aBGD+2///4YPnw4gPBgQU/89NNPeO+99wAAv/vd7zB06NAOdU455RT84he/AAA89NBD\nfdIGERERERERUSbioEYvtM9XseP8G1316quvGuXZs2fHrdd+7Oeff8aqVauS3gYREREREQ0SqU41\nYfoJJRkHNXronXfewZYtWwAAxx57bI/a+OqrrwAAWVlZ2GWXXeLW23PPPTuck8w2iIiIiIiIiDKR\nJdUdSHfBYNBYDrW1tRWbN2/Gyy+/bMyjcdxxx+Hcc8/tUds//vgjAGDUqFEwm81x640ePbrDOcls\ng4iIiIiIBgldhLd0kU59oYzEQY0E3n33XRxxxBEd9u+666645JJLcNZZZ0HTeraMaW1tLYDE6Svq\n8fZzktlGIh6PJ+6x+vr6brVFRERERERElCwc1EjAarXC6XQCANra2owlUVeuXIk33ngDhxxyiDFh\naHe1tLQACKeOdCY7O9sot0eNJLONRNxud7fqExEREREREfUHzqmRwCGHHIL6+nrU19ejra0NW7Zs\nwV//+ldkZWXh+eefx4wZM3o98Wb7hKNdOR4vKiQZbRARERER0QAn0mBiUE4USknEQY1uGjp0KC67\n7DJ8+OGHyMrKwrZt23Deeef1qK28vDwAgNfr7bSeerz9nGS2kUhdXV3cbd26dd1qi4iIiIiIiChZ\nOKjRQ7vssgvOPPNMAMDHH3+MDRs2dLuN9nkuqqqqOq1XWVnZ4ZxktpFIQUFB3M3lcnWrLSIiIiIi\nIqJk4aBGL+y6665GuScRC5MmTQIAbNy4EX6/P249te32c5LZBhERERERDRK6SH26CdNPKIk4qNEL\n7ZOGAoDD4ej2+TNmzAAABAIBfPnll3HrffLJJ0Z5jz32SHobRERERERERJmIgxq98N577wEALBYL\nxo8f3+3zjz/+eKP8zDPPxKwjhMBzzz0HAJg6dSpGjx6d9DaIiIiIiIiIMhEHNWJItJIIALz44ot4\n7bXXAABHHXUUCgoKOtRpampCfX09GhoaYrYxYsQInHjiiQCARx99NOYqKg888ICROnLZZZf1SRtE\nRERERDRICJF+G1EvaKIr3+AHmTPPPBMNDQ045phjMGHCBJSWlsLtdqOlpQUrV67EM888g//85z8Q\nQqCoqAifffYZxo4d26GdiRMnYvXq1XA6naivr495rbVr12L33XdHfX09SkpKMH/+fBx44IFoaWnB\nU089hdtuuw26rmPffffFBx98AJOp4zhUMtroKY/HA7fbHfnJDIDLxRIRERFRuhEAQgDCK/vFuiE5\n0DmdTjQ2NuLLBVdg+sThqe6OQdd1WGZcAQCoqKhAeXl5ajtEGceS6g6kI5/Ph//+97/473//22m9\nadOmYcGCBTEHNLpq7NixeO211zB79mxUVVXhV7/6VYc6v/zlL/Hqq6/GHYxIRhtEREREREREmYaD\nGjE89dRTuOCCC/DWW2/hq6++wrZt27B9+3bY7XaUlJRg1113xfHHH49Zs2bBbDbHbSc/Px9OpxNO\np7PT6+233374/vvv8dBDD+GNN97Apk2bYLfbsdNOO2HOnDk4++yzO71OstogIiIiIqIBTqTZiiPp\n1BfKSEw/oV5h+gkRERERpT+mnxjpJ09djukT0iz95JfzADD9hHqGkRpERERERESDhQ5AT6P72unU\nF8pInGCBiIiIiIiIiDISBzWIiIiIiIiIKCMx/YSIiIiIiGiw0DlRKA0sjNQgIiIiIiIioozEQQ0i\nIiIiIiIiykhMPyEiIiIiIhosxOBKP6msrMTixYvx+eefY8uWLaisrITFYsGwYcPwy1/+EqeccgpK\nSkq61NZ3332HBQsW4LvvvkNjYyPKyspw8MEH4/TTT4fT6cyoNgYSTQjBNXSoxzweD9xud+QnMwAt\nld0hIiIiIopBAAgBAOrq6lBQUJDa7qSA0+lEY2Mjvnz895i+07BUd8eg6zos+18HAKioqEB5eXnS\n2r7yyisxf/78TutkZ2fjpptuwpVXXhm3jhAC1157Le6++27oMQZhhg4dimeeeQb7779/2rcxEDH9\nhIiIiIiIiAacmpoajBs3Dueffz4efPBBvPrqq1i6dCleeuklXH311SgoKIDX68W8efNw1113xW3n\nz3/+M+68807ouo6DDz4YL730Ej788EPce++9KC4uxtatW3H00Ufj+++/T/s2BiJGalCvMFKDiIiI\niNIfIzWMSI3Hfo/pOw1NdXcMuq7DcuD1AJIfqeHxeDp9rSsrKzFjxgxs2rQJDocDVVVVcDgcUXV+\n/PFH7LzzzgiFQjjhhBPw4osvwmSSsQFr1qzBbrvthsbGRhxwwAF4//33O1wnXdoYqBipQURERERE\nRANOosGr0tJSnH/++QCAlpYWfPvttx3q3HPPPQiFQrBYLHjggQeiBhIAYNy4cbjqqqsAAB988AE+\n++yztG1joOKgBhERERER0WAh9PTbUqioqMgoW63WqGOhUAgLFy4EABx66KEYNiz2XCRnnXWWUX75\n5ZfTso2BjIMaRERERERENCi99957AMLpOZMnT4469tNPP6GmpgYAsN9++8VtY8SIEUbazCeffJKW\nbQxkHNQgIiIiIiKiQaWyshJXXHEFXnjhBQDA3XffjZycnKg6q1atMsrjx4/vtL324z/88ENatjGQ\nWVLdASIiIiIiIuonQgB6Gq0VofSloaEBHo8nbtXeTPB66qmnYs2aNdB1HVVVVaisrISu69hll11w\n44034oQTTuhwztatW43y0KGdT67aftzj8cDn88Fut6dVGwMZBzWIiIiIiIgo5aZNm9bp8d4s3Pn9\n999j5cqVUftycnJw0EEHYcqUKTHPaW5ujqrbGfV4c3OzMZiQLm0MZEw/ISIiIiIiogHtueeew/Ll\ny7Fs2TK8/vrruO6665CdnY377rsPu+22G954440O5wSDQaNsNps7bV89rp6XLm0MZIzUICIiIiIi\nGix0AeipXXEkitKXFStWYOTIkX1ymR2jMY488khccsklOOigg7Bq1SqcfPLJWLVqVdT1HQ6HUW5r\na+u0fa/XG/O8dGljIGOkBhEREREREaWc0+lEQUFB3C3ZSkpK8OCDDwIAWltb8fjjj0cdV69ZV1fX\naVvtc4FYrdaowYR0aWMg46AGERERERERDUoHHHAAbDYbgHCkiGqnnXYyyuvXr++0nfbj48ePh6Zp\nadfGQMZBDSIiIiIiosGiffWTdNpSyGQywWq1Rp6a6L7svPPOxhwVy5cvj9tGW1sbvv/+ewDA1KlT\n07KNgYyDGkRERERERDQorV27Fi0tLQCA8vLyqGP5+fnYb7/9AACLFi2KO/Hmm2++Cb/fDwA4+uij\n07KNgYyDGkRERERERIOFrqfZltpIjRtuuMEoH3vssR2On3vuuQCAbdu24V//+leH47qu44477gAA\nFBUVpXUbAxUHNYiIiIiIiGhA+eGHH3DJJZdg8eLF8Pl8HY5/8803OOGEE/DMM88AAI444ggccsgh\nHeqddtpp2H333QEAf/jDH/Dss88aaSoejwdnn302li1bBgC46aabkJubm7ZtDFSa2DFxiKgbPB4P\n3G535CczgMExGQ0RERERZRIBIAQgvHpEX6ykke6cTicaGxvx5YPnY/q4slR3x6DrApYjbgIAVFRU\ndEgB6anPPvsMe+21l/Gz2+1GSUkJzGYztmzZYqwSAgAzZ87ESy+9hLy8vJhtbd68Gfvuuy82bNgA\nACguLkZRURHWrVtnLLF6wQUX4B//+Efc/qRLGwMRIzWIiIiIiIgGi1RPCtpPE4VOmTIF//jHP3DC\nCSfA5XKhrq4Oq1atwvfffw+Px4Pc3FzMmjULL7zwAt5+++24AxoAMHz4cKxYsQIXX3wxCgoKUF1d\njR9++AFtbW34xS9+gSeffDLhQEK6tDEQMVKDeoWRGkRERESU/hipYURqPHBe+kVqHHUzgORGauyo\nsbER27Ztg9/vh9PpxPDhw2Eydf8efzAYxJYtW9DU1ITS0lIUFRVlbBsDhSXVHSAiIiIiIiLqS/n5\n+cjPz+91OxaLBaNGjRoQbQwUHNQgIiIiIiIaNAQg9FR3QmLiAPUS59QgIiIiIiIioozEQQ0iIiIi\nIiIiykhMPyEiIiIiIhos+nDFkR5Jp75QRmKkBhERERERERFlJEZqEBERERERDRaM1KABhpEaRERE\nRERERJSROKhBRERERERERBmJ6SdERERERESDhdABXU91LySmn1AvMVKDiIiIiIiIiDISBzWIiIiI\niIiIKCMx/YSIiIiIiGiw0JFeKR/p1BfKSIzUICIiIiIiIqKMxEENIiIiIiIiIspITD8hIiIiIiIa\nLHSRXikf6dQXykiM1CAiIiIiIiKijMRBDSIiIiIiIiLKSEw/ISIiIiIiGiyEDuh6qnshMf2EeomR\nGkRERERERESUkRipQURERERENFgIEd7SRTr1hTISIzWIiIiIiIiIKCNxUIOIiIiIiIiIMhLTT4iI\niIiIiAYLHek1OWc69YUyEiM1iIiIiIiIiCgjcVCDiIiIiIiIiDIS00+IiIiIiIgGCyHSK+UjnfpC\nGYmRGkRERERERESUkRipQURERJQEGrRUd6FrtAzpZzwidXd1BTLvjnLGvC8jMvE5JqLU4qAGERER\nERHRYKHr4S1dMP2EeonpJ0RERERERESUkRipQURERERENFhwolAaYBipQUREREREREQZiYMaRERE\nRERERJSRmH5CREREREQ0WOhIr5SPdOoLZSQOahAREdGg1OOlLpOyJGrvg2W71H8t0XUyJWhXWamh\nO0+/6NkKD/GWFc2sxVG7pl+WfI33PlRen/bnXBOhDvuIiDqTKX/JiIiIiIiIiIiiMFKDiIiIiIho\nsNC5+gkNLIzUICIiIiIiIqKMxEENIiIiIiIiIspITD8hIiIiIiIaJIQQEGmU8pFOfaHMxEgNIiIi\nIiIiIspIjNQgIiKijNOrZShjLsma+D6Pcc24y6TG3q/FrG9OWFdT2pP7Y+3rynmx+xFdN3Gfkkkk\nWG5VKEt7Ru1H7PNitRfvGvHa6Grfelq3L3XndepW3W7eA+1O2+pzF9L9AIBgqElW0NtkXS7vmjxC\nhLd0kU59oYzESA0iIiIiIiIiykgc1CAiIiIiIiKijMT0EyIiIiIiosFCF+EtXaRTXygjMVKDiIiI\niIiIiDISBzVi8Hq9WLx4MW644QYceuihmDRpElwuF5xOJyZMmICzzz4bH3zwQVKvuWXLFlx11VWY\nMmUKnE4niouLsc8+++CBBx6A3+/vtzaIiIiIiIiIMoUmBKeb3dFvfvMbPPHEEwnrnXHGGXj88cdh\ns9l6db133nkHp5xyCurr62Menzp1Kt566y2UlJT0aRs94fF44Ha7Iz+Zgd7MRk9ERINOj1cxibmC\nyY7UFUFi1I9apSHeKiFyRRCTZolxXJZNJpnVq8XZb9KsHdowax2Pq9cL1wnvt2h2Y59VyzHK6n6L\nkG1YoOyPZB2bhHJtJRPZpDxHmlLHFHks6nFTN143Pc6qFfH3h1fEEJoes66urFyirmKiax1XIVHr\nxj0PoQ77otqI0a56XjyJVljpqa6sRmKKsbqO+rp3pb1YbcSv23F/rH07CiJolBtQCQCo8a429nl9\nW42yELJuzwgg8prV1dWhoKCgl+1lHqfTicbGRnxx7WxMH1GU6u4YdF3AevEjAICKigqUl5entkOU\ncRipEUP7OE95eTluueUWLF++HB6PB/X19Xj77bexzz77AAD+85//YO7cub261o8//oiTTjoJ9fX1\nGDFiBF599VV4PB5s3rwZt99+OywWC7755hscc8wxCAZj/2OejDaIiIiIiIiIMg0HNWIYNmwYnnji\nCaxduxbXX389dt99dyP9ZObMmViyZAkOOOAAAMCCBQvwxRdf9Pha8+bNQ1NTExwOB5YsWYLjjjsO\nLpcLw4YNwzXXXIOHHnoIALB8+XI8/vjjfdYGERERERERUabhoEYMt956K84++2yYTLGfHqvViptv\nvtn4+X//+1+PrlNRUWGce8EFF2Ds2LEd6px33nkYP348AOD+++/vkzaIiIiIiGiQaF/9JJ02ol7g\noEYPTZ482Shv3ry5R20sXLjQKM+ZMyduvVNOOQUAsHLlSqxZsybpbRARERERERFlIkviKhRLZWWl\nUc7Ly+tRG8uXLwcA2Gw2TJ8+PW69vffe2yh/8cUXGDduXFLbICIi6g/dmhQ07kSgHe/HRLWrxZkM\nUVM/8oQnQFQn4lQn7lTrmk1yMvD2+haTXTkeu6xO3GnTso1y++SeViGP26EcF/J6dqWcE2nPYZZ9\ny7PKiRyzLbL/Ocr8jjalbIk8p1aTvCtqVZ4utWzWOpbNmjyvp3fF1Kkz1anqdeU1bN8fVI6r93HV\nm7oh0XF/KOo82W5QV/d3PE+ok5F24drt/Yw3HWiybz6bYvxKxHsdYv36qOdrcfd3PLEr58Xqm6Y8\nc7GOA0BbSB6oaBoBAPhEeVSbArVGORTivHBJk27REenUF8pIjNTooeeff94o77nnnj1q48cffwQA\njBw5ElarNW690aNHG+VVq1YlvQ0iIiIiIiKiTMRIjR7YtGkT7r33XgDA0KFDcdxxx/Wone3btwNA\nwmVW1eM1NTVJbyMRj8cT91i8JWSJiIiIiIiI+hoHNbrJ7/fjlFNOQXNzMwDg3nvvRXZ2doKzYmtv\nIysrq9N6avvt5ySzjUTcbne36hMR0eDWV2kmUW1HpZmoqSNqWUYwauiYXqKmlqhlNb3EYlZSQ0zh\nsl3LNfbZNZmCmiUcRjlbyPNy9Ryj7DCH+5RnlR/BbEquh1p2KJ/SCiLdK82SyQ7Dsv3yuF2W82yy\nbLfIkH27LVy2WkPy8Vllexa7LJttSjnydJjUjxrK06/Fyy2IQehROSdyf7BjWU1PUY/rIXW/puwP\nl0MB2Tld73g83LYshyL7dV05T8Q+T93f3kasfUD8tJSeivUboakpQUo51n71V00zJT5Pi3Ge2awr\nx2O3F+s89biqtVn+3n22JXwDzrP2F8a+beZvjLIeajXKAkxX6A0hRPTvYoqlU18oMzH9pJvOO+88\nfPbZZwCA888/v9PJObtKi/uBrmvHk9UGERERERERUSZhpEY3XHnllXjyyScBAEcffTQefPDBXrWX\nm5sLj8cDr9fbaT31eG5ubtSxZLSRSF1dXdxj9fX1GDNmTLfaIyIiIiIiIkoGDmp00c0334z58+cD\nAA466CC88MILsFh69/QVFRVh06ZNqK6u7rReVVWVUS4sLEx6G4kUFBR0qz4REQ0+XUo5iRk1mCDN\nBIiZahIvzSR6RZPYq5i0p5S0p5MAgM0sU0fU9JIcyL+BOSKcapKvy+Mui0xVybfJ6zmssv8FNlke\nkRMO3y/LCshrm2RIv0mLvWKERQvXsZuV3AuFSQnH9wflkidqOoE5spyISUkFMFvktYWaDqJEgxtP\nY5yUk3gpJbHajZtyoqaa6B3rqikn0GO/19rfEmYlpcakXs+i9lmWLe2rn4jYx1UiRpS8el48epz2\njH7GSc9IRH19o/fH2BfnGona6PZ5MX6l1X3q+0Fto32lmmSn7VAMXP2EBhgOanTBvffeiz/96U8A\ngL322guvvfZawjksumLChAn4+uuvsXHjRgSDwbiDJBUVFUZ54sSJSW+DiIiIMphJg2YxQTOboZlN\nMFs1aGYTNLMJFrsOzWQCNA0WWwiayQSLTY+MmGgw29tHCzSYrLqRrmqy6eG1TIWO8Iq0Apr89h81\n4qIlGNSIXhNVKYcS1FHqmqIGRtTBB7UJreO+qIGajsvGqtfu8aBGFwb0TAkGNeINHCSixZlbIhmD\nGsb0NXG6noxBDUueHJDMMxcBAIab5GjXTm1jjXLA75Jt9GBOjVAohDVrfuz2eUSU/jiokcAjjzyC\nK664AgCw2267YdGiRd1O34hnjz32wLPPPgufz4cVK1Zg9913j1nv008/Nco71klGG0RERN2RzIlA\nE0VkhJvoGJWhRmSY4kz4qUZiWNVIDFP473i2SUZh5OtyUmynnm+Ui6zyJobTFo6AKMySfR6bK7+h\nlWbJCTotyhc+NRLDHNnfFpLRFCF10soYd/1NVjParFaYLGa0WswwWcObsFhgspgBLd5XW8CsITI4\nAVj94X5YfcrkoMpTbtWUCUSVL8CWSP9N8b78KqIiLow775q606DHiXAwehfncvHOS9i37rxvFfGf\n3d7pyvM5EKmvX0tQ/h5vFuGoJ1+OXKUvR5ORUAGrnHC3J4LBYOJKRJSROFFoJ5588klceOGFAIBd\ndtkFb7/9NpxOZ9LaV5eCff755+PWe+GFFwAAkyZNwk477ZT0NoiIiCjFTBrM2TZYnTnIKs5HzjA3\nckcXI3/CUOSOLYFjRCGyy1zIKsqDzZkDS44dJqulk0EjIqI4hJApKOmyEfUCBzXieOmll3DOOedA\nCIFJkybh3XffTfrSpmPHjsXhhx8OAPjHP/6BDRs2dKjzf//3f/jxx3Co3MUXX9wnbRAREVH/0Sxm\nWHKzYCvMQ/YwN3LHFCN/pzI4RhUhu8wFmzsXlrwsmOwW9DC4gIiIaNBg+kkMixYtwumnn45QKITx\n48dj8eLFGDJkSLfb2XnnnfHjjz/C5XKhpqYmZp177rkHH374IZqamnDwwQfjoYcewgEHHIDW1lY8\n9dRTuOqqqwAA06ZNw3nnnddnbRAREXUmYcpJgjSTDm0YKSWdp5kAsVNNLCaZFmI1q2kmyiSfJpcs\nKxN+Fujh3P1CIVNSirLl9UqyZT/G58mUjCJbeHJPh0WGsauTeTYEZD+97eklmoaAyQZLjg2WbCtg\nt8Nkib6nFNI1oA0dqOkJWeZw2WGWqSN5VtmPbLMs25X+qROLWiKTglqUfWaLUMpyv0n5hGiKnKfF\nyxSKQ8SaNDTeDdkYKSWxzu/YXt+M+sSdZ6Kn7Q2Q24hdeU0StqG8Zt5m+UZrCYV/L5118ndY05RJ\nb9U2et8NIhpAOKgRw1133QW/P5wXu3btWowYMaLT+occcgjeeuutDvuDwSBCoVCnOXxTpkzB888/\nj9NOOw3r1q0zoi5UkyZNwv/+9z9YrdYYLSSnDSIiIkoCkwZLtg3m7GxYcmww263wKV/iggkmjCQi\n6nM60ivlI536QhlpgIwbJ5dQbiPouo5QKJRw642jjjoK3377LS6++GKMGTMGNpsNubm52HXXXXHn\nnXfiq6++wrBhw/q8DSIiIuo+c44NtqI85I4qgnN8KRwjCmF3O2DOsjJ9hIiIqI9pQsQNBBy0QqEQ\nuvO0mEwmmEwdx4fa29E0DWazOcaZmc/j8ShzjZjBT29ERANH36WcAFokpSRRmgkQO9XEbpYrlKir\nmOSi0CgX6rJcbJUpKkOyw9cZnye7NiInYJSdSlqHLyT7XOcPn+c3mWFz2GHLy4LJkWUsg6o+G2qM\npvoJICeSypGrpHpkmdTVSGKXsyMpJTk2JeUkW662YstRUkqylZQSZaUW2CL9tCmvg5IKo1nVpVDU\nOpFHYFbaMsXJRTHFeE909y5sMnIckqmnuSOxnot0lOy75IleP+V6ep3XKG/4LPw7+u+f5e/2e83/\nM8o+f7W8RA+6FQwG8e23XwEA6urqUFBQkOCMgcfpdKKxsRHLLzkW04cVJj6hn+i6gO3aJwAAFRUV\nKC8vT2l/KPMw/SSGZA1ADNSBDCIiosHGZLcgOz8X1tws6HY54BJrCVYiIiLqPxzUICIiIorBZLPA\n7nDAmpcNk90CPRC+WeFPs0ACIiKiwYyDGkRERINcwjQTIE6qSeI0k+jVTTqmmkSlmZiVNBOTTBex\nWWQ4evuKJk6UGvsKdbnkeqldnjfMISMmJ+XLkYgim0zbaFcTSS0xWc1osLtgz8+CJcsajsTQAXgB\nS2RFDDX0PUdZjcRplSkgdiV1xKaUcyyRFVTsMt3FbpcpJRalDWuWPK89pcScL59PU77yfObmKJ2y\ny3K2fH5hi9S3KJGkVuWjoEUpq9Gmkf0iap/yGquvd6z3SYwU3V7RuzGq1NNr9zjlpGenpVRPB+l6\nmiakvH6mmlqjXLZ5NQAgf4PL2KeZGPXcF4RIryyvdOoLZSYOahAREdHgpmmwO7Nhd+XAmmNDG1co\nISIiyhgc1CAiIhqEeh6dAbTfjk40CWi4rEZnyKgBcyRCw2pxGPvsZjlzZ7ZZTuKXj2KjXKQPAQAM\ntcvIhOFKRMYvnDLSwW3zGeWGgKyzpiXSjywrsl05yHJmw2rW0AagLRg9v6NDicRwRaIoss3yGrlW\nGXGRq0SAZCmRGLYsGYlhyw23Z8mVF9FyZd9MDvkcadnKMuy5WdH/B4Bc+dwJhxqpoey3K1Eb7ZEa\nXN6d0oQYMsQo27/bAABwLpfHTfyqQkRdwH8piIiIaNDQTBqyC3KQ43ZAWPkxiIgGIV0kf9Wb3kin\nvlBG4l9zIiIiGvBMNguyCnJgc+YgFApHKgSZx01ERJTxOKhBREQ0iCRMO0mQchLVRtw0E7lfnQjU\napYpErZI2kmWkmbi1OTkn25dppwMs8q0lOH54bZ3dsWe+LN9wk8A+K7BDpvDBkdRLkwOO5oAwA/Y\nIw8lyyzvDhYoE3TmWmQ5T0kvyYuklDiyZFpLVq6SWqJMRmp2KhN65iopJfmRdJA8JV0kV05uihxZ\nFtlKqkkkpUTk5sp9NqaR0MChDXUBABzKtxOTxolCiSgxDmoQERHRgGPLy0Lh0HxYIvNSBBndTEQU\nxvQTGmA4qEFEREQDg6bBXpCDbHcuzDYzAv5MXF+TiIiIuoODGkRERANc91JOYg8EaFFh4JHVT5SU\nE7NJXdlEpk2oq5tkWVxG2WEqBAAUimHGvqFwG+UR+bK9KU55F294djjVpMonP8J802BDTkEOcotz\noZvMaAwB8ALZSpfzrR1TTZxWmTriVFJY8rJkOcehpJq4wuepqSXmArm6iOZU0kjylfSSfGU1krxI\n+oi6ckkOU0qI2n9PrCb5+6dpHJjsC0IAIo3mFEqnvlBm4qAGERERZSy7KwdD8vNhtoZHMHR+OCYi\nIhpUOKhBREREGcfuzEbOkDyYrGb4mGZCREQ0aHFQg4iIaADqacpJ1HlK6LcWY6UTk6asbGKR6RZ2\ni1ytRF3dxKUNNcrFenilk1HZMg1jgpLWMdUp0z7UFU1WBXORV5IPYbOiKQggCOQon2by7TLNxKWs\nYuKyBZVyuG2no83Y58iXIe82t2zDXCAfo6kg0tcCJV0kX5aFUz5u5Mly1IolZq7mQEQplm4ThYq+\n60sgEMDy5cvxwQcfYPXq1di2bRs8Hg/y8/MxZcoUHH300Tj00EOhxV35C3jttdfwpz/9qUvXW7x4\nMQoLCzuts2LFCjz11FP47rvv0NjYiLKyMhx88MH41a9+hYKCgk7PTWYbAwkHNYiIiCjtmWwWOEry\n4Y8MnvhDCU4gIqJBbc2aNZg6dSpaW1tjHl+8eDHuv/9+7LXXXnj66adRXl4es15dXR2++eabLl0z\nEAjEPSaEwLx583DvvfdC7DCQ8+qrr+K2227D008/jYMOOqhP2xiIOKhBREQ0QCSMzgCUCI3uRWeY\nNDkhpikyKajVLKMzsqwuo5xrHmKUC8VwozwMRUZ5jCvcxm4FcnTCapLRFN832iPd0WAuzEeOOwfN\nAOyRrioBFChQozCUyT/ddhntoUZl5LrCURk22R2Yi+TkplqBMslngRJ94coHoEz2CUDk58vjdvkc\nEVE3NYW/eAZ0OVmu4AyS1AttbW3GgMYuu+yCk046CXvuuSfcbje2bNmCBQsW4KWXXsKnn36KAw88\nEF988QWKioo6bXPJkiVwuVxxj3d2/vXXX4/58+cDAGbOnImLL74YQ4YMwRdffIFbb70VlZWVOPbY\nY/Hxxx9jl1126bM2BiIOahAREVFaynJlI68kH744K7IQEVEP6JEtXfRhJszuu++OO++8EwcffHDU\n/j322APHH3887r77blx11VXYsGEDbrzxRvz973/vtL2dd9454cBHLD/88APuuusuAMDs2bPx3HPP\nwWQK/23ba6+9cNRRR2HXXXdFQ0MDLr74Ynz44Yd90sZAxU8JRERElFbMWVa4RxfCOdQFk5kfVYiI\nqPt22mknfP755x0GNFRXXnklJkyYAAB4/vnn+6wv8+fPRygUgsViwf33328MRrQbM2YMrr76agDA\nRx99hE8//bRP2hioGKlBRESUwbqXcgK038/obsqJ2SzTM6zm8ISZ2Va3sS/fXGqUS0IjjPJou9Mo\nT3DJ6+zqCqeGbPLKPJLNzSbkl+bD4Y7MmxHJJFEnAi20hW8vqhN/FilpJq4cmWaS55Jle7Fswzwk\nO/xYhygTeLplP4VLppSIfLkf+UoqChElldhWDwBoDcr0NdGXt/BpwLPZbAnraJqGX/7yl1i9ejW2\nb9+OxsZG5KtphUkQCoWwcOFCAOGUkbKyspj1zjrrLFx33XUAgJdffhl77bVXUtsYyHj7g4iIiFLO\nmmPDkHFDjAENIiLqIwIQukirLZUaGhqMclcGQrpr9erVqK2tBQDsu+++cesNGzbMmKz0k08+SXob\nAxkjNYiIiChlNJMGR0k+slw5aGvrQtQJERFRkni9XixZsgRAOF0lKyur0/o33HADfv75ZyOiY/Lk\nyTj88MNxxBFHxF0WdtWqVUZ5p5126rT98ePHY/369VHnJKuNgYyDGkRERBkoYdpJjJSTqPOUNBNN\nk6sNmE02pSw/3NktMvWiPe3EpQ0z9g0LyVVOxuU5jPL0AnkHzqmsTPJ1vR32XDusQ53wWc1AG5Cr\nfCrJt6jnyRVSiuzhlUsKsmRqicsll+vLHiJnv7OUKiuaDFFSR4a4AACisMDYJdTZ7LOzMeiFlDVz\nQ5HnVF2JIhiKWVfTg8r+SH09yTMSmuIEGmsx9nclJjnGeSJWW3H7k4aDcT288631dLWR7pynVNW2\n1xjltrVeAECjsiKmLpT3EyVPGk8U2tDQAI/HE7dqQUFB3GM9cccddxiRGr/+9a8T1v/HP/4R9fPi\nxYvxwAMP4Be/+AWefvpp7Lzzzh3O2bZtm1GOlzbSbujQoQAAj8cDn88He2RVrWS0MZBxUIOIiIj6\nl6YhvywfOW4HWkOJqxMR0eAwbdq0To8LkbxUlaVLl+L2228HEJ5k89JLL41bd/LkyTjuuOMwZcoU\njBo1CpqmYfXq1ViwYAGWLFmC7777Dvvttx8+/vhjTJ48Oerc5uZmo5yT03mKpXq8ubnZGJBIRhsD\nGQc1iIiIqN+Ys6xwDHWhOdR5iC8REVFf2bBhA2bPno1AIACbzYZnnnkm7mDBKaecEjOKY5999sE5\n55yDBx54AJdccgnq6+tx7rnndpjLIhiUEUdms7nTfqnH1fOS0cZAxkENIiKiDNHrlBPASDvpbspJ\njlWuSODWwqubjBAyBHaSS6Zs7DNExo/X+eWHqzUWJ/IL8wC/hvabbXmyGyiy6UpZtlGopJq488Mh\n6o4iv7HPWiw/zphLldVKSlxGURTJlVpEQaTsyKBJSQOR58MnV3rRlDL88vnQ/Er8vvqBtr0ciLEP\niE45CSjl9lSTUJz0EzXVIRRjf3dvrMZ6m8dL8YiZchKnbtyMko71o/bES3dJdL3+0NMJFruVEtTT\na8TbL9vT61qMcuXm8L839T55oq4r72VKHoEev6x9QunLihUrMHLkyD69XHV1NQ477DBUVVXBZDLh\nySefxIwZM+LWTxQZ8fvf/x6fffYZnn76aXz66adYsWJFVMRJbq5caautrS1GC5LX6415XjLaGMi4\n+gkRERH1Kc1sQv5IN/JL83cYeCEiIpKcTicKCgribr1VX1+PWbNm4aeffgIQniNjzpw5vW73rLPO\nMspLly6NOqb2u30Fk3jq6uoAAFarNWowJRltDGSM1CAiIspk3YjOCFcPh0ao0RkWs/zQo0ZnOCwy\nOqNQG2WUR6EUADCxQLZxULG8c7SmWebvbtdscA8rQKvJDLtyK8VpDd+aK1AmAS3OktEGhTly8k+X\nW7adVRI+z1wmJyPVipXojCJl8s/CQll2J3dyuV5T7qRprfKxai2yjBZ5Fxutkfotyh06r7yLLdqU\nsk9GXwh/qENZ+NXIClnUg8oddPlSQESaVlPZRUi+v4SulpU6Ce4ExxvfijlHp/p21mI3bJwX73gP\nr5dQKm8RJjngojvzffZ4agMhn1xvkwzVWtsQ/rfHE5BvPl1w0h1KnubmZhxxxBFYsWIFAODee+/F\neeedl5S2R48ebZS3b98edWz8+PFGef369Z220358/PjxUaupJKONgYyRGkRERNQncgodKBpdCJO1\n8/xfIiLqP0IICD29tr7m9XpxzDHH4LPPPgMA3Hrrrbj88suT1n5jY6NRdjgcUcd23nlnWCzhmwvL\nly/vtI8rV64E0HHC1GS0MZBxUIOIiIiSSjNpyB9RAEcJ002IiCi1/H4/TjzxRLz//vsAgBtuuAHX\nXXddUq/xzjvvGOUdVz/Jz8/H/vvvDwB44403EAjEnivmzTffhD8yP9IxxxyT9DYGMqafEBERpbG4\nk4MagwXxUk6U/TEmBY2XcpJrLTXKxaLcKI8yyVSUya5wG3sWylSI7xvDE4yarWZgqBsNNgvgA3KU\nIA23OhGoPZwiUZQlJ7ssypXpFnklykSgpbL/prJIX4eoaSbKJKBqmkm2nLy0XygfMrUWufwemluU\n/ZH0kkYltUQpi2b5nIpG+dzoTeEw/GCLvKMZ8srXO9CmzHYfkK99ICifO38w/BoFQvK4iMpEkft9\nQdleexX1nRhSUgh0pZzofqvahjrepSlnmmKkj0Sdl6BuvF8Z9U5ed86LbqNnd5S1BOeJbuW7xKZ3\np40uPAz1dY19vZ7RlXSllqD8KrK+NVyuh0fpg5JW1cPr0eAWDAZx6qmn4s033wQAzJs3DzfddFNS\nr1FRUYH58+cDANxuNw4++OAOdc4991y89957qKqqwmOPPYYLL7ww6ngoFDKWly0uLo45IJGMNgYq\nRmoQERFRUtgcNrjHFsFi5z0TIqK0pafh1geEEPjNb36DV155BUB4lZK77rqrW238+OOP+O1vf4s3\n3ngDLeo8RwivQvKf//wHe++9tzF55+233x5zcs5TTz3VWGHliiuuwIIFCyAio8q1tbU488wzjbSS\nm2++uUMKS7LaGKj4qYOIiIh6LafQgbyS/PAPnNuPiIhS7O2338aCBQsAAJqm4YMPPkg4z8Rzzz2H\nCRMmGD83NzfjkUcewSOPPAIAyMvLQ2lpKTRNw/r1641UD7PZjJtvvhnnn39+zHY1TcMrr7yCfffd\nFxUVFTjzzDNx2WWXobCwMKqdiy66qE/bGKg4qEFERJRmEqecxKkblXIi/8SrK52YTeEUhHgpJyVi\njFEea5UpJ1PdMh1hfG74g9OK+vAqJ/ll+QjkOuCNZE645eInKFRSTortMqWkxBFezcPtkne+cspk\ngLl5mLK6SYlLNlgcXtFEuOU+4eqnlU1C4dEarVmmlmgNcnI4NCkpJ41KuUE+Rt0Tftx6gwyrDzbI\nx+1rks9zm1emjrT6wnf+vAH5unpDallJOdFlOajcAW2L7Pcp4f8WJQ0j2ywrZ5nl/pLs8As7JF95\nHEq7Pr/ycTJByoK6MolJqWoyyf3tq5uYTPIiUWv8mNW6StOR+lH71Oup89XGqBO1CooW7xroKN5D\nTnY8dKK72erqNDHqRq1WEud1il69RuvQbvy6Mc7rwrWblZWSav3h97tDyLvcGsxKWWkidpeoi4To\n3ko3fa3HK+kkoM47IYTAt99+m/Acr7IyFQBMmjQJjz32GN58800sWbIEtbW1aGpqMo6Xlpbi8MMP\nx6WXXppwwGTo0KFYsWIF/vznP2PBggWoqakxIjymT5+OefPm4bTTTuvzNgYiDmoQERFRj2gmDa7h\nLtjzstDgT1yfMlRIh+71Qw94INraIIIhhJQlXf0++XEy1hdaVdQgQ9SAQ8f90QMdsg2zMvii7jeZ\nYwxqKG0IdTBEGXCIOagR59ox572NM3gRcwCkFxJ+CU2wnG7UPr3rgxrxrhtzAATRS/zGurZa19eQ\nZZQ9VeF/RNaul4Nn37WuMsqBgFwmU/RgWEP01TdnSlv7778/vv76626do0ZpAOGVTObOnYu5c+cC\nAFpaWrBt2zYEg0G43W4UFxd3q/38/Hz89a9/xfz587Ft2zY0NTWhtLQULperX9sYaDioQURERN2m\nmU1wlxfCmm1NXJkyiwCCLT4EW30ItviQb24AAOhuJSpFGdSAEiXSnUiNqDJi7Fe/NUc1m2B/1C39\nONdQdxvXi3NeVJgIUqcbgxox63ZhUCPqvPbXMt51Y9WN13a8KJGQbEREwopCSnhRMBiMWe7JoAbj\nOwaf/Pz8pC9r6nA4MG7cuF63YzKZMGzYsJS3MVBwUIOIiCgNxE05iSvy7SpOyolJk2Hd7SknAJBl\ndQKIn3IyXkk52a1Ihn6X2OVEGStbs1FYXoiQxQR/5HtGoZJyUqzULc6S4b8lDpmS4R4SDvG1D5f9\nNw9zyUZKlNVNSopkuSCcfgJbHw6mKOHHWmODLNdHvtR7lC/3dTIMWffIlUtCHhm6EqiXr623Kdzv\n5tZcY1+TXz6WZrUcks9/czD8PLUpqSV+Ze4Sr1IOKN/d1A965sipNuWLuSNSIegNwBzwItDiQ7DV\njxK7zzi3TYT7sanGKR+r8n4NxbvrH6kTb7USc5yojfb9ZiVawqykoqjnWcwhpU7kvKhIDuUaantK\nnfYoES1O5Ii6PyrCw9TxuCphpIYpzpfseAMOEXEjJ6KiJTrWjxdNES+Kor1O1Ao5cVbOiTovUtbj\nXE99vzT65D8czZGBsgDk4IU6ctKzgQyKqQ8n5+wRvrTUSxzUICIioi4zZ1lRNKwIJrOGED+IZiwR\n0uFr9kFva0OgxQc9qMNk4wyvRESUeTioQURElM602LHvmmaO/D9OdIZZRmeok4I6LOFIjGJRbuxT\nozP2GCKjA1xWeSvvh0YLbA4bCovc0EzhBU6KoiYElXdXS7OUCUHzZH68q0RGQNhGhicvNanRGWWy\nH2JIoSwneyJQPXJnvklGXERN+Ompl2UlEkPUhiNNQjUyIiNQJ0d2WhvkhKxNrfI5b2iTT1RjIByJ\n0RBQojCUu9/NMrAlKvqivewLxp5oU2VRDtiVyTGzgkG0NbbB6/Uh0Bp+jXSjOQ1tunwv/dAoI0Y8\nvnAlv6wcNaCld2NwS+2z2n2zcqD92YiaR0OdOyPB/qh2lUbiXbt9f/Tx2M9zzCk14rwO/TFPaLzn\nPtZuta6IExmmx3hd47280e2JmPtj9Uc97lWCMtY3h3+v1uJLY18wFL2EJhFRLBzUICIiooSy8uxw\njyyIv9IDpaWANwBvYxu8jW3I0cPfIHP46Y9oUBP64Fj9hAYP/lkjIiKiTtnys+F2cUAjU/hb/Wht\naENrgxc29ZsL53QlIqIBiIMaREREKRJ3ctB4KScxlmHQNPlNVU05sSkpJzlKekmhNgoAMNYsl6Hb\nVZkQNN8ivwT/1GRCtjMLBQUFUVdunxR0iJJyUpItUzJK8uWEoPmlMhXFNipbPpbh7nChTPZDL5IT\ngsKRg2TSGtQJPyPlWo+sUCvTT/Rq2f9gjex/W034OW9slI+jvk0+5/V+mX6ippeo5aZIekmLEnbf\nqqSUtARkzkkwRni/+h6wKC+KJaCjpcGLlnovTMqKEl6ljZq28A8en7x4qy7zXdQJGn2aXyn7Isfl\nPh09m3/DBPlcaMp72xSjbBKmLtSVT4IWqW+Kk/ih1o06L0b9eHVj0WKu89o/urJMqR4jgSTWPgAQ\nMZJc4tWNv79jG0KLHRbQ/t4CAI+2FQDQ2LZFnicCHc4hItoRBzWIiIgophxXNlzDXanuBsURDITQ\n7PGi2eOFXZmowJTKZUeJKP0JcPUTGlA4qEFEREQd2J3ZcBW4Ut0N2oHQBZrrvWjytCLUKu9i263m\nTs4iIiIauDioQURE1M9ipp10JeVEWemkPe3EbJLpD1azwyjnWNxG2a2NMMrlWikAYBe3bGuITd6y\n+zFBygkAlNjDaQpl2XI1kyFOuUpB3lCZpmAdKdNItOEyvUREVjoRQ2RqDOzKRXrKK/uk1cn0Eq2m\nVtapqQcA6JUyzSRQKfvsrZXPTX2TTOOp84af6zq/sjKImmbil8+YuopJU0BZISUYTttoDslUD11J\nIQgqaR3qa6+1CTTUtsLX0AZdb1+NRNatDsjH3WCSqTZtkK9LIBLq70ervJ4S/q8rof4hJS1FF+G+\nCqVvohuzDGpa7NARDbEHYtrrx0oLCR+X58VKNVGvF+8asc9LPDAUr0+JxEuJiSVW+kZccTJfYqWR\nRB0XidOHEvVDJEhBinqPxLkTr77P2oLh960/qKxE1IV+EhFxUIOIiIgMWXl2FDDlJC3oQqDJ04r6\nmhaYI1OWWJlbQkS9JER6rTiSTn2hzMRBDSIiIgIAWB12uAsLdogaof4W8AdRX9OMxlqvEZWRo3Hp\nEiIiolg4qEFERJTO1FB6daWTSNqJRVnxJMviNMpO8zCjPEIvM8qTCsIrdIx2yLDu7xsssOVYUegu\njApnL5SLeRgpJ4BMOykpkOkbucNkGLllRK7s8wgl5aREppoYaSe23n9Zj0ozqVXSTKpkWVTKlIzg\n1nD/W6pkukF9o0wzqWmVq5vUKKkm9f5wfY/MVEGDkmbS7JfPaXNQPl8tQp4Q0MInhNTQ/chz7m3x\nobnKj5bGcFiGuupGdSSNpB7Vxr42yMfkh0w/CQZleomuy36EImkkapqJmiLQnmay4/7O9nVX3FSU\nGPu7kuoR87y46S6dt9eV9JOu9qEvJeN16Er6iVG3hzNKdqWfap2QHn7f67o3XnVKFh1IwtsoaRip\nQb3FQQ0iIqJBzmK3oHCUG5qJk9CnQlN9K2q3N6Kt1Y8skZ34BCIiIjJwUIOIiGgQM1nMKCp3QzMz\n5aQ/CSHQ4GlBbVUjAv5g4hOIiIgoJg5qEBER9YNurXiihMFryoonJqXcnnaSZXEZ+/LMpUZ5WEiu\neDLeKe/+71oQ/gL9bb0VmklD0ZhCCIsJoUiIhltJORkSI+UEkGknecPlcctImb4BNeWkrFiW3YWy\njrWHaSe+8God6momUSubbJPl0NYmedpWGYNSXxNekWV7i1yZpbpNPvBav0Upy6brfeE2mgIydL8p\nKFM5mjS50ohfkyfqmrrSiY762iY0VPsQjLSjazIOvE7bZpRbdPlYgqFwSonfJ68R0pVrxEkdEer+\nGGkE0SkC3VjNohu6lp7R9dSPRO31NG2l+1I1aWvf5Q0kJbWlG/2Lfq8GIv9XVtlh7FjfEOjLt1H3\n8WWmXuKgBhER0SDlHlkAq90CPz9Q9jkhBOprm7G9ug6BQAg2MM2EiIgoGTioQURE1EdiRmcASoSG\nKU7dOJODKpOC2i3hyAiHRU6+WaaXG+Wd8uRknfsNkdEEq5vC7VlKnQjYbQgEgSLZbFR0RklWm9zv\nlBEC7ZOCRkVnjFQmAS1VykWyDHMPJ2JskBNiGlEZlTXyGtvqjXJgi4woad4mn7uaBodRrvKGBxSq\nfEpEhk8+/56o6Az5fDQEwwealYgMn0lezw85Qad6h7m53oeqSg8C/mD4LrYG1OtbjONtQdl/f0i2\nHQzJ518Yk3yqERmxJ/yMFmN/VyZwTOKtU3USwLi/E7HEiaZIPKlg7yMo+nvyz2RLRsRF9yRx8tJu\nTGJKRARwUIOIiGjQyR2SC7+TkQJ9raXZi21baxBs43wlRJQ+BFc/oQGGgxpERESDSFZ+FnKL81Dn\nS3VPBi6/P4itW7ajqTEcdWHR7CnuERER0cDFQQ0iIqJ0oMVOOTFpcgJLq1mmUGRbwpNuDhEjjX1j\nspxG+ZeF8jbc+tZIykmWBeahLjQHgQLle3aRTdYtyZK5F8X5MhUib6jcbxkRSW1RJwRNRspJQKZT\naB6PLFfLVBNUhdNPQpsbjV2+zTIlo367nPyzsllJOVEmAq32hftUqwzs1PlkyHuD0o8mJaWk2RxO\ng2kT8tohJR3EJCyoqqxDzfb6qIfVFKw0ym2B8LGgLlNLdGXCT3XyTzW9xAjvVydW7FKKSBJvx3bl\ndqrWeVRKt27IRqUhdCcdJHH6QqI0mMF65zi1E3OmUegAEWUUDmoQERENAiaLCa6RbjToTIXoCw31\nzaje2oBgkPMBEFF6EyK9Bu7SqS+UmTioQURENAg4hxfAbDED/sR1qev8/gC2bK5Gc1Mr00yIiIhS\ngIMaRERESZR4xROgPZRe08zKYfkn2WxSUk4sMp0iyyLTS5xaKQBgpCZTPXZxyxD9eiVzwevKh9ls\nA3xAbuQyaspJqZJyUpIr0y3yS2R+hnW47Ic23A0AECVJSDnxKykntTLNRNtWLetsqTWKwU3NAICW\nrfKxbq+Tz0ulV06AWtmmrH6iTNZZ4wvfFvQoK5t4dJlq02yS6SVtkOWAHn4+RCRMvrbGg8qtHojI\nbcaW4HajrjdQJ/usrGKii/BzrevqpCZK2H3c9JJYq5ik4e3NPutTnAiYBOku8aThMzewdeN9kdoU\nmEFCaEA6Re3xJade4qAGERHRAGZ3ZiG30JG4InWZz+fH5k1VaG3xQkPPlqklIiKi5OCgBhER0QBl\ntluQX+aClyknSVNbU4+tW6uM6AwiIiJKLQ5qEBER9ZU4ofEyRSXOiidK+olNWfEk11xslMv0YQCA\ncW55Xp5FftH+udmEkmEFqPJpsCvBBC5buE6xXaZ9FOfIlBPXEFm2DZf90IYVGOX2tBNRKFc/6VbK\nCWCknWjbZcqGtrVKXmOzTEUJrJd9atwa7lNlY66xr9KbJctt8qNNjcz6QI2yuonHHx7laVDSTJpM\nMl3EJ5qNciiSLhIMBLFlUw2am1qhK6kgLf4tsp9B2c+QsrpJ1ComkdVS4qaWMEzfkGiFEgDpmYJD\nPTLQ38/pROhRmW4px19j6i0OahAREQ1ABUOdsNj4Zz4ZmhpbsHHDNkDvzrKiRERE1B/4aYeIiGiA\nyXJlIyc/O3FF6pwQqNy6HdXV4SgOs8ZBDSIionTDQQ0iIqJe6lKYvJJqAq199RMl5URTVjxRUk6y\nLYVGuVAMM8rlOeE6U/JlDPHqJjMsdguGOAugK+G8efIyKLKF0x+GZMv0CHehknIyTH40MA1zyRNL\nZaqJKHBHKisNd0Wg40onUSknG2XKib9C9smzVQ7QbI2knWz1yuVTq3zKSihKykltm7K6SUi21xBJ\nNfGiwdjXpjfJE4WOQCCIDeu3wNsq+9wWrI/8X54XVNqNWtFEyGuLWCt3xIm3Zgi+1JfPRdd+Zwcu\nvs8GNyEAIdLnd4DpJ9RbHNQgIiIaIDQNcI9whcdM4qyASYm1tHixvmIzgsEQTBo/KhEREaUz/qUm\nIiLqoZh3e6MmBzXFrKtFviirX5gtZjnZpd2SZ5SdWqlRHmmSURvTI8ESW9vkNSxFeQhYLAgEAacM\n/EChTY5wFGWFowmKXC3GvqwyWdc8PF/+UCKjM3S3vDYcOeiykLy2Vlcry9uqAURPCKpGZ9RtkdfY\nokwKutkbmSjUq0ZnyNt8tT651EsdZPRFk9ljlL16PQAgoMvnwDinrh5bNkWiSDQTWgPKhKXB8ASi\nId1r7BO6vF5URAYjMdIaXwciooGDgxpEREQDgM1hg6XQkbgixbV1SxW2b6+DSetmWg0RUSYRXP2E\nBhYOahAREWU4zazBOcwFD1NOekTXdWzYsAWNDc2JKxMREVFa4aAGERFRf1BWzmifINRkkjkiNmVy\n0FzzEKNcog81ymNdMoIgFLmzVd0GFI5wokE3I1f5q15ok7e+iuxysssiRzh1IqdETmRpGSrTXVAi\n00xEkVvuz1fqJKKmnNR2TDkBALFpOwAgsD72hKBqysmmVjkp6Na2cBpPtVfeZqz1KyknWr1RboZM\nHfGG1Mk9w8+BppkQDIZQsW4jvF7ZRrOvUqmrTgQaPk8IdfRIud2p3G5kegMRpSuhM1KDBhYOahAR\nEWWw7PwsZDu5fGtP+P0BrF2zAX5/ABqXayUiIspIHNQgIiLKUJpZQ8EwZ6q7kZF8Pj/Wrd2EQCCY\nuDIRERGlLQ5qEBERdUNvVzwB5Kon6oonWRaXUXZCrngy3C7TUsbmyhjdtc0a3COcsFvk9fLVFU/s\n8st6UXabrFMYTqGwlslro7TAKIpCpZyvrISSSNyUkypZZ5NMBwluCK88Ur9FSS2JscoJIFNOAJl2\nUuPzGftqTXJlk2ZsN8ptasqJLuu3etuwds0GhJQ+t/lkGyFlVRQhgko5Ul/EnryEKSdElAmE0CBE\njL9lKcL0E+otxloSERFlIKad9Eyrtw1rfl4fNaBBREREmYuRGkRERBlGM2twlTHtpLvaBzR0PY1m\nyCMiIqJe4aBGHEIIrF69GsuWLcPnn3+Ozz//HNu2bQMAnH766bjrrruSer2Ghgb861//wuuvv47N\nmzfDbrdj/PjxOPXUUzF79mxoWuIQsWS0QUREvROVnqKknLSveALIVU/sFrmiSK652CgP1eXqJxNd\nMqiyKpJF4hzqRFAzASEgTzYLp1VGHxTaZbpFQb5cwcNeFm5PK1FSS4YoKScul9xvVRqPRZcxw1p9\nvSxXyzQTbFFSTjbKJVMbt4Sfg20N8jnY4pWpKJVe+bjVlU7a005qTTLFpUkoKSe6TDkJRFY5AQBv\njAgNf7BR9i0kU06gppxAieiIESPNlBMiyjRCaBB6+nwvYPoJ9RYHNeKYOXMmFi9eHPNYXV1dUq+1\nfPlynHjiidi8eXPU/u+++w4vv/wyDj74YLz88stwOuPflUtGG0RElP6sOVbkFORAWcWUEmhr8zHl\nhIiIaIDinBpx+P1+TJgwAWeeeSYeeOABfPTRR31ynY0bN+Koo47C5s2b4Xa78Y9//AOrVq3CF198\ngUsvvRSapuG9997D7NmzIeIMYyajDSIiygyuoa5UdyGjBALBDpOCEhER0cDBSI043nnnHdjtMgy2\nra2tk9o9d80112D79u2w2WxYvHgxpk2bZhzbbbfdUFZWhmuuuQbvvvsunn32WZx22ml90gYREfWA\nkdYX+x6Bpsn9JiUVxWIKrzySZZZpH25drngyKldOAJqj/KWuzc5Fs2YB/IAjst9tl4PVhVaZNlGY\nI/9u5QyR+83taSdFLmOfcClRfNldn3xUa6yX5RqZDoJtshzcJFNOmjbLB7OtPrzSyVY15aRNTbWR\nj6tWCUtpX+lETTnxhuTKJQFdppwEgyH8/PM6+APRYS3+QDjtJKTUFSKg1FDm3FBuBjDVhIgGAiHS\nK+UjnfpCmYmRGnGoAxp9ZevWrXj22WcBAL/5zW+iBiPaXXnllRg5ciQAYP78+X3SBhERpT+z1Yz8\n4tzEFQlAeG6sinUb4fMxT4eIiGggY6RGCi1cuNBIBznjjDNi1jGbzZgzZw7uvvtufPnll9i4caMx\nQJGsNoiIqHNRk38mOh5nclBzJDoDAOyWcLREnklODloGl1GeqMzhWRkJuHAX50NomhEr0B7BUaBO\nDpolozPyXLJsKbbJBgvDE3MKt7yeyFMu2BUt4YlHtbp6paMyOiO0RU7W2apM9VRVLwdltrWGn49t\nbWZjX7VX3q6r88mJTus0eZ1mhCM01OgMf0hOhAoRjrJYv34TmptllIg6KageidAQQp0ENHZ6CqMz\niIiI0hsjNVLos88+AwBYrVbMmDEjbr199tnHKH/++edJb4OIiNKbPdeOrPysxBUJALBtWxU8nobE\nFYmIBiOhhVdASaONqDc4qJFCq1atAgAMHz6803SXsWPHdjgnmW0QEVF6cw3tZiTFIFbnqUdl5fbE\nFYmIiGhAYPpJClVVVQEASktLO61XVlbW4ZxktpGIx+OJe6y+vr5bbRERZTxNvaPU8d5A9OSgMu3D\nYpYTcNrN4UEKd0imn4x2ybptSiaEucCBgMmCQDB60tB8SzgtosAmJwF15cqJL7OKZNqESZ2LoyA8\nKajIy1MuIlNA4lJWD9EaI1EQtfXGPr1SRkb4tsi6tR45IFPZKqNNqnzhB7NdmYe7zicfSx2ajHIz\naoxyWyh8nUDUJJ/h81pb27BxwxZjf6yUk3D9QHsBsTDlhIgGMqFrEHr6REdwolDqLQ5qpFB7rm9O\nTk6n9bKVmejV/OBktZGI2+3uVn0iIkoOk8WE/OK8xBUJwWAI69Zt4NLlREREgwzTT1JI18OTmWla\n5yOlJpN8mdrPSWYbRESUnnKL8xL++05h69dvQiAQSFyRiIiIBhRGaqRQbm4u6uvr0dbW1mk9r1eG\nzObmRi/nl4w2Eqmrq4t7rL6+HmPGjOlWe0REmSDRiifRleXAsbriickk/8zazA6j3L7qSSlkasZw\nJeBucytgtVtgycmJuvuQr/zVdtnCKR4uu/z33+GSy5eahygTi7rlv/vCFYn8yJH96QqtSaZyGKue\nVNcb+4Jb5WolDTXywVS1yEjBSp98bqoj3a5VU06EjCRs0uRqKq26vI4/1AIACIXkY62srEZjY7h/\nQV2uhBIz5ST8Q/h/TDMhokFIIL1SPtKpL5SZOKiRQm63G5s3b0ZNTU2n9dTjBQUFSW8jke7WJyKi\n3nOWcXLQrmhqbsa2bd2bK4qIiIgGDqafpNCECRMAABs2bOg0JWT9+vVGeeLEiUlvg4iI0ktWnh1Z\nufFXtKKwYDCI9RWbUt0NIiIiSiFGaqTQ9OnT8cILL8Dr9eL777/HLrvsErPe559/HnVOstsgIqKe\naU9R0TT559SklC0mmQLSvuIJIFc9Ge2S6RgtMgsDOaV5CETGqR2yCvKtcvC6wBZOv3DlyvQTW6FM\nmdEKlXyWfDnZqHDktnc0/gNrp6Quag1yNZL2VU9CVTJdpHW7fNzbm2XKSZWSclIrM1RQ5wunz3iC\n8hoNZiXlJFRvlP1Bee2QLhvZuHELgsEgdF2mokSnnCjLyCjxzUw7IaLBTAhAiPSZr4npJ9RbjNRI\noWOPPdYov/zyy3HrtR8bPXo0dt5556S3QURE6SPLmQVrljVxxUGutrYODQ2NiSsSERHRgMZBjRSa\nMmUK9t13XwDA3//+d1RXV3eo89prr+Grr74CAFxwwQV90gYREaWPPC7hmpDP58emTVtT3Q0iIiJK\nA0w/6UMHHngg1qxZA6fTiZUrV8asc88992C//fZDbW0tZs2ahSeeeAJTp06Frut44YUXcP755wMA\nxo4di0suuaTP2iAiok5ELauq3A8wVj0xy6Mmm1G2WeQKIw5ToVEeIsIDF0XKAiWbW4DcwhzYhAVW\n5RJRK55YZTqFKyuchpHjVFY8KcpWKisrnuQrq17ZE8zVEZLX0JrUlBOPbG97OO3EXynTYTyN8hrb\nffIatX75YOraZIxxfWT51SZTg7HPq8tyICRTW9SUE10PYf36DdD1kHI8XsqJLDPlhIgoTOgadD19\n0k90/vNMvcRBjTieeeYZzJs3L+6xN9980/jZ4XBg9erVHepVVlZiy5YtaG5u7nCs3Z577ol//etf\nmDt3LlasWIFp06bB5XLB6/XC5wt/iBs6dCjeeOMNZGdn91kbRESUWpoGOBmlkVBNTS2am1tS3Q0i\nIiJKExzUiKOlpQVbtmyJeay1tRWtra3Gzw6HI2a9rjrzzDPxi1/8ArfeeisWLVqE+vp6AOGBiDlz\n5uCGG25IuKxqMtogIhrs2if+7HqdcBSCOjmoWY3UMMnohQK92CiPygvXaQrItuwF2dBNGnRdINsi\n9+dZ5C0sly1glPMd4QlCrYUyEkIriDM5aLbyd8ra+XwdmjIQr3lk5ARq5fwVoapwZESzRz7WGq8M\nO6n1y8iVOmVy0IaAnA21AeEokGbICBC/LiNDArqcAFXXw+f5A35s3rwFgNghOkM+L4zOICLqXHii\n0FT3QkqnvlBm4qBGHKeffjoOP/zwLtU1mWJPTfLBBx8gEAjEPa6aNm0aXnjhBQBAXV0d7HZ7twdL\nktEGERH1v3CURm7iioPcpo1bIPjpl4iIiBQc1IgjJycHOTk5iSt2oqSkpEfnud3uXl03WW0QEVH/\ncBQ6oJs5d3dn6uvr0djYlLgiERERDSoc1CAiIool4eSggKaFUznUyUGtJjl3UbZJpv0VCpkOUmAP\nt72tVYdm0pCVnwO7WV4vX8kQcdlkOoXTLicFzXGFUy7MRcoAfIGScqJODpqlzEgaS4tMqdTUZVLr\n6o2iXi3TUtqqw32tbZbRgLV+2ek6n3wsjX45mWhDSKaUNJvDbftC8nr+kJwrIxTyKeUANm7cDCF0\n6CKciiJ0+VwAOoiIqGsENAiRPhOFMgCPeou3hYiIiFIorzAHJkZpdGrr1ioEg8HEFYmIiGjQ4aco\nIiKiFNE0IH8I59LoTJvPh+3ba1LdDSIiIkpTTD8hIiLqIU3ruPqJ1SxTMvIxxCgPz5EpIN5QONY2\nq8AB3WSCLgCHEgnsUFY8ybfKCIW8bJm+YSkIX1tzKUt1O+OseGKJ8ec+IFcM0VqUpcfrlRVPauQc\nFsFqme7RWB9OefG0ybSbOmXFkwZlMZJG5TpNJtlemx5OOwmEZOqLrsu6AuG0m00bN0Io6SUisupJ\n+/HwD0I5j3HMRESdEYLpJzSwMFKDiIgoBcJRGlyhqjMN9Q1oampOXJGIiIgGLUZqEBERpUBOQQ6E\n1Zy44iAlhMDmzdtS3Q0iIiJKcxzUICKiQU1D4hBctY6mpJq0p51YzDIFJMvsNMouXSnbZRu1bQJZ\neTnIUuIlc5UVT/ItMrUi3ypXAclxyvQMc4E90rCM9hA5SiqKuuKJSXmMeiTO1ytTWTQ1GsIjU0RC\n2+VqJK01cgCmzhtuW13xxKMsRtLkl/1vFF6j7NVkaktAD7cdDMnjui5TbbZv3w6fvy2yXz4HRnoJ\nU06IiHpEFxr0NEo/0flPOPUS00+IiIj6WY4zCxYb7yvEE9JD2LatKtXdICIiogzAT1RERET9zMkV\nTzpVXbUdoVAocUUiIuo+XYPQ0ydSgxOFUm9xUIOIiKidpn7IU4IZNTWwUaZhmEzh1T+sZpnqkaMV\nGOWhNjl44Yt8R7c7bDBnWxCCgN0k281R/iKr6Sd52TKvw+qSn/w0ZyTVJFeZbDRbSTmJteIJAATD\nKR6aV6aWQEk/EXWyHKiVq440N8nreHzh1Jf6gOx/o1/2rSko02RaTbI9n1DajqSdhKJWPAkiGAxh\nW+U2CKGseCKU5VREKFKXn4KJiIiI6SdERET9Kp9RGp2qrKyG4G07IiIi6iJGahARESUk7wGYlIlC\nzZFIDZtJDlQ49UJZzpFRHQ1+HdYsCwJZVpgjE486lL/CDrOMTMizysiE7BwZqWHKU07IjURlZMvJ\nQYXVrnQ5xuSgANAWnnxTa26V+xpk1EaoTk4g2uaR12ufHBQA6vzhx1WvBFA0BWT/m4Rsw6vJiUfb\nJwcFgJAx+afsWyAQxPbtNeG9Qk4OCuggIqLkECK9Uj7SqS+UmRipQURE1E/yixyJKw1i27ZVMUqD\niIiIuoWDGkRERP3AZDYhtyAn1d1IW4FgALW1danuBhEREWUYpp8QEdGgpKHzmd/V45oyUagWlX4S\nTvfINsnJQYtEvlG2Kpcwu3LCWSACsEayUtT0k1yLTLFw2IJG2Z4rJw015SqDIo5wOoiwW+U+q1JW\nU06Csj2tLTxBJ5plKoiol6koIY8yOWizTG1pCNiMclMw/Hw0K+knLV2YHDQYkiklujHhZ7hvlZWV\n0HX5WIVQVj9Rojc4QSgRUe/oAHSRPquf6PxnnXqJkRpERET9gKkn8QWDIWMuDSIiIqLu4KAGERFR\nH8txZsFs4Z/ceKqqtnMuDSIiIuoRpp8QEREZlIEHTR2EkKuYmEzyT6fVFE7PcECmnwzJUtI0AuEv\n6g5nDnQlbSI7MsCRY5b7ci0yRSTHLlc8MSsrwGoOJb3EHrmORdmnCsn22lc8AQCtNZJq0iRTTkSj\nPO5rkI+70SdXU2kMyv1NkUyTZr9MEWkRss9tmpJyontll3SZftKeXiJ0oGZ7HTSYoHPFEyKiPieE\nBpFG6Scc06be4m0jIiKiPmTLtsDusCWuOEht314LXecABhEREfUMIzWIiIj6UF4h59KIRwiB6mrO\npUFE1J+E0AbNRKE+nw/Lli3DBx98gNWrV2Pbtm3weDzIz8/HlClTcPTRR+Pwww+HpnXt+fjiiy/w\n1FNP4bvvvkNjYyPKyspw8MEH46yzzkJhYWFGtTGQaIJJrNQLHo8Hbrc78pMZSLCaABFRujBWN4n6\nIKOuciLTOswmuQqIzSpXN8m3DwMAlIupxr6pefLDRENQYOSkYmgmDRblOmU54esMz5ERCqMdMh1k\n+JAGo5w3XvbOPEb5oDKsBAAgCmXqi8hWVkdRoh+0ZpkOorUPImyqMvYF1sjr1VVkGeX1HvlYK1rl\n/s2RhVO2tcoVT6ogl2P1iK1GuTVYa5T9IdmPkO6Hp64eFevXyy6HlBVZoK6EwkgOIuotAUT+Xamr\nq0NBQUHn1Qcgp9OJxsZGvPLLUzA5f0iqu2PQhcCkdx4CAFRUVKC8vDwp7f7888/YZZdd0KakYMYy\nY8YMPPPMMxgzZkzcOkIIXH755bj//vtjzgFVXFyMp59+GoccckjatzEQMf2EiIioj+S5s6GZONgb\nT1XV9lR3gYiIBiifz2cMaOy666649dZb8e677+Krr77Cf//7X8yZMwcA8Pnnn+PAAw9EdXV13Lau\nueYa3HfffRBC4IgjjsDrr7+Ozz//HA899BDKyspQXV2N4447DitWrEj7NgYipp8QERH1kTx3TuJK\ng1RLcwu8Xm/iikRElFSDaaLQPffcE3fddRf233//qP3Tp0/H0UcfjRkzZuCKK67Apk2bcOONN+Kh\nhx7q0MbKlStxzz33AABOOeUUPPvss0a6yh577IEjjjgC06dPR319PS6++GIsXbo0bdsYqBipQURE\nFKGp/2kmZbMYm9lkN7ZsrQDZWgFKzfnG5g3q8AZ1CLsFZpsZuhDQhYDNrBmb3QzYzUCOWTc2hy1o\nbHZHyNhMOVZjg90mN6s1vGkmuem6sWl+v9xavcaG5laguRWi0WdsgQYYW3ObzdiaghZjawlC2QRa\nggKtesDY2rRWY/MLuQWFz9h0PWRs27fXQdNMEEKXm/IfhLIRERF104QJE/Dpp592GNBQXX755Zg0\naRIA4IUXXohZ55577oGu67Barbjvvvs6zL9RXl6Oa665BgDw8ccf45NPPknbNgYqDmoQERH1gVxG\nacQVCAZRX9+QuCIREVEPWa3WhBOAapqGGTNmAABqamrQ2NgYdTwUCuG1114DAMycOROlpaUx2znz\nzDON8ssvv5yWbQxkHNQgIiJKMpPZBIcrO3HFQapme23iSkRE1Cf0NNxSqaFBDrLbbNFLsP/444+o\nqwtPhL3vvvvGbWPo0KEYPXo0AHSIkEiXNgYyzqlBRESDhtatFZrMRslkkn8urcpKKDlwAgCyzPIe\nQaM/BOeQLLQEdViUy2WZ5Q85keZyLUG5z+43ypZcpc8O5QNWliwLs+yfISDbg88ny+rM783heSxC\nTfJ6/hb5+Fr8ctWXlqB8XK1K023B8AoCbZq8hl/IlUt0IVdFEbpcxQQIz9xeU1On/Kz0M+UfbYmI\naDBpbW3Fe++9ByCcrpKVlRV1fNWqVUZ5/Pjx6Mz48eNRUVERdU46tTGQcVCDiIgoyThBaHwNDY0I\nBoOJKxIR0aDT0NAAj8cT93iyl+K9/fbbjZST3/zmNx2OV1ZWGuWhQ4d22lZZWRkAoL6+Hj6fD3a7\nPa3aGMg4qEFERIOTkWerZGJqJuWwLJs0+efSZnYYZace/nBlVnJ2LdkWWOwW6EJAM8k2spS/uA5z\nOCLBoURq2LNkdIMpV+lTjvJhJEspWzv+CdeUwQKtTYmAaJGrjIjm8H69UUZFeL3yzlRLULbrDZmU\nMpRy+AevJtsNKBEXIV2J1IiKvhDYvn07AB26iPRVMDqDiKhfpfHqJ9OmTUtQN3mTR3/44Ye44447\nAADjxo3DJZdc0qFOc3OzUc7O7jytNCdH3tBobm42BhPSpY2BjHNqEBERJVG+25G40iDl8/vQ3NyS\nuCIREVEfqqiowOzZsxEMBmG32/Hss8/GHCwIheSIvjlW2qdCPa5GJKZLGwMZIzWIiIiSRNM05Lqy\nElccpDhBKBERdWbFihUYOXJkn16jqqoKhx12GLZv3w6z2YwFCxZgt912i1nX4ZA3Krxeb8w6sY7n\n5srJsdKljYGMgxpERESG2CknZpMM3bRr8gNCoRb+kNESmTgz15UNmDXoCIfHZisTiGYrN1ay29NP\nbPIOii1H3oXRHEqoaLYyUahVTuKJ9jsxavpGUKZ9RE0U2ionChXN4QlCA00y9LjFp0wOGjIrZdmE\nNyhDfn0ifCBoktcI6vIaISEnIW0PFRZCoLa2Ae3PsYhMJiqghBIrYcVR+4mIKGl0AHoapZ/oyj/3\nTqcz6fNmqDweD2bOnIk1a9ZA0zT885//xOzZs+PWV/vSvvpIPO3HbTZbVApIurQxkDH9hIiIKEny\nCgfHh4eeaGhojAqfJSIi6k9NTU2YNWsWvvvuOwDAfffdh3POOafTc3baaSejXFFR0Wnd9evXAwiv\nPqIpc22lSxsDGSM1iIiIksBiNSM7144gJ76MqbY2/mz2RETUf0QaTxTaV1pbW3HUUUdh+fLlAIA7\n77wTv//97xOet/POO8NisSAYDBrnxuL1evH9998D6DjZabq0MZAxUoOIiAY1Tf1PMxlb+E9keLOY\nbMZm1/KMzaxpMGsa2vQgrE4r/Hr0hFw2s2ZsdhOMzWEOwWEOIcsaNDZLLozNlGMzNmTJTdisxgbN\nFN50yC0QlJvPL7cWn7HpzUHozUH4vRZjaw3IrSVoMjZvCMbmC+lygx8++BEQbcami4CxCT1kbAAQ\nCATQ2NgEtbNChLfoB0BERJRcPp8Pxx9/PD766CMAwF/+8hdcddVVXTo3Ly8PBxxwAADgjTfeQCAQ\niFlPPXbMMcekZRsDGQc1iIiIkiDPzdSTeOrq6lPdBSIiGoSCwSBOOeUUvPPOOwCAa665Bn/605+6\n1cZ5550HAKiursYjjzwS8xq33347AKCkpCTmYEK6tDFQcVCDiIiol+w5VtjszOiMh6knRETpQ4jw\n5JzptPUFXddx1lln4bXXXgMAXH755caX/u445ZRT8Mtf/hIAMG/ePDzxxBPQ9XB0YXV1NU4//XR8\n+eWXAIBbbrkl5uSc6dLGQKUJ0R9ZTDRQeTweuN3uyE9mAOmTn0dEtCNN/TcqMnmWBrnah2aSa9Rb\nLXKVkzz7UKM82iSXfSvVXAAAR1kOXEXh+jlmuZLIsBw50DHSIf/cjskNL7c2srDB2Jc/Vk6iaRnj\nkv0sLTKKwq3st0eWjg3JlBetpUUe3y4HEsRWuZSqf024Tt1m+WFnQ32eUV7vlSuvbFaa29YqQ12r\nEJ5Z3SO2GvtagjVGORCSJ7a0tmL1j2sAALouV0UJ6eE67augRH6QRa5+QkRJJQCE/52tq6vr0xU2\n0pXT6URjYyOe3f00TMwrTnV3DLoQ2PX9+wGEJ8EsLy9PSrtvvvkmjjjiCADhJdcnT56c8JyXXnoJ\nEyZM6LB/27Zt2H///bFmTfjvmcvlgtvtxqZNm4x0j0suuQT33Xdf3LbTpY2BiLeViIiIeimvYPDc\nDemuOkZpEBFRCgSDctBfCIGVK1cmPMfr9cbcX1ZWhq+++go33XQTnnrqKVRVVaG+vh4AsMcee+Cq\nq67qdGnYdGpjIOKgBhERUS/k5NlhNjObMxYhOJ8GEVG6GSyrnxxwwAHG8q1dNW7cuLjH8vLycPfd\nd+Ouu+5CdXU1mpqaUFpaitzc3LjnpGsbA01KBjV8Ph+2b9+OtrY2DBkyBE6nMxXdICKiQUCLmxYX\nGYjQ5ICEppTNJptRtppkJEaOkCkqrSKAfFdu1DrwdrNahlKWq3tkmcMh0FabTDkxZSkDI1blz7NF\nKZuUx9K+dKwu24BPSeXwyVQP0Sz3h7zhNtoCsl2vLjvqU5sLyU+aPiEP+DVfuC0hr6GL6JVfAKCx\nsQm6LoznNaoOl74lIqI+lJeXh5133jnp7WqahpKSEpSUlGR8GwNFnw9qCCHwySef4L333sP777+P\nL774Ao2NjVF1rFYrxo8fjwMOOAAHHHAADjvssEGZ50ZERJlF0zQ4nNmJKw5SHk99qrtAREREA1yf\nDWrU1tbi3//+N/7xj39g7dq1ndYNBAL44Ycf8MMPP+Dhhx9GdnY25syZgwsvvBAzZszoqy4SEdFg\no8WK2lAiNZSySZMTfto0OXBhEbKO3WmBrukwKWG8VpM8blMiNbJMMurBbglHPViylGgFm7xedKSG\n0ogqMuM5Akr0g7puvVeJomiTdQJt4f55lUiNtpCmlGUTaqRGQImyCJrCbYfQMToDCD+Puq6job4Z\nIioiQzZuTATKyUGJiPqVDg16Gk3uz7g96q2kD2o0NzfjzjvvxPz586MmWsnKysL06dOx8847w+12\no7CwEHa7HbW1tairq8PGjRuxbNkyVFVVwev14oknnsATTzyBgw46CPfeey+mTZuW7K4SERH1Sp6L\nE4TGU9/QCC6wRkRERH0tqYMaTz/9NK644gpUVlYCAIYPH44zzjgDJ5xwAqZPnw6bzZaghfAyPosX\nL8aCBQvw4YcfYsmSJdhtt93w61//Gn/961+Rn5+fzC4TERH1iMmkweHMSnU30panriFxJSIi6ndC\n9N3knD2RTn2hzJTUQY17770XlZWVmDlzJq6++mocdNBBMJm6NyP86NGjce655+Lcc8/Fxo0b8cgj\nj+D+++/H448/jgsvvBC77757MrtMRESDnBY1Uaj8s2hRJgrNRZFRDkYCZfNdOdAhAAhkKefZlD97\ndrWsTBRqNYfTNpRLQFNzVdS/nTFTZmBMEKopS9bB55PlNpmKIrzy2n5f+KJtIXm9tpC8nk+JAw7o\nynmabC8o2sJdEMo1lABiXRdoamqBppkglDYEJwclIiKiJEvqGnQHHnggli5dirfffhuHHHJItwc0\ndjRy5EjceuutWL9+Pa666ipkZ3MyNiIiSg9MPYnPU88oDSIiIuofSY3UuOeee5LZnKGwsBB33nln\nn7RNRETUXSazCTl5doSUiS9Jqvc0Jq5EREQpIaBBF2k0USjTT6iX+nxJVyIionSkGTO/yzQMk0n+\nWTSb7EbZLuTcGT744XQ6EEAA5si5NuU8q0l+ULSpK56YlPQTS7gclX6irnJiiRPpqH7yay/7lBVP\n/DIVRfhkOSTn7UYgsuqJT005UT7c+pVxmoCS6BxUVjrRI4M5ut5x9ZNgMITm5lYjrUcItY4ep0xE\nRETUM0lNPyEiIhoMmHoSX0M9ozSIiIio/zBSg4iIqBvMkdQTiq2egxpERGlNB6AjjdJPUt0Bynj9\nOqixefNm/Otf/8L777+PNWvWoLGxEaFQ5/nIH330EaZPn95PPSQiokEjkh6hrn5i0tQ0Ejk5tVn5\nc2l3WRDSIikVkewMi/LZ0K5kkajpJzY1/cQaWbnEppxoVspanEBK5W+mFoq0p65+oqaftCnpIj41\nvSTcQZ9uUvbJJgJKiktAlwcCyuonIdEx7USDCcFgEM1NrVGrnOhqXa5+QkREREnWb4MaTz/9NM47\n7zy0trZ267xEgx5ERET9KZ+pJ3E11DelugtEREQ0yPTLoMaKFStw1llnIRQKwWw2Y//998fUqVNR\nUFCQcNnXYcOG9UcXiYiIEjKbTcjJzUpccZBqaGDqCRFRuhMivKWLdOoLZaZ+GdR4+OGHEQqFMHz4\ncLz55puYMmVKf1yWiIgGKS1urnDHgXRN2aeueJKtFRhlPZI2kZefDWEKtWedwBpZFcWqDNDblEvY\n1dVPzDLy0BIr/cSq/Ek2xx7w19TVT9ojGQNxVj9RckpCfiX9JBhOPwnocl9AadavZIj4lXSRkOaX\nbcdIPxG6huZmH0wmK0JBv3JE9kNAuVDkU2zUPiIiIqJu6pdBjR9++AEA8Je//IUDGkRElLG46kl8\njY3Nqe4CERF1gRAadJFGE4VybJt6qV8GNfLy8gAAkyZN6o/LERERSVrnURvqRKFmZaJQO+QAhk9r\ng8mkwZYf3ZYtUt+mTPKpThSaZVYiNSxKpIY9vF/7f/buOz6u6s7///tOVW9usrGNwYVOSCihBQiE\nFkwLhBY2C4SEX1gghQ2pGyAQQqghhE0FQs1+2RAIoYVAsjQDpiRUYwzY4F7U29R7f3/M6J4jNCNZ\n8kgzkl5PHvfho3vPnHtmZDzSZ87ncyLW23DIemDQagfyzL93pYZde6pPoVBz71TS3CeRzjzf/IVC\nzeqMlLXKImUVCk3JWh2S1d7WqUD2NU279qoOioMCAICRM3BBiwLZbbfdJEmrV68ejdsBAFBwVTUV\nAwRIJjbX9dTexkoNAAAw+kYlqPHVr35V0WhUt9xyy2jcDgCAgquuJfUkn46OTnlUegOAMcGTU3IH\nsCVGJagxa9Ys3XXXXXrsscf0ta99TfF4fDRuCwBAH479nxPIHiH/CAai/uEo4B+uk1JFTViuUnLl\n+UdQjoJyFA6YIxKQf5QF0v4RDqb8IxD1FIh6ciJB/1AkZI5gwByOdfSWrPc8yXUzRzLtH17KHG7S\n849UyvGPhBtUwg0q6Trm8OQfadfzj5ST8g/XM4f/emZfw/b2LqW9lH941iG5eQ4AAIAtNyo1NSTp\nhBNO0JNPPqmvfOUrmjNnjo499lgtWLBAFRUDf/J1wgknaMqUKaM0SwAA+qusKlMgX20LkHoCAACK\nZtSCGq7r6qWXXlJnZ6fWrVunX//615v1uD322IOgBgCgqEg9ya+zs0tpu1gpAKCkuV5p7ThSSnPB\n5uvs7FQ4HFY0Gi32VEYn/cTzPJ100kn6+te/rg8//HA0bgkAwCCCkoIKOCH/iAZr/MNWWReV66Tl\nOmkFvaB/hAOZIxqQf0QCnjmC5ohGUv4RjErBqKRoyByhoDkC1mHzXHP0pp+k0uaIm8NNyD9SqZB/\n5Eo5SaTNkfQ8/0hZ/6Wto5fjBNTe1iXHCcjz0uawknT6zBkAAJS8t99+WwceeKDKy8u111576Ykn\nnujX5+tf/7ruuOOOIsyuv1EJavzP//yP7r33XknSscceq4cfflirVq1SV1eXenp6Bjx233330Zgi\nAAA5VVRGFQoFB+84QbW1dRR7CgAAoECSyaSOOuooPfXUU4rFYnrxxRd16KGH6uKLLy721PIalfST\n++67T5J06qmn6u677x6NWwIAUBCknuQX64kpmUwN3hEAUDI8z5HrlU6dKNJPSsu9996rtWvX6r77\n7tMhhxyi//f//p8uvPBC/ehHP9L69ev1y1/+Uk6JbXE/KkGNtWvXSpLOOOOM0bgdAAC5OWaBopNt\nBwLmrTDilPvtpJPZqau8NtxniKhnckfD2eKhYWshR1nA/HQWDZpf+MNhk37hRAPZP60Hhq23ZHtl\niP2Dg/2DXzo7XtLUs/ASpu0mTNdEyoyXcIPZP824KWvclGvmmXSS5naeNWA2laS9rVuOgtnpmOue\nZx7niZ9WAQAYK15//XV96Utf0nHHHSdJOvvss3XQQQdp4cKF+vWvf61IJKKf//znxZ3kR4xKUGPG\njBmSpPLy8kF6AgBQOqJlYUWio1ZTe8wh9QQAxh5PjjyVzifthL5LS3d3txYsWNDn3Lx58/Tss8/q\nyCOP1I033qhp06YVaXa5jUpNjWOPPVaS9MILL4zG7QAAKIia2spiT6FkJZMp9fTEiz0NAABQQNtt\nt52WL1/e7/ykSZP0+OOPa5999tEPfvADPfbYY0WYXW6jEtQ46aSTdOCBB+rqq69m9xMAwCgLWIfh\nKCBHAQWcsH/Y0l5SlTVRuV7fmhFBBfwjHHAUDjiKBOQf4YDrHxHrCEXM4UQdOVEnk7fSewQD5nAc\nc3ieOdKuOVKp7GF2P/FSrn+4yYB/JNPmSLhO9pB/JNOeOTzXP1yl/MPzzCEnoPb2LnlK+0faTfiH\n57n+0Yf1XHr/AwAApWPhwoV65JFH5Hn936Nramr06KOPas8999TKlSuLMLvcRiWoEQqFdP/992v/\n/ffX3nvvrd///vfq7OwcjVsDADAsoVBQ5eWRYk+jZLW38z4OAGORq0xxzlI6UDpmzpypI488Um+9\n9VbO6zU1NfrrX/+qww8/XJMmTRrl2eU2KonCJ554ot544w1J0oYNG3TmmWfq7LPP1owZM1RRMXBV\n+XvvvVc77bTTaEwTADDu2YVCM2+B4WCZOSdTULO6ttxfSeBYVeJDVh+/UKj1EUHUKhQaDpqVCoGQ\nOe/0FgIN5CkImk/KFAJVOtu2dh/xYuZ+qaSZVCJtFwrN3CdpLaJIWZ/GpGTukXKs4qBWkVUvLXV1\nxD6yEmNz2gAAoNRdd911A16vr6/Xo48+OkqzGdyoBDVWrFihpUuX9jmXTqc3a8lKT0/PSE0LAIC8\nqmvYyjWfzo6unMtSAQAARtuoBDVOOOEE7bHHHsN67NSpUws8GwAABuY4jqqqCWrk09bKricAMFZ5\nErufYFwZlaDGd7/73dG4zYiIxWK655579NBDD2nVqlWKRqOaP3++TjnlFH36058uqfuM1lwBoBQ5\nm/EDmuME+rVDARO8SCspSaqprpRjpYOEZVJUwtYYveknIevWYTv9JGBSOQJhz+6U+TNoPTCQp8yV\nneLhWuknyUzbS5pzXsJKI0nYKSdm7Hhv+ok1naSV0JyUSWdJe8mcU+rqSCjohBVz28zU7IKqHy0Q\nCgAAMEJGJagxVi1ZskQnnHCClixZ0uf8P/7xD/3mN7/R5z//ed12220qLy8v+n1Ga64AMBGwlWt+\nPd0xpdPpwTsCAIAx5dZbb9WTTz45Kvc666yzdMABBxRkrJINatx2222KRqM65ZRTinL/9evX6/DD\nD9fKlStVWVmp7373uzrwwAPV1dWlO+64Q3fddZf+93//V+l0Wvfee29R7zNacwWAiaK6hqBGPm1t\n7HoCAGOZV2I7jpTSXCa6Z599Vrfddtuo3Gv//fcf/0GNq666Su+//76OPvpoVVaO/g+XP/jBD7Ry\n5UoFg0E98sgj+tSnPuVfO/zwwzVr1ixdeeWV+tOf/qQHHnhAxxxzTNHuM1pzBYAxw0odyZeWEnD6\nvwW6XlIVlWUKBD151i4gQc+kcoSsNJHe7JGIlXISckzqRShk2vbtnGB2DDvlJN/uJ2k3d7t3JxRr\nGxM3YV82c05a6SfJ7E4u9kYqdvpJyrHST6xUlF4d7Z1yszuapNKmmLdHygkAAGPafvvtp1Sq/3v/\nSFiwYEHBxnK8UShffvnll2vFihWb3X/16tX+FjFvvvmmdtxxxxGaWW4bN27U9OnTlU6n9e///u/6\n/e9/369PMpnU7NmztW7dOu2777569tlni3Kf0ZprPi0tLWpoaMh+FZRKqOgQgImlT/CiT1DD/HLv\nBCJ+OxKqlyRVl21lzjnlapw+SVOmNijohP3ztd40vz0tUOe3G8szfWZXmrfSrSvipl1nCmrWb2UC\nAJGtM6mATmOtmXN9jWmXmxoest+me2KmnS3W6a0zdS0SK8w9WtaYdMOVbdV+e0V3VJK0usu8Rqu7\nzQ8w691Wv93srPXbKS+mZDKpt99arnS2fkZHbLW5d6rdTNm1gh1WcMh+Lh6l4QCMKk/K/nvU3Nys\n+vr64k6nCGpra9Xe3q7/3uUMzatsLPZ0fK7n6YgXfipJWr58uebMmVPcCWHMGZWVGvfff79efvnl\nIT9uypQpRflL/ec//9nPF/7iF7+Ys084HNYpp5yin/3sZ3ruuee0du1aTZ8+fdTvM1pzBYCJgtST\n/NpJPQGAMc/1HLle6XwQSfoJtlSeUuuFtd122+ljH/vYgMf222+vigpTgf7www/X008/3efcaFm0\naJEkKRgMau+9987bb//995ckeZ6n559/vij3Ga25AsB44Dhh/wgEQgoEQgo65ghFgoqUhf30il4h\n+z/H8Y9IUIoEpbBjHQHPP4LBtH84IfmHgk72CJjD5nnmcN3cRzItJdPy4taRkn+kUgH/SLjmSLqO\nkq6jhCf/SHmuf7hO2j8CcvwjqJC62hMKKizPS8vz0nK9lH9kPgHNHJ71n/1c+pwHAAAogFFZqXHX\nXXdtVr9UKqV77rlH5557rhYvXqxkMvdWciOtdweRmTNnDhhUmT9/vt9+6623dPzxx4/6fUZrrgAw\nEbBKIz/XddXZ0V3saQAAtpCXPUpFKc0FY1NJFQoNhUI67bTTFI1GdeKJJ+rcc8/VU089NerzWLs2\nk0M8WIrGjBkz/Pa6deuKcp/RmGtLS0vea62trUMaCwBKWQ1BjbwIaAAAMP7F43Elk0mVlZUpFBp+\nuODNN9/UypUrtfPOO2vmzJkFnGF/o5J+MlSf+9znVFdXp6efflobN24c9ft3dmZyhgdLfbGv9z5m\ntO8zGnNtaGjIe2y77bZDGgsARp0T8A/HSqgIB8oVDphimoFAQJVV5t/KoBP1j5AXMkfA8Y9cKSeR\nQNo/QiHPPwJhxz+cUFBOKJjZ/aT3CAXN4TjmsPVJQUlLblpeyvWPdMLxj6QbtA7HOjIbpiTTnn/Y\n6ScpJf3D1tEeV8AJK+CE/ZQTz3NzHgAAYGz6j//4D1VXV+fcgEKSbr75Zp1++un6v//7vwHHuf76\n63XkkUf6G4CMpJIMajiOo1mzZkka3gqILdW7jU0wGBywn319OFvfFOI+ozVXABjvqqsr8u6qCqmj\nvavYUwAAFICXLRRaSgfGjueee0533XWX3n333WJPxVdS6Se2DRs2SJImT5486veuqqpSW1ubYrHY\ngP16esyWdVVVVUW5z2jMtbm5Oe+11tZWVmsAKEGBnG3HMe1AIPMW2Ls9aWWNtY2qpJBntnQNWdvC\nhgMBq535MxowqxMiQdMOhqztTKPWD229DwzZ89yMH+rsEvGpbDtt7uemzBjJtBk76Vrns92T1vaq\nSc/MMxWwVmhku3R3x+WlpUD2tUxmt2z1PDtI7uZpAwAAjJySDGosWbJE69ev1y677FKUrUfr6+u1\nevVqNTU1DdjP/mW/rq6uKPcZjblOxH28AUw81TVDD05PFGzlCgAASlXJpZ98+OGHOvPMM1VeXq6b\nbrqpKHPo3Snkgw8+kOflr8e7YsUKv71gwYKi3Ge05goA41lFZblCg6TxTWQd7RQJBYDxwi3BA9gS\no7JS49vf/rbee++9Aft4nqcPP/xQb7zxhmKxmHbeeWfdcMMNuuGGG3L2v+qqq0Ys7WG33XbTfffd\np66uLi1ZskQ77rhjzn4vvvhin8cU4z6jNVcAGKvslBPHMW97TjalxPWSqqquk6uUAlaaSdB6i4xY\nY0SsjwN625GgCSpHrJSTUNScD0SsoEk4O7ZdVTxkXQ9YqShJK2BtF+FMZ+7jWdki6ZSZXMK12l7/\n9JOUlcqSzvMjZUAhJRIpJWOe0jKpJmk3nrm3/TgKhAIAgCIYlaDGE088oZdffnlIj3njjTf0xhtv\n5L3+ne98Z8SCGkcffbQuvvhiSdIDDzyQN1DwwAMPSJJmzpw5rEBBIe4zWnMFgPGshtSTvCgQCgAA\nStmoBDV23333YRXSHEh1dXVBx7N9/OMf1yc+8Qm98sor+vnPf66vfvWrqq2t7dPn73//uxYtWiRJ\nOuuss+QMo2R+Ie4zWnMFgPEqEgmrrDxa7GmULFJPAGB88TxHXgntODJABj2wWUYlqPHrX/96NG5T\nUNdcc40+85nPaO3atTrmmGN0++23a+utt5aUWXly6qmnSpJmzJih//zP/8w5xvHHH6/ly5erurpa\nTz/99IjdpxBjAMBY5fTuGmIFbJ0+O4lYKSWBSL/H26s07JSTkKzdTxx79xMzdm9GSdSxdz8x6ScB\nK/1EfdJPsm07/SRo5bU4eUpepcx9vGweSe+fkpRKhqyuVvqJvROK13vdzC3l5Nnq2w2opzOloBNW\nwjXFQl03098j5QQAABRZSe5+Ugo+/elP6/rrr9fXv/51PfXUU5o7d6622WYbdXV1ae3atZIyu4I8\n+OCDeVeNLFmyREuXLu23cqLQ9ynEGAAwUZF6kl9nR8/gnQAAY4qn0irOWUpzgbFkyRI9/vjj/c6v\nWbNmwOsf7TcaCGoM4IILLtCuu+6qSy+9VE899ZTeffddSVJlZaU+97nP6YorrtDMmTNL4j6jNVcA\nGE8CgYCqqiuKPY2S1d5GPQ0AACai6667Ttddd92wr48mghqDOOigg3TQQQepq6tLq1evVjQa1YwZ\nMxQOhwd97P33369YLKbgZmwTuCX3KeQYADDe2LufBAOmdoantKpqKvpcDzllfjvsmVSVcLB/yokk\nRR0ve858zhQOm1QOO9vFsR8Yyf67bO94ki/lRFYKi2t9npXqTT+xTqXNeHEr5SRu5U4nstkxSSv9\nJOkkct65uyPh7wgTt9NPvN7naFJtPJEUDQAARl9Bgxpr1qzRjBkzCjmkr7OzU67rqqamZkTGH0xl\nZaUWLFgwpMdsv/32o3KfkRgDACYCUk/y6+6KKZ1mUTAAjDeuJLeE4tClNBdIZ5xxhvbee++Cjfep\nT32qYGPlU9CgxnHHHadtttlGl1xyiXbYYYeCjNnd3a2bbrpJV111lR555BHtscceBRkXAACCGvm1\nt7HrCQAAE83++++v/fffv9jTGJKCBjXKy8t1zz336I9//KOOOeYYffGLX9RRRx2lSKR/tfnBvP76\n67rjjjt02223acOGDQoEAiovLy/kdAEA45WVyhFwzFudnWpSURFVMNg35cPuG3Kttr3jibWxSm/a\nib3jSThsVjcEotaOLBHrLbd395OAveOJNbC9v13KjC1r5YSXPe/GzeVE0qSfJFyrbQ3Rm3aS8MzJ\nlGPaTjZVpbO1R66VXpJMm/oanjfI7ifszwcAAEZJQYMaDz30kK688kpdd911uv/++3X//feroaFB\nn/3sZ7X33ntr77331o477tgvOJFOp7Vq1SotXrxYL7zwgh5//HG9+uqr/vWDDz5Y1113nXbaaadC\nThcAMIHV1rIbVD6JeFLJREp9dsYFAIwLnhx5JfQPPGFwbKmCBjWqqqp0+eWX65xzztFll12mu+66\nS83Nzbrzzjt15513+v3Ky8vV0NCgaDSq5uZmtbW1ycvxqc5+++2n73znO1q4cGEhpwkAGJcCOduO\ntfoiYJ2vqckENRyZFQ0hzxRWDlnno9aKirBd77O3UGjArFgIhkzb6bOsw3rL7S0gna+QtL0CIm0t\ns4ibqqBeLHM+FTdzS1iFQmNpqzioNVw8u1IjZd0j0KdIaUhdbV0KKqykzDKQtGcKoJoVGoPX3KCA\nKAAAGEn5Sq1vkVmzZuk3v/mN1qxZoxtuuEG77767AtYPhD09PVq9erXef/99tba29glozJw5U//x\nH/+h119/Xc888wwBDQBAwUWjEUXLhp4aOVF0UE8DAACMESO6pWttba0uuOACXXDBBWpra9Mzzzyj\nl19+WevXr9fGjRuVSCQ0efJkTZkyRfPnz9eBBx6ouXPnjuSUAAAg9WQAqWRaPd25t3gFAIx9nlda\nO46U0lwmultvvVVPPvnkqNzrrLPO0gEHHFCQsUY0qGGrra3VUUcdpaOOOmq0bgkAmKDsgqDBQP8V\nGbV11X6foGNSToIyfSOOnXJiUjnCfQqFZtNPQiZFJBgxP505dq6KVWy0T4HQXOytVO1CoUkrBSSW\n6WOnn8Ss9JO4G7DaZohEOjO/uEzgwrXSSGLtKYWzr0Ob12n6eFbqS860E7Z/BQBgLHv22Wd12223\njcq99t9//7EX1AAAoBSEQiFVVLCbVj4dbT3FngIAYAR5EoVCkdN+++2nVCo1eMcCWLBgQcHGIqgB\nAJhQamqrij2FkuW5njo7CGoAADARnXnmmTrzzDOLPY0hI6gBABhzHPsTJsfpd86xUkrsVBRXrmpq\nK/vsyBG0dkcJW7ufhAPWrih9Uk6stpNJuQgFTIpIIOzl7mynovTuemKnpNi7gOVLOYmnrXbm3smk\nmX88bdox14ydtIZLZe/jOiZdpHenl/b27r6pKOk2v512TbqKv/uJR8oJAAAoLoIaAIAJw3EcVVez\nUiMfdj0BgPHPpVAoxpkR2dIVAIBSVFtbLccpnTziUuJ5Umc7qScAAGBsYaUGAGB8sNJMAlZKiWPF\n72tqKv3Uid7z9o4nIettMWwFP/qmnHjW+ezuJ2Er/cTabMUJ2ekn1ltu73kn32cL+VJRTDsdz/wZ\nT1gpJ2kzXsJKP7F3P0m6vc/fXA94QXW2x+Skg3Ids8tJ2ov7bde1C4els7Pk4zUAAFBcBDUAABNG\nTW11sadQskg9AYCJwSP9BOMM6ScAgAmhurpSwQBve/l0spUrAAAYg1ipAQAY4wIf+VNynP5vb7W1\nNX12QnGcTApI0NopJeLau5+YvhF795OA+UgpGsikZITDJr8jELV2YSmzU06sdu/YzmbsfpKydzwx\nbTeeeWwynTv9JGYNkUibsRNu9oJ161SHp1AqrJCkjkCLOW/teCJrVxSPXU8AAECJIKgBAJgQautq\nij2FktXBKg0AmDA8OfJUOkWzyT7BlhqVdbjJZHLwTgAAjJDKygqFw8Tx8+lopZ4GAAAYm0blJ7xD\nDz1U2267rb70pS9pv/32G41bAgAmmL6pJabtyVVtXZU89U2ZCDqZbUpCntmuJGylrUSCuXc/KQua\ncaLBTCpHyNr9xCmzc1VCudvZAIsXNAM7KWt3kTypKJ69+0kic594yoxh73iSsDdNsaqwpbI7lzhe\npm9PV0JeylMg+6ldt2fST9Ku2f2kb8pJjvQTj8/aAGAscCkUinFmVFZqdHZ26tZbb9X++++vHXbY\nQddcc402bNgwGrcGAEB19bXFnkLJIvUEAACMZaMS1DjhhBM0adIkSdLbb7+tb33rW5o5c6ZOOOEE\nPfzww0qn04OMAACY6BzrPznm8P9zwv4RcEL+UVFR7qee2GMEFVJQIYUU8Y+wE/CPSNAxR0D+URZ0\n/SMaSisaSitU5vqHUx7yD0WtIxL2Dy8UkhcKSQHHHLZ02hwp1z+8uOcfyURQyURQ8bQ5YumAfyQ8\n+UfSdf2j9/mHFVJYIcVbE0op7R9Jt9s/XDdlDs8chmsdhmf9BwAAMJJGJajx3e9+V6tXr9Yf/vAH\nHXzwwXIcR8lkUn/605901FFHac6cOfqv//ovLV++fDSmAwCYQFilkV+sO6FUkg8WAGAi8dQ3JF0K\nB7AlRiWoIUnRaFSnnHKKnnjiCS1btkzf+c531NjYKElatWqVLr/8cs2dO1ef+cxn9Ic//EGxWGy0\npgYAGMfq2fUkLwqEAgCAsW7Ughq2uXPn6ic/+YlWrlyp++67T5/97GcVCATkeZ6eeOIJnXbaaZox\nY4YuuOACvfrqq8WYIgBgrHACkhNQwIn4R6/KygqFI2H/60Ag5B9BJ6ygE1bEi/hHNBDwDzvlpCLo\n+UdZwDVHNKmyaFLBSvmHUxH2D5WXmSMSMUc4lDns9BPPNUfaOhIp//Dirn8kk0Elk31TTuKu4x+x\ntMzhuv7RmxYScUJKtqVV5kSUcOL+kfLM0TflJG2O3nkCAAAUWVGCGr1CoZCOO+44PfTQQ1qxYoUu\nueQSzZ49W5LU0tKiG2+8Ubvttpv22GMP3XzzzerpoZgZAGDzkXqSX6yL1BMAmIg8zym5A9gSRQ1q\n2CZNmqQ5c+Zo5syZ/a69/PLLOvvsszV37lzdcccdRZgdAGAsIvUkv45WPigAAABjX6jYE/jnP/+p\n3/72t7r77rvV1tbmn589e7bOPvtsVVVV6ZZbbtEbb7yhtWvX6otf/KJaWlp0wQUXFHHWAIDiCvRr\nO44553muqqoqFQoF5Vh9A45JRQk6UUlS1DXpKpGA6Vtm3SIaNKkWZSGz+0ckmmkHKkxnpyxiPdDc\nT2GrHQj2TtScc6120tzDS5jVFOm46RJPZN7CY27QPxezMkISaTNe0koVCWRfj57WmILKfDrWFtjk\nX08lTbDDI8UEADDGxWIxvfLKK1q8eLFeeOEFvfHGG0omk5KkF198UdXV1QM+/r777tN3v/vdzbrX\nM888o8mTJw/Y54UXXtDtt9+u119/Xe3t7Zo+fboOPvhgnXHGGZoyZcpm3acQY4wnRQlqtLe36+67\n79bvfvc7vfzyy/75YDCoz372szrnnHN05JFHKpD94fIb3/iGFi1apG984xtavHixLrnkEp177rkK\nhYoekwEAlChST/Lr6YwrnSJgAQATUantODKSc3nsscd01FFHKZVK5byeTg+ehtnW1qalS5du1v3y\n3UeSXNfVBRdcoJtuuqnP+VdffVWPPvqorrrqKt1111067LDDRnSM8WhUowKLFi3Sb3/7W91zzz3q\n7jYV17faait96Utf0pe//OWc6SeStO++++qRRx7R7Nmz1dLSog8++EBz584drakDAMaY+jqCGvm0\nt7DrCQBg/Ovu7lYoFNKee+6pvfbaS3vttZf+/Oc/65577hnWeM8995zq6uryXh9olcRFF13kByMW\nLlyo888/X1OmTNFLL72kSy+9VKtXr9bxxx+vp59+Wp/4xCdGbIzxaFSCGrfccouuueYaLVmyxD8X\nCAR0+OGH65xzztHChQsVDAYHGCGjoaFB8+bN06uvvqqWlpaRnDIAoAQ4soqHOU7O8042pcROP6mp\nqVYo1P99JWi97YW9TPpJ2DHnokEzbpn1DlkRNKkclWEr/aQ88/lSoNzqXG6lmUSjftMLW31601xS\nSXPO/rTITj/pMX1S3eY5xlKZ8XrS9jlrCCudxS7BFlJA8fakXJnrCc8EOVKuyXHxPGseViqKZz0W\nAIBSddhhh6m9vV1hKwV00aJFwx5v3rx5g6aX5PL666/r+uuvlySdeuqpuuuuu+Rkf675+Mc/rsMP\nP1y77babWlpadN555+WcYyHGGK9GpVDof//3f/sBjcbGRn3ve9/T+++/r4cffljHHnvsZgU0ejU2\nNmratGl9/mICAGCrb2CVRj5dHXG56VJaeAwAGE2eMmWcSukYKRUVFSXxe+O1114r13UViUR0/fXX\n+8GIXrNnz/brdjz33HN65plnRmSM8WpUghqBQECHHnqo/vjHP2rlypX68Y9/rK233npYYz366KNa\nt26dPvaxjxV4lgCA8SAQCKiull1P8ulo7ir2FAAAmDBSqZQeeOABSdKhhx6qadOm5ex3+umn++37\n7ruv4GOMZ6OSfvLII49o0qRJo3ErAMBEYKWaBAORPpfq6mr8QtOSFLR2PAkFyvx2RJnUkLKAWS3Y\nJ/3EWkRYHjCpIWVW+kmwPPPxklNpfQpUbu7RZ/cTu7h17/ztj6cSVipKPOE33R5z72TMTKo7m37S\nnbLST6wMlu6UnS6SkU67UldalcGQ1qc6za0903btlJM+5dtY3QEA44EnlVQSYSnNZSQsXbrUL52w\n//775+03ffp0bbvttnr//ff13HPPFXyM8WxUghoENAAAo6Whob7YUyhZXW0xed54//ERAICR8a1v\nfUvLli1Te3u7ampqtOOOO+qII44YsKSCXVdy/vz5A44/b948vf/++30eU6gxxjP2RAUAjBGBAdue\n5yocDquqqiLvSo6gTDviZlZqRMKmr10ctDxgViZUhMzqhbIys6IiWJlZ2eFUmIKg9uoMz16dEbTa\n2aKbjlUQ1F6d4XVaKzU6zTxiMbMKpCeV+eGpO21Wl8StxRQpq7BnKFsqtKelR6FsDm5LYJN/PZnq\nMffLUxxUXo5VGwRIAAAF1NbWNuCGEPX1xf3g4ve//32fr5999ln99re/1Xbbbae77747544j69at\n89vTp08fcPze662trYrH44pmC44XYozxjKAGAGDcaGioK/YUSlYq5aqnM6GqEG/9ADCRuZ4j13MG\n7zhKXCtAvttuuw3Yt1irDT/+8Y/ruOOO00477aStt95ajuNo6dKluvPOO/XII49o6dKlOuigg/TM\nM89o11137fPYzk6T4lleXj7gfSoqKvo8rjcgUYgxxjN+sgEAjBuTJjUUewolq6O5e/BOAACgj1NP\nPVVnnHFGv/O77767TjvtNP32t7/VOeeco46ODp199tl64YUX+uxMkra2bLdrfuVip7CkUmblZCHG\nGM8IagAASooj69Mj64cC+7xjFf90sqkmVVVVKivLfBoRcMwbesCxUk5kPt2IZlNRokHzw0G5lQ5b\nGbLST6zioOEy6weLquzbaLlVENT+RCRiFTG1fwZJZsdO5i4O6nWbdqrLPO+uuLlPVzozWbs4aCKd\n+xOssmBQqbaEKoIhJbPFSWMyn/okXSv9xLV/ALIGBwBghP3rX//S7Nmziz2NPgZb6fDlL39Zzzzz\njG6//Xa9+OKLeuWVV7T77rv71ysrK/12LBYbcKyeHvN+XFVVVdAxxrNR2dIVAICRNmkyqzTyiXUl\nlYxPjE9rAAAD80rw6FVbW6v6+vq8R6myt1JdtGhRn2v2vJuamgYcp/d6JBLpk0ZSiDHGM4IaAIAx\nLxAIqL6uttjTKFmdLaSeAAAwUrbeemu/vWnTpj7XtttuO7+9YsWKAcfpvb5gwYI+KSyFGGM8I/0E\nADA2WDuaBBzz9uV5rurr6xQImDfuoJWeEnbMstGwzO4hZYHMGOUh87hKO/0kaFIvKiImTSRcZdJS\nnOpsOkulGVdl1jLVkJWWYs1fbnbshEkzUXfcXO42qyoS3WZS3UnzvDtTmfF6rAyRnrSZWyCbruN5\nntKdCZWHMv0/jHVIkmJOu9837Zp7e3l2P/H6fJbW32DXAQAYr1pbW/32R1M+dtppJ4VCIaVSKS1e\nvFjnnntuzjG6u7v15ptvSupfMLUQY4xnrNQAAIx5k0k9yaurLSY37Q7eEQAwIXie5JbYMdY99thj\nfnunnXbqc626uloHHXSQJOnhhx9Wwv5Aw/LQQw8pma21dfTRRxd8jPGMoAYAYEwrLy8bdHuziayd\nXU8AABgxy5Yt07XXXitJmjJlij796U/36/OVr3xFkrRx40b96le/6nc9lUrpJz/5iSSpsbFRCxcu\nHJExxiuCGgCAEhbIeThOyD+mTJkkSXIU8I9gIOIfIafMP8pcc5QHgyoPBlURkn+UB13/qAyl/KO8\nPOkfwZqAfzhVUTlVUami3D+8sog5QiH/6CPlZo5E0hwxc7idrn/09ET8oysV8o9YOqBYOqB4Wv7h\neZ5/BAOOvJSrZFdCac/zj+ZAi5oDLUq4nf6RdhP+4Xop/5Bc6wAAjAduCR6l6q233tIZZ5yhP/3p\nT/0KdHZ2durmm2/Wvvvu66efXHXVVTk/aDnxxBO13377SZIuuugi/e53v/O3aV23bp1OPvlk/fOf\n/5QkXXHFFTkLfBZijPGKmhoAgDErEAiovqGu2NMoWe3NXcWeAgAARbXXXnupvd3UkVq/fn2fa4GA\n+Zz/0ksv1cknn+x/3d3drdtuu0233XabpMyOItOmTZPjOFq9erUfVAiHw/rpT3+qM844I+ccHMfR\nvffeqwMOOEDvvPOOvvzlL+ub3/ym6uvr+4zzzW9+U2eeeeaIjTFeEdQAAIxZkybVK+Cw6DCfzuae\nwTsBADCOvfPOO2pra8t5bdmyZX2+bmlp6fP1TjvtpDvvvFOPPvqo/vGPf2jNmjVauXKlf33rrbfW\nkUceqfPPP1877rjjgPOYNm2aXnnlFV1++eW6/fbbtWbNGnV0ZIp377PPPrrooot03HHHjfgY45Hj\ned44KM2CYmlpaVFDQ2+BvqCkibFtEICR41hBCkdm549AsNK0nYgkaYcdF6i8LLPMMxQ0O5BUhEzh\n0OrANL89zWv0243ZXUpmVpp/t7auMDt/zKnuNI+bbj7hKZtndjcJbD05O9hk/5xnbS3rhU1fJ2V2\nUHHasuOt3WD6fmC2gOtZFvPba9fU+O3lHaai+gfdmc8l1nSZt/GOlNkKJdCd1IYVmR/O2pPmef3L\nfUOS1Jx4zz/XnTBLatOuWd3huaYQmSdrm5UcPzqw+wmA0uZJ2X/HmpubVV9fX9zpFEFtba3a29t1\n0bbnaFb59GJPx+d6rr721o8kScuXL9ecOXMKOv6yZcv8VQyDaWxsVF1dXd7rqVRK69atUyqVUkND\ng2pqavL2HUxTU5M6Ojo0bdq0YdcGK8QY4wErNQAAY1J1dZXKotHBO05QHU0UCAUAYP78+QUbKxQK\naebMmQUZa9KkSZo0aVLRxxgPCGoAAIrOybfKy1q1EXDstyxXU6c2SPLkOJnHhgImwBEKmFUb5Z5Z\n3VAeMGNUhDKPqwyZFQbVIbOioarcrJaI1Jn5BWrN2KrKFOHyrGJc9uoMhcxKEyXjpp3K3iduVkK4\n3WYlR6LbPK4naebclTKvR0/2Q6e4a0qshbKvRSqZlteTUFn2OS7rMatOYk5mlUjSNakpnpey2mY8\nVl8AAIBSR1ADADDmRKPRLVryOd51NHWTDAgAyMmTI7eE3iVKaS4Ym6iuBgAYc6ZOnTx4pwnK86SO\nFgqEAgCAiYGVGgCAEmMVCnX6v00Fg0E1TGpQb2Hi3j5BK/0k4piiohWuKZxVGTZjV4czf9aETPGw\n2ohJESmvMikZgVprHtXWvu+96SdlVnGuSNi07eyNtEnrUDx7nx6TcuJ2mnnEYybFpTNpxutJm/nH\neqdn3SMaDKi7tUcR11PMul17wFR9T6QzqSiua6WcyOrcpz0w0lMAAECxEdQAAIwpU6ZMYhvXAVAg\nFAAwEM/LuYlV0ZTSXDA28VMhAGDMcBxHU0g9ySsZSyrWlRi8IwAAwDjBSg0AQNH4u544Tv9zkgJO\n3y1bJ01qUCgYUiBgdgcJZ3c6iTgmLaTCq/bblYGI364K9U8/qQubNIyaCpN+Eq03aRiBBrODimpN\n26vMprlEzT0UtN5ak1aAIWVSTdSTuY/Xae6XNhuUqDtuxutMm+faZTJU1JPq/9GW19aj6nDm9Vtu\njd2pJjONdKbeRto1c/M8a25enlQUPkoDgHHB1VASDUdeKc0FYxNBDQDAmDGtcUqxp1CyXNdTdysF\nQgEAwMRC+gkAYExoaKhXJBwZvOME1dHULc9lNQUAAJhYWKkBACgB1o4nVrqIYxUEbZzeqI/ueCJJ\nwWAm/SQasFJO0mb3k6qoSd+otWIi9dm0k/qoScOorDHtYIO1i0mtGU/VVvpJeXbXk3zBlqRJ63B6\nYuZ8d6btdpgUkViHmWdH3Ny7M2Vegx4r/SSd3XkkGMi8Jl3N3bL2ZdEmr92MLbP7SdLNrObwPGv3\nEyvlhB1NAGB88ySVUgy8lOaCsYmVGgCAkldfX6dohFUa+XS1xpROpgfvCAAAMM4Q1AAAlLzG6VOL\nPYWS1r6pq9hTAAAAKArSTwAAo8re3aR31xPHMakXH93xpKGhXmXRspw7nkhS1Mmkhtg7nlQFzBjV\nYRO/rw2bNa4NkUz6RW2FSQuJTsqz40mdGdurslJRotHeSZtzdspJ3KSXqMsq4tneLUlKt5nVFd1d\nJnmkI5U7/aTT2qTEzU61LJ1SRTqlimhAm2JmvDan2W8nUibo0bvriWulnwy19jwpKgAwdnnZo1SU\n0lwwNrFSAwBQ0qbPmFbsKZS0jo2dg3cCAAAYp1ipAQAookxs3S78aRcHnTJlqqKRTDHOQMCsXggF\ny/12eaBOklSVNisrqsvMqo66PsVB09b5zCqKqlqzmiI4yercYFZn9CkOWmburbBVTLRXwlqp0dVt\nzneY1RJuS2Z1SKLFPNf2uFld0p408++yFlSkrGpqwYCjVDwltyuu3lqoa+Lmfp2BJjOltHVvt7cY\nqlWDw7NXalhtj8/PAABAaSOoAQAoSYFAQNOns0pjIO2bulQ2eDcAAHyuV1o7jpTSXDA2kX4CAChJ\n06ZNUShE7D0fN+Wqs7l78I4AAADjGD8tAgBGXK7ioJlmJm8iGLBSOhRQKBTS1GlT+6ScRIKmQGc0\nUOO3K9xMmkh10KRv1FnFQesiVnHQqMnlqK/KFO6MTrbuPNkqDlpv7uHVWOfL7blmpU0qR9/ioNau\nJK2m9kW6JZMC0tVh1lm0xc1z7bCKg3ZYxUFTVjpIsL1LDVFHLXFzriXQ4rdj6TZzP9fMqbdAqGel\nnGxO4U+KgwLA+OB5pZVdWEpzwdjESg0AQMmZMWOaAg5vUfl4aY9VGgAAACKoAQAoMeXlZZo0qaHY\n0yhp7Zu65JGEDAAAQPoJAGBk5E05kdnZI+CnnZgY++zZsxUIZPqEAialJBww6SdVqvfbNcqkhtRG\nrB1PzMM0KWJSQxrKYn67sj6TAhKcaqWTNFi7nNRZ6ScVVvpJwHpevRIJ0+62Uk7aTMqJ29Tjt+NN\nmefb3m3veGLekjtT5h5pK3YRlCPP89TV1KXGaKbPG91m3HZno99OWjuepF0zP5N2Yu94AgCYKDyV\n1jtAKc0FYxMrNQAAJaO+vk5VVRXFnkZJ62jqkptmlQYAAIBEUAMAUCICgYC2mjm92NMoaZ7nqX1j\n1+AdAQAAJgjSTwAABbM5KSdOIOK3A04k2zWgmTNnKBKOKGinnATNqo3yQJ3frk7X+u26cGbXkLqo\nuV992CxmnRQxO3/U15jimuFpmbi+M6XazLne3MOrtFJOImZnklycuElrcTpMykmuHU8kqbsj8xxb\nE+a5tqXMa9Ru7XiSsFZlhDu7VR/ypJCjTdldTzbJ7HjS45p20jVpKW6f9JPs4J694NdqU4YeAMY1\n18scpaKU5oKxiZUaAICiq6go1+TJk4o9jZLmeZ46NnYO3hEAAGACYaXGAOLxuP71r39p8eLFWrx4\nsdauXStJ+uxnP6tvfvObBb1XOp3WX//6Vz300ENatWqVotGo5s+fr5NPPlm77rrrqI0BAMUwe/bM\nYk+h5LVv6lKIWhoAAAB9ENTI48wzz9Tdd9+thF3RPmvOnDkFvdcHH3ygU045Rc8//3y/a1dccYW+\n+tWv6oYbblA4nH/5cyHGAIDh6JNy0oe1GNAxbzehYHWfXo2NU1VZac6Fg2Y3kvKASTOpds2OJ7XB\nMr9dF82kbUw2WS2aGjX5G5MrTRpG+aSUmcfU7D3rzb29WrPjiaqs9JN8ktn7dJl7qN3UvHBb+u94\nIkltXZn5t+XZ8SRlZYMEHMl1XXVt6tT0MtPn9ZZMKk2Ls97cI9VuxkiblBjXM8+7N9XE0+ABks3p\nAwAYW7zsUSpKaS4Ymwhq5LFs2TJJ0p577qm99tpLH//4x3X22WcX/D6tra06/PDDtXTpUoXDYf3H\nf/yHDjzwQHV1denOO+/Uo48+ql/+8peKx+O6+eabR2wMACiGsrIyNTZOLfY0Sl7Hxt4dT/IFkAAA\nACYmghp5/P73v9fs2bMViWQ++ovFYiMS1Lj44ou1dOlSOY6j++67T0cddZR/7Qtf+IK++tWv6le/\n+pVuueUWnXrqqfrMZz4zImMAwFDlXKHhWAVBHbMyzF6d4ThmxcK222yjYDCicMCsvIgETN8KNfjt\nWpmVE/UR8/bVkK21OTlqViNMLjOrFGrqTTvcaC3nmJJZleE11PmnvGprpcbm6MmM7XRaO5K0mna6\nqX9xUMkUCG1N5i4OmrSqptUEpZ6Obk0uC2hdjzm/UW2Zcd0m/1wiba0SyVUcVLIKhFIcFAAAjH0U\nCs1j3rx5fkBjpLS0tOhXv/qVJOnzn/98n2BEr2uvvVaTJmWK5/3kJz8ZkTEAoBhmzJim8orywTtO\ncB0bO4g5AAAKxvPMDiilcgBbgqBGET344IN+zY4zzzwzZ5+KigqdfPLJkqQnn3xSmzZtKvgYADDa\nKisrNG3alGJPo+SlEil1NXUP3hEAAGCCIv2kiJ566ilJUiAQ0H777Ze33wEHHKD//u//Vjqd1qJF\ni3TMMccUdAwA2FxDSTkJBsqttln5FgyGtO02c+Q4IYUCmTSMqJWeUuWYrV3rXFMotCFqxpgUNfOY\nGs2kUTRaKSeT60wgoKzRTDXQaBX/nFQnSfJqzD0U2Yxiyum033S6slustpoCnW6zSQHJVRxUkloT\nmbdfuzhowgyrgJM537GhU7Uh8xHWv5pN4dFmJ7Mj11CKg0qDF/+kOCgAjG+eHHklVKOplOaCsYmV\nGkW0ZMkSSdKMGTNUXV2dt992223nt996662CjwEAo2n27K0UHuH0vvEg0ZNUd2ts8I4AAAATGCs1\nimj16tWSpK222mrAfvb1NWvWFHyMwbS0tOS91traOqSxAExskyc3qK6+dvCOUOuatmJPAQAAoOQR\n1Ciizs7MsuWKiooB+9nXex9TyDEG09DQMHgnAONWn5QTx14iGsieyp1yEgmbnUQ8z1V5eZlmzdpK\nQat/JJt2Uh60djlJm3ZD2KRsTCoziwunlpkUicayTF2hKdUm5aSy0ez2EZphrWKbUm/mVJcNrlRZ\nKSmbwbH+DXXaOzKNlg7/XHqj2XWkq91OObF2P0kFs+fMuGkr66M8HlOtl5LKHa3qMqkjG2SCzL27\nngxtxxPJT0Wh+igATEiuSqs4J29H2FKknxRRMpn5gTMUGji2FA6bXwB6i4IWcgwAGGnBYFDbbjv7\nI0ER5OR56ljXMXg/AAAAsFKjmCorK9XW1qZYbOCc6Z4eUxiu6iOfKBZijME0Nzfnvdba2qptt912\nSOMBmHi22WYWdTQ2U8emLkWS6cE7AgAAYOIENf785z/rxhtvzHltv/3206WXXjrKM5Lq6uq0Zs2a\nAWtWSH1rWtTW9s1FL8QYg6mvrx+8E4BxZbCUk8zpzAowO+UkGq7z2142zWHGVo2qrTH/joRDlX67\nIpDpX+uaHU/qgyZdriFqdlaZarI3NK3MpFY0VmbSL2qnmuBueCszJzVaKSeTTGqLV5NNjwlsxuqR\neNxvOm1mtxE1Z+peuBtNCkhsk3mNWrrNPJoT5i23I5m5Z8yKXUQCUjqVVk9TpyrCZi3uii7zvHp3\nPJGkWLJVEjueAACGxvNIP8H4MmGCGitXrtQTTzyR89pQVy4Uyrx58/TWW2/pww8/HLCffX3+/PkF\nHwMARkpDQ52mTp00eEdIktrWtssrpZ80AQAAStyECWocd9xx2n777XNemzp16ijPJmPXXXfVAw88\noPb2di1btixvsOHFF1/s85hCjwEAI6GqqlKzZs8o9jTGjHh3Qt1tbOEKAAAwFBMmqDFz5kzNnDmz\n2NPo46ijjtLll18uSXrwwQf1jW98I2e/hx56SJI0bdo07bHHHgUfA8DE1ifVxD/pWNeD1nnzthEK\nZtJIouH+KWrRaETz5s5VMBDM9jVpGL0pJ5JU402WJE1yzA4lk8rMPaaazUM0rcykVjRWmJ1O6idn\n2pGtTEFkZ7q5R98dT6zzUSufJRdrxYTTbqWctLSa8TZk0k+S683c2tpM+kxTzNyjJWmeV1e2u2ev\nud3Upvps2ZFl7SZ1ZJ2z0Twuu+OJZHY9GdKOJ5lOAgBMXF72KBWlNBeMTex+UkSf/OQntcMOO0iS\nfv7zn6u7u7tfnxdeeEH/93//J0n64he/qECg77esEGMAQCGFQiHNnbe1gsHg4J0hKVMcNBVLDd4R\nAAAAfUyYlRrFcNZZZ+nDDz9UVVWV7r///n7XHcfRVVddpaOPPlorVqzQySefrNtuu00NDZlCdv/8\n5z910kknyfM8NTQ06Nvf/vaIjAFg4hnK6gwnYHYtCQZMkc+ySP8VGsFgUPPmb61oJKqwVfCzPGAK\nFNd60/x2gzIrNBqi5u1oSp7VGdPLzS5Ok+tNYc6ymZl5B7aqMw+cagqCug1WTY9KM6fBOO2tpt1s\nFWPeYM6n12YCyV2bzCqRpm5zD7s4aIupNeqv1KiOOHKTaXW2dqrHWkzxQY95fi2BNX47ljL37i0Q\nSnFQAMBQuBQKxThDUCOPRx55RNdee63/teu6fa595jOf8b8uLy/XX/7yl35jLFq0SEuXLh1wt5GF\nCxfqhz/8oX70ox/pwQcf1KxZs7Trrruqq6tLb7zxhjzPU3l5ue677z5NmpS72F4hxgCALeU4jrad\nO1vlZWWDd4avdU0bxUEBAACGiaBGHqtXr867W8qaNWu0Zo355KyysjJnv8116aWXatddd9Ull1yi\nN954Q88//7ykzCeehx12mK677rq8RU4LOQYADFdvQKOyonzwzvDF2mOKdcQH7wgAAICcCGrk8dnP\nflZ/+9vfNqtvKJT7Zbz11lvV1dWlcDic87rthBNO0AknnKCVK1dq5cqVikaj2nbbbVVf339590iO\nAWD8yplyIklOsN91J2CCEyE7jSRHUVDP8bTNtrNUXVU5pJQTSZpclkltmVpu7t1YlvbbW5Wb3UCm\n1nX67cqZZvVcsDftpNGknHhTrFVpNeZ+g+o093Ba28z5jSb9xF1niobG1mXm3WQVB90UN+k6LQlT\nw6jLyhIJOZLnenKaTHHQ5zeYIp/rrZST7rQpDppMmbQU18sUCKU4KABgKLzsf6WilOaCsYmgRh4z\nZszQjBlbthXhPvvsM+THzJo1S7Nmzdqi+xZiDADYXNtsO0s11VXFnsaY07q2XXVJd/COAAAAyItt\nMAAAw9KbckJAY+hinXF1NvffrQoAAABDw0oNABjH+qScOHb6ScA6nU0/caL+uXDIBCoqIiaVw3Uz\nORSBQCBTQ6OyXKGgSVWpCJgUkBpvst+epBq/3ZtyIknTsmkn08vMioWtyk2NiWm1Jh2keoZJswht\nZaWUTM/c05ts5unV1GmzJcy4TptJLbFTTrx1JhUlscbkkbQ2Z2oqbYqZ4qhNCbNzTLuVcpLOrq71\nXE/hpjZNK5eWm6en1a65X5vW+e14qsOM4SbMnHrTTqzdTzZnCS/LfAFgYvPY/QTjDCs1AABD0rtt\na2UlRUGHo3Vdu9LJ9OAdAQAAMCiCGgCAzRaNRrRgu21UXs62rcMR74yrs4m0EwAAgEIh/QQAxoH8\nO5tYO5ooaJ03//wHszud2Ckn5WGTRuJm0xwqK8s1Z5tZCoUy40QCmdSLMmuXkzpvqt+e5Ni7nJj7\nTSmzdzrJpJ3MrDC7nMyoNekWtTNMukVoa2v77K2sVJNpmTQXr8HMWYE8r4ctu/bWaTVpH84ms9OI\n1pnzydU9frt9o0nT2dCd2fVkQ9zsctUUN/fusjYmqQp66tnYpvqo46eALO80425yVvvtnqS140m6\n/44nkuRl0076ppOw4wkAYGBe9igVpTQXjE0ENQAAg6qtq9bs2TMUCLDAb7ja1rQpzW4nAAAABUVQ\nAwAwoMbGKZrWOHnwjsiru7VHblts8I4AAIwwl0KhGGcIagDAGJMz1WSQnU0ybZM2EQyYmhhlkXpJ\nUjhgCn+6XlKO42jrOVuprs6kl0QCJqWkMrvTSU263j83KWhSWOyUk2lWTdHGMlMkc0ZZ5hf96XW5\nU07CsyvM/GdYKSdTrXZd9v5BK71mMzjtrZk/m5rNSSvlJLXabE3Svd48l00dJg2mKZt20po0r3mH\nteNJOCClk2l1rmvT1uXmp7ZFGzKdVjtrzePSZseThJVykk6bYIi/44kkeb2rPgZPOWHHEwAAMF4R\n1AAA9BONRrTNNrMUtbZfxfA0rWyRV0ofiQEAAIwjBDUAAH3U1ddoq5mTqZ9RAO3rO5ToTg7eEQCA\nUVRKKR+lNBeMTQQ1gAki7+4YGFAxl+1vzo4mvakmdl8nYFZXBKyUk1DQpHJURs0uJb08pTRjq2ma\nPLleAcfs5hENmJSSKpm0j9p0nSSpIWRSWSZZKSeNfVJOTE7GjHKTTtFYn0k7qZ5hfvEPzbJSTmZa\ntTymmbY3ycxDUfMcB9VpUkqcpmyqyQaTcpJe2+63e9aY13RTq0k52RAz92tKZJ5vu50Vkv0rE+uI\nq6KjUxXZl2dJm/m79GEqc89WmR1PYqk2v51MmW1fXc+8dp7dJqUEAACAoAYAQCovj2rm7FkqKxtC\ngAB5pZNpNa1q1UxeTgAAgBFFUAOYKBxWauQ0yJrHUV/hkvf7lK/4Z+afcXtFRjBoFQENWUU+QzV+\n2/NMsc4pU+rVOGNKn0KhZQHTt8qt89t1MoVCG6KZFSGTy8zcppWZ13O6tTpjeoVZeTClwayWqJyZ\nKXIZmmXup+kNZp52QVB7dUa5tQxkMD09fjNgFwXd0CRJcle1+qfiq0zRzeYWs0JlfY+538aEeets\nydY0jZuXU5OiUtPaVk0Ju2pLmu/ne52m+Of6wIeSpK7kRv9c0ioO6nqmWGru4qCSXyCU4qAAgCFw\n1afEdNHxboUtRVADACaoSCSsWbOnq7JqCAECDKp9XbsSXYnBOwIAAGCLEdQAgAloypR6TZs+RYEA\nK3gKqbutR05T1+AdAQAAUBAENYAJg50scsr5O/1ILsrM/X3w01ycQM6+jlW4M2gVAg0GMqkm0ZBJ\nCykPm/QNW8qNq6wsqpmzGlVdaVIrIoFMEcwKmcfVpk06SK1VCLQ+YlJfJpdl5jwlal6v6WVmhcL0\nSpNyMmmK+UW/bLZ5XsFZ9ZnGNCvNZIqVflJvpZxEzGswKCvlxGlqMufXmXQPb3WmWGdilZlz80ZT\npHRdl1nBsiFm7t0UM39purIZNtVhKRVPq2tDm6qsd9ZXmuJ+e3Vgld/uSK+TJMVTHf65dNoUUO2b\nckJxUABA4bhe5igV7H6CLUVQAwAmgEAgoOmNUzR5Sr0c6qsUnOd6avmwmR/MAAAARhlBDQAY52pr\nqzRj5lQ5wVIqCzaeeGpZ2aJUIj14VwAAis0rrdURpTQXjE0ENYAJwk5fGDmF+KV5pNJkhjI3k2LR\nd7eJQTibM3c7paR/eoljXQ9YaSYha0cTO9WkIphJzwhY39+Ul0l5KCuPatbMGaqszDw2JLNDSplM\n+kmFm2nXBkzqRW2ZeXtoiJo5NZgpaVo0kxYxrcykWEyrMWkmtY0mnSIy0zwwMMtKj8mmndg7m3h1\ndeb6UGp+5Ek5cdZaKSdrzO4niQ8y/ds2mNd2Q1elacfNnDclzDxarcyQqrDUtrZdU9IxKZut8tR6\n83dmhbfOb7e4K/12LNkqSUpZKSeunWaSN+XE+vuY46dA0lMAAMBEQ1ADAMaZcDikaY2TVN9Qo6DD\nP/MjqaupS11N3ZpSOXhfAAAAFB4/7QLAOBEMBjRlWr3qJlWKshkjL9YZV2Jte7GnAQDAkLga2ZLo\nQ8UaQ2wpghrABBEMlA/eqYC8IbxdOqOwM8vmzMfLlWqSJzjg5Eg1cfKkluRLOem7i0kmNSRsp5kE\nzQ4kZU6t3w45Jo0k7SXlOI6mTmvQ5Cl1CgQDClr/tIc807faNWkrVQFzviaa6V8fMXOrN5fVEDGv\ny5SIyb2YUpFJ35hUZ1JOqmaYvsGZZvmCM8PaxWTaZL/p9qadVJrUlyHryuyyEti0yZxblzvlJLnC\n7MjSvi7z+q9rN6k462Pme9KUMK9Hm9kgRWVBKRVLqnt1i7atyPwo9ry5nd5NmNSXJn3gt7tT1jzS\nmXm4nhm4744n9t9FUk4AAADyIagBAGNUIOCoflKdJk+pUzg8GjVTIEnpZFqbPmiWV0r74QEAAExQ\nBDUAYIwJBBxNmlKryVNqpUApLSAd/9y0q5YVzUoned0BAGOTx+4nGGcIagDjmGPlTkTCNTmuD572\nMZQ0klKWM7VkgPO9cqWZfPR87+sYCJjVEnaBzqCV6mGnjkQCJu2hSpk0jKhypwnF1aNwOKhJU2vV\n0FCrQHZXkJAy9wy55t6VnhmjImjOV5WZXV1qI+bvRn0248JOM2mImN03ppSZHToaqkz6RtXkTOpE\nZIZ5roEZ9WbS06wdTaaalBOv3uoz3BUmnZ3mnr1pJ+tM+om3ykr1WGl2RWlbY9JL1rdlXv91VsrJ\nxrh5jZrNpi4KOZLnZbZunRVOKvuy643WzOv4Xk+r33dDYIXf7kqavJRkyqTpuG4i+6d1k7w7ngAA\nACAfghoAUOLKyiOaPKVKNXUVchxHgXyFPjBiPM9T08oWxbsSUvXg/QEAADA6CGoA45m1BUZtdPaI\n3MJTuqDjOQoO3mkQueaUb0WGa61ECeRYudKnsKfMqoKAY9ohZYt8OqbIZ8RaLVHjmiKfYeuf3ahn\n2kkvM+e4TLHI6tpy1U2pUkVlVCEFM+XBvb4rcCqzKzHKQmaeVWHzGlZbCyFqrXZ9xLxGk8KZFQKT\nrBUZ9VVmdUPVJFPMMjzVej2mZVf/TK0zA08ybW9Sg2nXmtdg2No7/GafoqBrM6sh3D6rM8wKiLZ1\nZnXMujazOmZtLHN+Y9x8H+zVGbbKllaF3LhUJX3YZV7/ZZ2ZlStrgx/65zqS6/12PGXmnHbN6+t6\nvTcyf//6rs4YuDho//4AAGweT+x+gvGFoAYAlJBgKKC6SVWqm1ypUGjLAzzYci1r2hROxAbvCAAA\ngFFHUAMASkBlTZlqGioUqQ2TXFJC2ta3q6u5W6oavC8AAABGH0ENYBxzrBSJud7H/PZQajK4gywK\nHOz6UBWiXkTvnNw8iys9J/d5x8ukVkSsfxqDnlktEbRSUcqtPrWRUPa6mXvAehqJtHmNulIm7SMQ\nCaq6vlzVDRUKhAM5xjDtCiu9pDxo2pXZb3GV9a95TcjcryZs7ldvFf9ssFJN6qozqSblDSb1JTzV\neg2mWEVmJ1nthkxKiddgCn96Ndb1Amwz67S1mfYGU3SzN+VEktyVrZKkxEqTJtOyvsJvr+807XUx\nk4qyKVsUdJOVcmL/zajtaFe4p0uTq6SWhPlevNVqXrtVzkpJUltqtX8ulmr126m06Zt2zfy8bFFQ\nz7NTpUg5AQCMPM/z5JXQliOlNBeMTQQ1AGAUBYIBVdWVqbqhQuFy80t/oYND2DJt6zMBDQAAAJQ2\nghoAMMKcgKOKmjJV1pXLqQj5BVxdPpkoSW3r29WxMbNCAwCA8caV5JbQjyD8OIQtRVADGMccxyy1\nP3By/QA9N0/ve06+N8KhLI138qSZBApQUKJ3fvZ87Dl7OfpKUjYDRDVW1kS1lcoRc83k7Md1JDPn\nO1NW37Sn8poyVdSVK1wZleP0f2J2qkNZMHO93PpXOWptxlJpna8MmvtUZ9NLqkMmtaQ2YtJIastN\nbkVVtUmFiE4ydw9NjUiSApPNbiW50kwkyauz2tXZPsHCFjR1mltMe6OVcrJqg99MrzJpKfGVmdeg\neaOVctJlIhLrYuYb2ptyIkktJhvEV9/ZprJYl6ZVS61WysmrTabzCplUkxY3k34SS5n59E05MW3P\nM98X+bvxlFL9eQAAgLGHoAYAFEgg6KisukzlNWVyyiNyshGaFL+3jgmta9oUSZByAgDAeBKLxfTK\nK69o8eLFeuGFF/TGG28omcx80PDiiy+qurp6s8d64YUXdPvtt+v1119Xe3u7pk+froMPPlhnnHGG\npkyZMqbGGE8IagDAFgiGg6qsLlNZTZnqyiL++XiatZRjhuepeVWretpi7HICABj3PKmkKnmN5Fwe\ne+wxHXXUUUqlUjmvp9PpnOc/ynVdXXDBBbrpppv6nH/11Vf16KOP6qqrrtJdd92lww47rOTHGI8I\nagDjWCRkUgjO222F386RCdFHvtxGz8vWgrDSMAbra3Oc3J3zzScQ6N/fHsO+R6452df79LXaqZTJ\n8ehOZIIS7QkTnFjVXea3mxOZ1IVwZUSxsnJFq6MKRUKKZ98PU31SWcw9wtbzy5deUhbMLOeoDJpl\nHRUh066y0ksqwyaNoaoskxZRXmHSI6K15g061GBuEmwwz8WZZP323ruLSX2df8qrM21VmrSOgktk\nnovTtMnMbYNpa22T30yt7vDbsZWmS1NT5rms6yr3z62Lme/hpoR5DdqslJNwQPJcTzXNzap041K1\ntDFuvlmvNJnX+X1vrbmft8Jvd6eaM08jZeaWP+XEfA9zpmqx4wkAAAXV3d2tUCikPffcU3vttZf2\n2msv/fnPf9Y999wzpHEuuugiP5CwcOFCnX/++ZoyZYpeeuklXXrppVq9erWOP/54Pf300/rEJz5R\n0mOMRwQ1AGAQgUhI4cqoaiLlilRGpYCjYKoAxT9QVG7aVdMHzSoP5CiuAQAAxrzDDjtM7e3tClvb\nzC9atGhIY7z++uu6/vrrJUmnnnqq7rrrLr9W2sc//nEdfvjh2m233dTS0qLzzjsv5/ilMsZ4FRi8\nCwBMLE4woFB1uaKN9aqdN021205RxbQaRarLClPJFEWXTqa18b1NSnQnB+8MAMA44nmZgueldIyU\nioqKPgGN4bj22mvluq4ikYiuv/76fsXfZ8+ere9+97uSpOeee07PPPNMyY4xXrFSAxjHastm+e3y\n6i3/5S1XSonn5m4PxskTUrXP50tXyTWfXPNw0wGrr7keDH1kosGgnLIyJdvrFaiIqilp0nZSqUDO\nZM8ya8OP2uwOKeXWuCFr7lErjaYiZFJDokE7pSTTLrd2Likrt1JKKq2UEmtjkkB15p/xQI3Z6cap\nMWkYqqu02tbOJTWmKJZXW5dpRLbsTX+ztZtUjUBzNr1kvUkz8daZ3U/Sq7r9duca8/3c1GLSZzb2\nZNJq1sftXU5M31ZrIUZVSErGUmr/oFk7VSSkCmlph3krfL3ZdLZTTjZ6y/12T7LZb/emnbiueVyf\nlBNrd5O+aSRub2flQsoJAADFl0ql9MADD0iSDj30UE2bNi1nv9NPP10XXXSRJOm+++7T/vvvX3Jj\njGcENVAwu+76MYVC/JUqJZOrFvjtwNYztni8XEEN+3evQgQ17J1ehxLUyDUPJ0/tj2Aw97hBJxsA\n4MP7cSvWGVfLhy15a8EAAAD0Wrp0qVpaMh+2DBQgmD59urbddlu9//77eu6550pyjPGM30CBccy1\nftNfubau3/XAZnwa7GrgQEafvrmCHnkE8gUsrCEGm1+fuVldU73zsM5tztxSXibSUmWtppgUjVvz\nseZmzT8czPSPWsU8I2EzRiRirbKImshPuNK0gxWZ+QWqzRIQp8Yq7FllVmKo0lqJUVXR909JXoXV\nrrQKgo5kwc9ckiY65LSY1RfORrMqQxsy5901beZha82qh471VsHPDrPqZH3MvDZN8cxbWXPCfI87\nrSLnk7Ndu5q6tFXXJs2py3y9uDkz9pJW8z1e4a3z2/bqjO7kRr8dt4uCpjNFQV3PjOHZBUE9u6o6\ne/sCAIpvpFM+hqqUP2hYsmSJ354/f/6AfefNm6f333+/z2NKaYzxjKAGAGBc8zxP7Wva1NPaI23+\nVvQAAGCUtbW1+SsScqmvrx/F2Ujr1pkPO6ZPnz5g397rra2tisfjikajJTXGeEZQAwAwbrmptJpX\ntSjZQ04RAABSpm5TKdVusmey2267Ddx3lJd1dHZ2+u3y8vIBemaKktqP6w0mlMoY4xlBDWAccz2z\njH/Otvmj3gMPkuNcnveTQtfUGHR/pnz38/rPZ7PeA3tTVKzUkoDJflDA+hfTsQqFOpHM45wyM2En\naqWRVFhFPMusASutN5mKbI6EnVpivWl5VuqIV5ajT6nsypKrCKgkbbT+/lmFQFPruiRJ8bXmNW9r\nNs91Q5dpb4yZ164pab4Zbdm/5nHr+91YLqV6Empb26I50R4p+9D/22Be8yWtmdSRlY759KPJ+8Bv\n50s5SaVN8dLetBM75WSz/kfI8ReylH7ABAAAUjpt0kgDgYF/MA0Gzc9+qZT5uaBUxhjPCGoAAMad\nnuYuda5rz3wx/j+gAABgXPjXv/6l2bNnF3savspKU88rFosN2Lenp8dvV1WZmmalMsZ4RlADADBu\nuGlXravb5KQGfsMHAGCi8kq4UGhtbe2o180YiD2XpqamAXqa65FIpE8KSKmMMZ4R1ADGsWTaRGrL\nPjl18x+Y740u1zugO8wdHQZZOpfpM4SUiuG+O+e6h30qbP0zac/ZPh/KLvOLWqkl4bDf9KxcRq8s\nmvO8n0ZiLRksefFM6oXT3Oyf6rOzycZWv+muM+kbyXUmLap7U+Z1am43KTWbeszOJhvj1u4nCfP6\nt1slMnq/hVsFE2pf06xqL62GcrNM84n1VspJm/l/YqWzSpLU4q30z/UkzXPps8uJa4Ikbp/dTbJt\nK+WkbxrJUHOgAABAqdhuu+389ooVKwbs23t9wYIFchzzw2SpjDGebcZvFQAAlLaupi61Lt8kN5ke\nvDMAAMBm2GmnnRQKZT7IWrx4cd5+3d3devPNNyX1L3ZaKmOMZwQ1AABjlpty1bSiWW1r21kJAQDA\nZvC80jtKVXV1tQ466CBJ0sMPP6xEIpGz30MPPaRkMrOM9Oijjy7JMcYz0k+AcSxp79Kw5279O2xO\nykaunRyGmupRCjtz5NtuJdfc7DQTux20U1FK4DmNloTJ9XBazc4lTktrpmHtbOKtb/fb6fUm1SO2\n0bxe7S0mv7O5O5NqsiluUkRaEtbOJinzuE6rgHdtWIp3xNS2ulW7ViSkBqklYVJ3nlhn0n/e6ejy\n26sDq8x93EzaSXfKpJwkU6Zv311O7JSTuJlI9v+P4aacsOMJAACl7Stf+Yoef/xxbdy4Ub/61a90\nwQUX9LmeSqX0k5/8RJLU2NiohQsXluwY4xUrNQAAY4rnempf3arWD1vkpQkKAACAkXPiiSdqv/32\nkyRddNFF+t3vfudvsbpu3TqdfPLJ+uc//ylJuuKKK3IW5yyVMcYrx/NKecEPSl1LS4saGhokSbvu\n+gk/1wulIRg0BRefe/7aIs4EY4K1BZjT3mbarWb1hTa1+k1vU6aQZnqTWdGQXG9WKXS2mCKfrZ2m\nEGhTzPy9bElm/s1oslZZdFpFQO36Vo1lrpJdcXWuadM2ZWZFxQtNmTftJW3m3it6TJHPNUFTCLQt\nvcZvx1KZ55KwC4Km7YKgZkWGZ63UyF0UdGgFQVmhAQCjzZOU+QWwubm5pHbYGC21tbVqb2/XEfVf\nVkN4erGn4/M8T3/YeJkkafny5ZozZ05Bx99rr73U3m5+llm/fr1aW1slSfPnz1fAWpV76aWX6uST\nT+43xvr163XAAQfonXfekZRJB6mvr9fq1av9wMI3v/lNXXtt/p+3S2WM8YjfQAEAJc9zPXWuaVXc\n2r0EAABgMO+8847a2tpyXlu2bFmfr1taWnL2mzZtml555RVdfvnluv3227VmzRp1dGQ+FNlnn310\n0UUX6bjjjhtwHqUyxnjESg1sEVZqlDZWamBISnSlRqwjrrY1bZoZMRfmVpkVFazUAAAMjpUavSs1\nDq8rvZUa/7Np5FZqLFu2zF/FMJjGxkbV1dUN2q+pqUkdHR2aNm2aysvLB+1fymOMB/wGCoxjrhsf\nvBMmpnbzi3xvAKNP8KLVXPearF/6N5nAR2pj5hf5Lit40d5lAhbNVvCi1Sr+2Zw0AYyebCHQpPU7\nfl12uHQipcntG5Xqiks1UtI1uSiPrDV5ou+2Z4Idq9Lm05WNARPI6Ext8NuxlAnWJNOZFJbhBzIk\nP5hBIAMAgJI0f/78go85adIkTZo0aVyMMR4Q1AAAlBTP9dS1qVPdTZ2qqyQwBwAAgPwIagAASka8\nPabYpnalk5u3TBQAAAyNpz5Jk0XHOkZsKYIawDhGyZwJKm5WNzhWtW/HSjlRq1Uwq7VTkuS1mNSS\ndJMZI9lkfvTpbjOpJh3d0czDY1EzVCJs2laaSXfaVBbvtrI6yrJd5gS61bOhTanuhKZVm3u/0lLl\nt99sM+knyzvNXNc46zP3c0y9jO5Uk9+Op8xrkEyb+h+um8j8OdyUk8wDNBBSTgAAAEYWQQ0AQNGk\nk2l1buhQVSp3VXIAAABgIAQ1AACjznVddW7slNfWlVl3WlnsGQEAMDF48kpqNW8pzQVjE0ENABgL\nXOsN3956tSuTOuJ0dpnrHZ2m3Wa3zePcVivVpCWTchFvMSkiPZ3m7aGzx9qC1Uo16Uhl+rRY27HG\n7DQTqyxGJHvac1019rQr1typBtfTjIbMziMfWrum/Hl1td/+oNMMsiph0kg2Bdf57XZ3bebe2S1a\nJSlubdOa6rO7ScJve14y+2dhU04AAAAweghqAABGnuepu6lLXU2dKo8miz0bAAAAjBMENYBxzHGC\ng3dC8STML/dOd3ZFRY9ZVeBYbfu8uq12e2aFhtdpFbvsMG23zaxCSLaZFQZxayVGd3dFZihrFUZn\n0hT87EyZv0ctCfO4ZHa4hGsKeEYC5h7bVqbkua7iLV2q7W6W0q5UJzXFzX0eX59ZlbGiw6yEWB0z\nxTw3OJv8dlvArM7osguBpjOrMpIps1ol7dqrM8xr4Lr2FrHZexZ4dQbFQQEApcz1+i4ALTYWQGJL\nEdQAABScm0qrp61N8dZuyfVUW1ZKm8cBAABgvCCoAQAomHQ8pe5NnYq19aisnDQTAABKjSfJLaFV\nhaxwxJYiqAGMY4FAePBO2Hz2Ws20VVwylW0nzTknaQpS9i3sadrqMmkWfkpJl5VGErOKWnabAIHb\naaVTdGZWQCQ7TApIosf80x6Llfvtzpj5+9CVMu2ebHpJa9I8rssq+Gn/qJH2zH3KApl7zypPKNkZ\nU6KlU3Xpjsw7yySp2UozeXKjXfzTjLg6+7w3eGZL12Y7zcQ16Sd2IdBE2qSa9BYCdV3zetkpJ70F\nQbNXrPNWJdMc1zdnPSw/iAEAABQXQQ0AwLB4aVex1m519LTKS2UDBNGBHwMAAAAUEkENAMCQJDrj\nirV2K5hdadJQnmvFAwAAKEWeV1rFOUtpLhibCGoA41gwUGm+aO/Y7Mc5Xp6ijvnOD4djpTdY7T4C\nTv9zdt9888mmiTiulSKStvqmrF1H4lZqgp9GYp1L5kgzkaS09Yt8MttOWefscWOm7XVZKRKddjsz\nv7SVnZKOm+efjJvnnYib1JHueCTzZ8Kci6XNbiVxK42kLWXteJIy53vvYv9MEbV2MSkPunKTaSVa\nuzUp0ZJZlRGUgrWZOW+y0kweW1frt1dZ2TVrrOe90TV/F5uDGyRJXZ5JM+lJtprn2ifNxLw46T6p\nJpm2nWbi2X837FSUfLubmAf2P/fRLqScAAAAlAyCGgCAnNy0q0RHTKnOLqV7soEDVmUAAACghBDU\nAMaxcLDCbwdefHWYo+T4VDrfgo3BNj3PtfJCfRdfmHUDkgKB/o/LPUTOafZZOdGnyKc578WtPqnM\nE/MSqX7nJMlL2m37Ppmx3ZS5h7WQQK694iJmnmzcKtwZy66+iKfMKouka9op14wRt863ZYt7dlkr\nL1yrmGfAyf09qQiZ59K7KqM+kpCXdpXq6FEk1iG3O1O0tMMN+7Uy3us0hUff6cjce32PGWtdjyl0\nuslrN/N0mv12Z6DJb/ekWiRt5oqMQQuB2qszrO8VqzMAAPC58tj9BOMKQQ0AmODcVFrJzph6Eu1K\nd2V2EgmFU4M8CgAAACg+ghoAMAGle5JKdMaU7IwrnMysrqiKxgd5FAAAAFBaCGrk4XmeXnvtNT33\n3HNavXq11q5dK8/z1NjYqL333ltHHHGEwuHw4AMNweLFi/XQQw9p1apVikajmj9/vk488UTNmjVr\nVMfA+BEOmUKhPS809buerz5n3jqhuVYHWqkOQ6kj2ufeVoqE4+ToY6ecWI9z8qSi+PO052PN3bUy\nTry0GSSddLLXzU1SSdNOu+afzLTVx82mhqQ863H2GK6VcmIV8ey2Uk26s+cTVppJ2sv9BEOB/t+I\nKiudJGJdjwQyT9ZLpVWR7FS6Oy63Kyavt3BqWIoFMvduTkT8xy3aVOW311jFSzfFzIu3MZFJGWmR\nSTPpCLT4bbv4Zzzd6beTVqpJ0s0MnkrH/HOelVqSO80kdyHQ/MtXh/IX2rrMclgAwHjE7icYZwhq\n5HD11VfryiuvVHNzc94+M2bM0C9+8Qsdf/zxW3y/DRs26IwzztAjjzzS79q3vvUtfe9739Mll1yi\nQCDPb6AFGgPA+OGm0kp3J+TGupXqjstLphWKJAZ/IAAAADCGENTI4c033/QDGrvttpsOP/xwzZs3\nT47jaPHixbrzzju1Zs0afe5zn9Mtt9yiM888c9j36urq0pFHHqlXXnlFgUBAp512mg488EB1dXXp\n7rvv1uLFi3XZZZepq6tL11577YiNAWAM86R0LKFULKFUT1KxWDyz7aqksiC7lQAAAGD8cjyPBT8f\ndeaZZ2rTpk364Q9/qD333LPf9bffflsHHXSQ1q9fr8rKSr377rtqbGwc1r2+973v6Sc/+Ykk6Y47\n7tDpp5/uX0un0/q3f/s3/eEPf5AkLVq0SPvss8+IjDFcLS0tamhokCTtuusnFAoRJyslk6t28dvX\nLdhpwL72LhlunrQH/7r9RZ5/QQYbo8+uHPaGJ7n69LlupaoMkh7g5dkqxbXOu1a6RypHGomdAmLv\nQNIn1STbJ51nOvYs7NSRkGO3M69q2Lqea+cSz/WkRFJuPKl0PKlIqkduLCnX+qYk3dzzX9NT5rff\n7TT/r27KltJoiZtBmuIm1aPNNfknrYFWv92lTKpJzNrlJOGaNBN7R5O0G7fa/Xc02aw0k0F3Nxle\nmkmfrqScAMA45UnKBPqbm5tVX19f3OkUQW1trdrb2/Wp6rNUGxre7y4jwfM8PdSa+V1m+fLlmjNn\nTnEnhDGHXIQcfvzjH+svf/lLzoCGJG2//fa66qqrJGVWSfzxj38c1n06Ojp0ww03SJIWLlzYJxgh\nScFgUL/61a9UU1MjSbrssstGZAwApcdzXaVjSSXbuxXf1K7YmmZ1LV+vrmVr1PPhRsXXtyrV2iW3\nJ0EyKgAAACYsgho5zJgxY9A+hx12mN9+6623hnWfhx56SN3d3ZKkL3/5yzn71NTU6KSTTpIkPf74\n42ptbS34GACKwPXkxlNKdcWVbOtWoqlDsXWt6v5wo7reXauuZWvV88EGxde2KNnUoVRHj7wE26wC\nAIAt43peyR3AliBXYJiSSbM82nWHsOWD5amnnpIkOY6jAw44IG+/gw46S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1pk2blvO57LfffgW9V1dXl3fGGWfk/T7/27/9m9fZ2TniYxRLMpn0vv71r3uO4+Sc+9FHH+1t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\niIiIiIgcEpsaREREREREROSQ/g9HtCFtSmMdawAAAABJRU5ErkJggg==\n"
          }
        }
      ],
      "source": [
        "sim_data_opt.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\")\n",
        "plt.show()"
      ],
      "id": "673cb72f"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "With the optimized design, we can directly export a GDS file of the\n",
        "S-bend for fabrication."
      ],
      "id": "fc734a7b-f55c-4c3b-ac03-131c4ad2572e"
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [],
      "source": [
        "# make the misc/ directory to store the GDS file if it doesn't exist already\n",
        "import os\n",
        "\n",
        "if not os.path.exists(\"./misc/\"):\n",
        "    os.mkdir(\"./misc/\")\n",
        "\n",
        "sim_opt.to_gds_file(fname=\"misc/optimized_sbend.gds\", z=0)"
      ],
      "id": "97f1dec0"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  }
}