{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Bayesian optimization of a Y branch\n",
        "\n",
        "**Note: the cost of running the entire notebook is larger than 10\n",
        "FlexCredits.**\n",
        "\n",
        "[Bayesian\n",
        "optimization](https://en.wikipedia.org/wiki/Bayesian_optimization) is a\n",
        "popular technique for searching and optimizing a design space. It works\n",
        "by building a probabilistic model, such as a [Gaussian\n",
        "process](https://en.wikipedia.org/wiki/Gaussian_process), to predict the\n",
        "outcome of a more complex objective function. Unlike other optimizers\n",
        "like genetic algorithms or particle swarm optimization, Bayesian\n",
        "optimization uses an acquisition function to select new potential\n",
        "solutions, focusing on areas of the design space that the Gaussian\n",
        "process predicts will have high objective values and high uncertainty.\n",
        "Over the course of the optimization, the model’s uncertainty reduces,\n",
        "improving its accuracy as a surrogate for the true objective function.\n",
        "This makes Bayesian optimization a direct approach to optimization,\n",
        "particularly well-suited to problems with small (\\< 30 dimensions) and\n",
        "expensive-to-evaluate objective functions.\n",
        "\n",
        "However, as Bayesian optimization is an iterative process with many\n",
        "individual components, it can be challenging to design and parallelize,\n",
        "leading to long run times and high computational costs. Fortunately, the\n",
        "Design plugin in `Tidy3D` includes the Bayesian optimization method\n",
        "`MethodBayOpt` which eliminates much of this complexity. Users can\n",
        "quickly and easily run a Bayesian optimization for complex FDTD\n",
        "simulations and analyze the results. Further details on Bayesian\n",
        "optimization and the methods used in `Tidy3D` are available in the\n",
        "open-source Python library\n",
        "[bayesian_optimization](https://bayesian-optimization.github.io/BayesianOptimization/2.0.0/basic-tour.html).\n",
        "\n",
        "This notebook details how to perform a Bayesian optimization with the\n",
        "`Tidy3D` Design plugin for the development of a Y-junction. It is based\n",
        "on the work of\n",
        "`Zhengqi Gao, Zhengxing Zhang, and Duane S. Boning, \"Automatic Synthesis of Broadband Silicon Photonic Devices via Bayesian Optimization\" Journal of Lightwave Technology 40, 7879-7892 (2022)`\n",
        "[DOI:10.1109/JLT.2022.3207052](https://doi.org/10.1109/JLT.2022.3207052).\n",
        "A detailed description of how to build a Y-junction can be found in the\n",
        "[Waveguide Y\n",
        "junction](https://www.flexcompute.com/tidy3d/examples/notebooks/YJunction/).\n",
        "\n",
        "<img src=\"img/bayesian_opt_y_junction.png\" width=\"400\" alt=\"Schematic of the waveguide Y junction\">\n",
        "\n",
        "If you are curious about other `Design` plugin features, see these\n",
        "notebooks:\n",
        "\n",
        "1.  [Intro to Design\n",
        "    Plugin](https://www.flexcompute.com/tidy3d/examples/notebooks/Design/)\n",
        "\n",
        "2.  [Particle Swarm Optimizer\n",
        "    PBS](https://www.flexcompute.com/tidy3d/examples/notebooks/ParticleSwarmOptimizedPBS/)\n",
        "\n",
        "3.  [Particle Swarm Optimizer Bullseye\n",
        "    Cavity](https://www.flexcompute.com/tidy3d/examples/notebooks/BullseyeCavityPSO/)\n",
        "\n",
        "4.  [Genetic Algorithm\n",
        "    Reflector](https://www.flexcompute.com/tidy3d/examples/notebooks/GeneticAlgorithmReflector/)\n",
        "\n",
        "5.  [All-Dielectric Structural\n",
        "    Colors](https://www.flexcompute.com/tidy3d/examples/notebooks/AllDielectricStructuralColor/)\n",
        "\n",
        "6.  [Parameter\n",
        "    Scan](https://www.flexcompute.com/tidy3d/examples/notebooks/ParameterScan/)"
      ],
      "id": "7e276053-4a7b-4b5f-b323-f66cc9e07f30"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "# The Bayesian optimizer uses the bayesian-optimization external package version 1.5.1.\n",
        "# Uncomment the following line to install the package\n",
        "# pip install bayesian-optimization==1.5.1\n",
        "\n",
        "import gdstk\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tidy3d as td\n",
        "import tidy3d.plugins.design as tdd\n",
        "import tidy3d.web as web\n",
        "from scipy.interpolate import make_interp_spline"
      ],
      "id": "f6523ada"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Simulation Setup\n",
        "\n",
        "The simulation is defined across the 1.5 $\\mu m$ to 1.6 $\\mu m$\n",
        "wavelength range with 100 sampling points. The base of the model is\n",
        "silicon, the top of the model is silicon dioxide; we use the material\n",
        "constants included in the Tidy3D Tidy3D’s [material\n",
        "library](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/material_library.html#)."
      ],
      "id": "298b8e36-8669-4e0c-8a05-01044f24a401"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "lda0 = 1.55  # central wavelength\n",
        "freq0 = td.C_0 / lda0  # central frequency\n",
        "n_wav = 100  # Number of wavelengths to sample in the range\n",
        "ldas = np.linspace(1.5, 1.6, n_wav)  # 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\n",
        "\n",
        "si = td.material_library[\"cSi\"][\"Palik_LowLoss\"]\n",
        "sio2 = td.material_library[\"SiO2\"][\"Palik_LowLoss\"]"
      ],
      "id": "e389ed6e"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The following parameters describe the geometry of the waveguide. The\n",
        "optimizable design space is the junction which is split into 13 discrete\n",
        "segments."
      ],
      "id": "612285f0-bae2-45cd-94c9-d92751998223"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "t = 0.22  # thickness of the silicon layer\n",
        "num_d = 13  # dimensional space of the design region\n",
        "\n",
        "l_in = 1  # input waveguide length\n",
        "l_junction = 2  # length of the junction\n",
        "l_bend = 6  # horizontal length of the waveguide bend\n",
        "h_bend = 2  # vertical offset of the waveguide bend\n",
        "l_out = 1  # output waveguide length\n",
        "branch_width = 0.5  # width of one Y branch\n",
        "branch_sep = 0.2  # distance between y branches at the junction\n",
        "inf_eff = 100  # effective infinity"
      ],
      "id": "51eca979"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The most effective way to use the Design plugin is to split the workflow\n",
        "into “pre” and “post” functions which can surround a call to the Tidy3D\n",
        "cloud that carries out the computation. This takes advantage of\n",
        "automated simulation batching, allowing for parallelization which saves\n",
        "a considerable amount of time. The pre-function returns a `Simulation`;\n",
        "the post-function then analyzes the corresponding `SimulationData`.\n",
        "Together they can be considered the fitness function (or objective\n",
        "function / figure of merit) which the Bayesian optimization process is\n",
        "working to predict.\n",
        "\n",
        "The function `fn_pre` needs to take the parameters that are being\n",
        "optimized: these are the 13 width segments of the junction. They always\n",
        "input as a dictionary and can be unpacked within the function (as below)\n",
        "or included as keyword arguments. These parameters are used to build a\n",
        "Y-junction `Simulation` object.\n",
        "\n",
        "The function `fn_post` then takes the `SimulationData` object output by\n",
        "the simulation and computes the objective function. The value of\n",
        "objective is then fed back into the Bayesian optimization to inform the\n",
        "probabilistic model. In this case, we extract the power passing through\n",
        "the Y-junction and the power reflected back to the source. These values\n",
        "are evaluated in a custom loss function described by `Gao et al.` to\n",
        "determine the effectiveness of the junction design:\n",
        "\n",
        "where the summation is performed over the simulated wavelengths,\n",
        "$N_\\lambda$ is the number of wavelength points, $R$ is the reflected\n",
        "power and $T$ is the transmitted power in one branch.\n",
        "\n",
        "The aim of this function is to achieve a transmitted power of 0.5\n",
        "through each branch, whilst driving the power reflected towards the\n",
        "source to zero. Note there is a minus sign in front of the output value\n",
        "as this loss function is a minimizing function, whilst all the\n",
        "optimizers in the Design plugin are built to maximize the objective\n",
        "function."
      ],
      "id": "6cb3c27b-9ddf-406d-a5ae-fcdde8bba314"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "def fn_pre(**w_params: dict) -> td.Simulation:\n",
        "    \"\"\"Create a Simulation of a Y splitter from a series of junction widths.\n",
        "\n",
        "    Includes mode monitors to measure the power transmitted and reflected to source.\n",
        "    \"\"\"\n",
        "    w_start = 0.5\n",
        "    w_end = branch_width * 2 + branch_sep\n",
        "\n",
        "    widths = [w_start]  # Ensures input waveguide is included in spline for first point of junction\n",
        "    widths.extend(list(w_params.values()))\n",
        "    widths.append(w_end)  # Ensures final point of junction smoothly converts to the branches\n",
        "\n",
        "    x_junction = np.linspace(\n",
        "        l_in, l_in + l_junction, num_d + 2\n",
        "    )  # x coordinates of the top edge vertices\n",
        "    y_junction = np.array(widths)  # y coordinates of the top edge vertices\n",
        "\n",
        "    # pass vertices through spline and increase sampling to smooth the geometry\n",
        "    new_x_junction = np.linspace(\n",
        "        l_in, l_in + l_junction, 100\n",
        "    )  # x coordinates of the top edge vertices\n",
        "    spline = make_interp_spline(x_junction, y_junction, k=2)\n",
        "    spline_yjunction = spline(new_x_junction)\n",
        "\n",
        "    # using concatenate to include bottom edge vertices\n",
        "    x_junction = np.concatenate((new_x_junction, np.flipud(new_x_junction)))\n",
        "    y_junction = np.concatenate((spline_yjunction / 2, -np.flipud(spline_yjunction / 2)))\n",
        "\n",
        "    # stacking x and y coordinates to form vertices pairs\n",
        "    vertices = np.transpose(np.vstack((x_junction, y_junction)))\n",
        "\n",
        "    junction = td.Structure(\n",
        "        geometry=td.PolySlab(vertices=vertices, axis=2, slab_bounds=(0, t)), medium=si\n",
        "    )\n",
        "\n",
        "    x_start = l_in + l_junction  # x coordinate of the starting point of the waveguide bends\n",
        "\n",
        "    x_bend = np.linspace(x_start, x_start + l_bend, 100)  # x coordinates of the top edge vertices\n",
        "\n",
        "    y_bend = (\n",
        "        (x_bend - x_start) * h_bend / l_bend\n",
        "        - h_bend * np.sin(2 * np.pi * (x_bend - x_start) / l_bend) / (np.pi * 2)\n",
        "        + w_end / 2\n",
        "        - w_start / 2\n",
        "    )  # y coordinates of the top edge vertices\n",
        "\n",
        "    # adding the last point to include the straight waveguide at the output\n",
        "    x_bend = np.append(x_bend, inf_eff)\n",
        "    y_bend = np.append(y_bend, y_bend[-1])\n",
        "\n",
        "    # add path to the cell\n",
        "    cell = gdstk.Cell(\"bends\")\n",
        "    cell.add(\n",
        "        gdstk.FlexPath(x_bend + 1j * y_bend, branch_width, layer=1, datatype=0)\n",
        "    )  # top waveguide bend\n",
        "    cell.add(\n",
        "        gdstk.FlexPath(x_bend - 1j * y_bend, branch_width, layer=1, datatype=0)\n",
        "    )  # bottom waveguide bend\n",
        "\n",
        "    # define top waveguide bend structure\n",
        "    wg_bend_1 = td.Structure(\n",
        "        geometry=td.PolySlab.from_gds(\n",
        "            cell,\n",
        "            gds_layer=1,\n",
        "            axis=2,\n",
        "            slab_bounds=(0, t),\n",
        "        )[0],\n",
        "        medium=si,\n",
        "    )\n",
        "\n",
        "    # define bottom waveguide bend structure\n",
        "    wg_bend_2 = td.Structure(\n",
        "        geometry=td.PolySlab.from_gds(\n",
        "            cell,\n",
        "            gds_layer=1,\n",
        "            axis=2,\n",
        "            slab_bounds=(0, t),\n",
        "        )[1],\n",
        "        medium=si,\n",
        "    )\n",
        "\n",
        "    # straight input waveguide\n",
        "    wg_in = td.Structure(\n",
        "        geometry=td.Box.from_bounds(rmin=(-inf_eff, -w_start / 2, 0), rmax=(l_in, w_start / 2, t)),\n",
        "        medium=si,\n",
        "    )\n",
        "\n",
        "    # the entire model is the collection of all structures defined so far\n",
        "    model_structure = [wg_in, junction, wg_bend_1, wg_bend_2]\n",
        "\n",
        "    Lx = l_in + l_junction + l_out + l_bend  # simulation domain size in x direction\n",
        "    Ly = w_end + 2 * h_bend + 1.5 * lda0  # simulation domain size in y direction\n",
        "    Lz = 10 * t  # simulation domain size in z direction\n",
        "    sim_size = (Lx, Ly, Lz)\n",
        "\n",
        "    # 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=(l_in / 2, 0, t / 2),\n",
        "        size=(0, 4 * w_start, 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_11 = td.ModeMonitor(\n",
        "        center=(l_in / 3, 0, t / 2),\n",
        "        size=(0, 4 * w_start, 6 * t),\n",
        "        freqs=freqs,\n",
        "        mode_spec=mode_spec,\n",
        "        name=\"mode_11\",\n",
        "    )\n",
        "\n",
        "    mode_monitor_12 = td.ModeMonitor(\n",
        "        center=(l_in + l_junction + l_bend + l_out / 2, w_end / 2 - w_start / 2 + h_bend, t / 2),\n",
        "        size=(0, 4 * w_start, 6 * t),\n",
        "        freqs=freqs,\n",
        "        mode_spec=mode_spec,\n",
        "        name=\"mode_12\",\n",
        "    )\n",
        "\n",
        "    # add a field monitor to visualize field distribution at z=t/2\n",
        "    field_monitor = td.FieldMonitor(\n",
        "        center=(0, 0, t / 2), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\"\n",
        "    )\n",
        "\n",
        "    run_time = 5e-13  # simulation run time\n",
        "\n",
        "    # construct simulation\n",
        "    sim = td.Simulation(\n",
        "        center=(Lx / 2, 0, 0),\n",
        "        size=sim_size,\n",
        "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=20, wavelength=lda0),\n",
        "        structures=model_structure,\n",
        "        sources=[mode_source],\n",
        "        monitors=[mode_monitor_11, mode_monitor_12, field_monitor],\n",
        "        run_time=run_time,\n",
        "        boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
        "        medium=sio2,\n",
        "    )\n",
        "\n",
        "    return sim\n",
        "\n",
        "\n",
        "def fn_post(sim_data: td.SimulationData) -> float:\n",
        "    \"\"\"Calculate the loss function from the power at the mode monitors in the SimulationData.\"\"\"\n",
        "    # Calculate the power reflected back to source and transmitted to one branch\n",
        "    power_reflected = np.squeeze(\n",
        "        np.abs(sim_data[\"mode_11\"].amps.sel(direction=\"-\", mode_index=0)) ** 2\n",
        "    )\n",
        "    power_transmitted = np.squeeze(\n",
        "        np.abs(sim_data[\"mode_12\"].amps.sel(direction=\"+\", mode_index=0)) ** 2\n",
        "    )\n",
        "\n",
        "    # Loss function proposed by Gao et al. which takes advantage of branch symmetry\n",
        "    loss_fn = 1 / 3 * n_wav * np.sum(power_reflected**2 + 2 * (power_transmitted - 0.5) ** 2)\n",
        "    output = -float(loss_fn.values)  # Negative value as this is a minimizing loss function\n",
        "\n",
        "    return output"
      ],
      "id": "ce018727"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can quickly check that `fn_pre` is working correctly by passing a set\n",
        "of potential `test_params` and plotting the result. Note that the gap\n",
        "between the `Polyslab` junction and the branches is a plotting artifact\n",
        "and doesn’t exist in the `Simulation`."
      ],
      "id": "93cfe6ae-0e88-4d59-860b-1cb4e47b11cc"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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xXHHFw4pmyOqxBCsAAAAA0i4cjWntniq9ua2c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\nUED2sF/hZHfkKIIVAAAAAAlhGIYmDC3UhKGFOtLg0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          }
        }
      ],
      "source": [
        "test_params = {\n",
        "    \"w1\": 0.5,\n",
        "    \"w2\": 0.5,\n",
        "    \"w3\": 0.6,\n",
        "    \"w4\": 0.7,\n",
        "    \"w5\": 0.9,\n",
        "    \"w6\": 1.26,\n",
        "    \"w7\": 1.4,\n",
        "    \"w8\": 1.4,\n",
        "    \"w9\": 1.4,\n",
        "    \"w10\": 1.4,\n",
        "    \"w11\": 1.31,\n",
        "    \"w12\": 0.5,\n",
        "    \"w13\": 0.5,\n",
        "}\n",
        "sim = fn_pre(**test_params)\n",
        "sim.plot(z=0)\n",
        "plt.show()"
      ],
      "id": "5d08e71c"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Next, we setup the Bayesian optimization method. This is done with the\n",
        "`MethodBayOpt` object. We don’t have the `lcb` (lower confidence bound)\n",
        "acquisition function used in the paper available to us, but by making\n",
        "our loss function negative and using the `ucb` (upper confidence bound)\n",
        "acquisition we achieve the same optimizer design. The `initial_iter` and\n",
        "`n_iter` options control the number of initial random samples and\n",
        "subsequent optimization iterations respectively. We can also set the\n",
        "random `seed` to ensure reproducible results (optional).\n",
        "\n",
        "We also create a list of `ParameterFloat` objects corresponding to the\n",
        "13 segment widths. The `span` option defines the bounds of each\n",
        "parameter.\n",
        "\n",
        "The `method` and `parameters` are then passed to a `DesignSpace` object\n",
        "which contains all we need to run the Bayesian optimization."
      ],
      "id": "f8d0d523-0519-470f-8a1e-db3d7aab59e5"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "method = tdd.MethodBayOpt(\n",
        "    initial_iter=30,\n",
        "    n_iter=70,\n",
        "    acq_func=\"ucb\",\n",
        "    kappa=0.3,\n",
        "    seed=1,\n",
        ")\n",
        "\n",
        "parameters = [tdd.ParameterFloat(name=f\"w_{i}\", span=(0.5, 1.6)) for i in range(num_d)]\n",
        "\n",
        "design_space = tdd.DesignSpace(\n",
        "    method=method, parameters=parameters, task_name=\"bay_opt_notebook\", path_dir=\"./data\"\n",
        ")"
      ],
      "id": "8a3a4361"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "It is then very easy to the launch this optimization with\n",
        "`design_space.run()`. This launches an initial random batch of 30\n",
        "simulations, as specified by `initial_iter`, followed by sequential\n",
        "computation of 70 simulations, as specified by `n_iter`. In the latter\n",
        "phase, the Bayesian optimizer chooses potential candidates based on\n",
        "simultaneously maximizing the objective value and efficiently exploring\n",
        "the design space. The total of 100 simulations takes around 90 minutes\n",
        "to compute. Once complete, the results are returned in a `pandas`\n",
        "dataframe for analysis."
      ],
      "id": "2f6e1a49-0399-4744-810b-bc1c7e58332f"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
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          "data": {
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              "<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\">07:51:28 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span> Simulations                                             \n",
              "</pre>"
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              "<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\">07:53:56 UTC </span>Best Fit from Initial Solutions: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-180.654</span>                          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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              "<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\">07:54:38 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
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              "<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\">07:55:17 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
              "</pre>"
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              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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              "<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\">07:55:58 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
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              "<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\">07:56:39 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-134.703</span>                                \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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              "<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\">07:56:40 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
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              "<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\">07:57:20 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-122.116</span>                                \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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              "<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\">07:57:21 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
              "</pre>"
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              "<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\">07:58:23 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
              "</pre>"
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              "<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\">07:59:08 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-80.47</span>                                  \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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              "<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\">08:00:02 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
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              "<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\">08:00:54 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
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              "<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\">08:01:31 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
              "</pre>"
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          "data": {
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              "<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\">08:02:07 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
              "</pre>"
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          "data": {
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              "<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\">08:02:58 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-74.239</span>                                \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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              "<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\">08:02:59 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
              "</pre>"
            ]
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        },
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              "<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\">08:03:50 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-68.458</span>                                \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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              "<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\">08:03:51 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
              "</pre>"
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          "data": {
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              "<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\">08:04:36 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-63.557</span>                                \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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              "<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 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
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              "<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\">08:05:30 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
              "</pre>"
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              "<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\">08:07:40 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
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          "data": {
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              "<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\">08:08:16 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-48.535</span>                                \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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              "<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\">08:08:18 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
              "</pre>"
            ]
          }
        },
        {
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          "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\">08:09:13 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
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              "<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\">08:09:45 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
              "</pre>"
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        },
        {
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          "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\">08:10:41 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
              "</pre>"
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          }
        },
        {
          "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\">08:11:45 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-43.538</span>                                \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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              "<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\">08:11:46 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
              "</pre>"
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        },
        {
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          "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\">08:12:55 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
              "</pre>"
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        },
        {
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          "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\">08:13:25 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-37.147</span>                                \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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              "<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\">08:13:26 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
              "</pre>"
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        },
        {
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          "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\">08:14:06 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">23</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-35.19</span>                                 \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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              "<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\">08:15:25 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
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              "<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\">08:15:59 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
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          }
        },
        {
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              "<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\">08:16:39 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">26</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-26.814</span>                                \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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        },
        {
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              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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        },
        {
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              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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        },
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              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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              "<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\">08:27:52 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">38</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-4.111</span>                                 \n",
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              "<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\">08:29:48 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">39</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-4.033</span>                                 \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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              "<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\">08:31:23 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">40</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-3.751</span>                                 \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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              "<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\">08:33:04 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">42</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-3.469</span>                                 \n",
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              "<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\">08:33:05 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
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              "<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\">08:33:39 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">43</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-3.204</span>                                 \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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        },
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              "<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\">08:34:18 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">44</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.863</span>                                 \n",
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              "<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\">08:34:52 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">45</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.695</span>                                 \n",
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              "<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\">08:35:46 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
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              "<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\">08:36:24 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">47</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.631</span>                                 \n",
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              "<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\">08:37:22 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
              "</pre>"
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              "</pre>"
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        },
        {
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          "data": {
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              "<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\">08:39:02 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">50</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.553</span>                                 \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
              "</pre>"
            ]
          }
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              "</pre>"
            ]
          }
        },
        {
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              "<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\">08:39:57 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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              "<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\">08:40:27 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">52</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.352</span>                                 \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
              "</pre>"
            ]
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        },
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              "<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\">08:40:28 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
              "</pre>"
            ]
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        {
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              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
              "</pre>"
            ]
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              "<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\">08:43:13 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
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        {
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          "data": {
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              "<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\">08:43:43 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">57</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.294</span>                                 \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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              "</pre>"
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        {
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              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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              "<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\">08:46:24 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">59</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.161</span>                                 \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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              "<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\">08:46:25 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
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              "<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\">08:46:59 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">60</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.151</span>                                 \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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              "<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\">08:47:51 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
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              "<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\">08:48:42 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">62</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.086</span>                                 \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
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              "<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\">08:49:15 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
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              "<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\">08:50:07 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
              "</pre>"
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              "<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\">08:50:59 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \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\">08:51:50 UTC </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">66</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.062</span>                                 \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
              "</pre>"
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              "<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\">08:51:51 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \n",
              "</pre>"
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        {
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            "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\">08:52:27 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \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\">08:53:18 UTC </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> Simulations                                              \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\">08:53:52 UTC </span>Best Result: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.061516190245546</span>                                    \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Best Parameters: w_0: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5498419634833278</span> w_1: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5</span> w_2:             \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.296267581582306</span> w_3: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.8380428776617914</span> w_4: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.6</span> w_5: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.6</span> w_6:   \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.6</span> w_7: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.452243062356684</span> w_8: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.51043959613503</span> w_9:              \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.4942004790785968</span> w_10: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.3010925645718425</span> w_11: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.6</span> w_12:        \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.1789774737217065</span>                                                 \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "results = design_space.run(fn_pre, fn_post, verbose=True)\n",
        "df = results.to_dataframe()"
      ],
      "id": "124389ef"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Results\n",
        "\n",
        "The best result can be extracted directly from the optimizer object.\n",
        "Plotting this, we see what the optimizer has returned as the optimized\n",
        "structure for this design of Y-junction."
      ],
      "id": "9bb9022e-02b6-4567-9404-f3350df6a92b"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best fitness: -2.061516190245546"
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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DFVc0qHCKrB5LsAIAAABg2AXDEW3YX6VXd5Yz\nOgVJVZVVoueyluulwiVa0HpQS1v2aHS4Uc5o+/LA+RG/8iL++CiWRJZZxnGGoSwjLM/x5aud9jxF\n7TZFbHaFbE75j08WPJBll3vSNVxxu90mdj5xvGMAAAAADJvqxla9srNcb+45ytwpGFZBe5bez5ut\n93NnaWKwRktb9mi2/4iajUh8eWBPOKCQzalWu1tt9qzB3yZkGPJEg3IZQ3tvO42oIgMcyRGTZYRV\nEPYN6bgBu0sBe9agX2czovIcHwnkMCIK2ZxqdOTKb3crLLuCNpeOuIoVsA99hEk8XAmnzjLrBCsA\nAAAAksowDO2orNdL245oa3md1d3BSGezqdxdqnJ3qXIibVrk26clvn0qDLccH23RpsKITwWRVvnt\nbrU63AMOOVxGWAURn4JDnP8jbHMobHMoqr6DHeP482GbQzIkh5H4rXQ2GfKEA6pxFQ14ct/YqB9P\nNChDah/148hT2O5QtatYR2ylao7apGhEYRNClZiow6VoNCqH/Ka1ORQEKwAAAACSIhCKaP3eo3p5\n+xEda2y1ujtAN62ObL1VsEBv58/X1MBRLW3Zo+ltlXJ2GMWSE2pT0OZSq8OtgM3V5ygWh9qDjQZX\n/pD7ZsimzbkzJB3pdZ+ww65dnkma4+99n4FyGFGNDnllN6J9Bis2wzg+T02bnEZEYZtDTQ6P2o5P\nBrwzZ7I25s5UZdZoFTTskyvY3E88lJio3alwIJCElgePYAUAAACAqby+Nr20vVyv7argdh+kBcNm\n04Hs8TqQPV75YZ8W+/ZqsW+fciNtchsheaJtKgq3KCq7/A63Wu3ufkd1PF28QnuzJyTcp4jNrpDd\npTP6CFYk6fFRZ8odDcpmJDJrSbuTW3bqjKYtfe7TvrJSmzzRoGwy1HZ8vpSQzakGV7425s7Ulpzp\nanMM37wnoVBo2I7VF4IVAAAAAKYor2/W81sPa8P+KkWiiX/IA6zU7MzV64WL9UbBIs30l2upb4+m\nBKrkMKLyRNvkiQSUG/ErYHep1Z6tYC9LNgdtrmELGRKZF6WjkK2XaMAwlG0ElRMJyGWEFbHZ1eLw\nyG93K2Kza49nojbmztJh99gRvaoSwQoAAACAhBmGoZ2V9Vq79bB2VNRb3R3ANFGbXbtzJmt3zmQV\nh5q01LdXC337lW0PHg8b2lQcblbEZm+fi8WeGivUmMFx/FYoTyQguwwF7FlqcOQraHOp2ZmjTbkz\ntDlnhlqcOVZ3NSUQrAAAAAAYtGjU0MZD1Xpu8yEdrmO5ZGS2BleBXiw6Ua8WnKC5/sNa2rJHZaG6\n47fHBJQbaVNexN8+iWyaK4z45DQiisquVke2/MdvezrgLtPGvJnamz1BxgAntx0pCFYAAAAADFg4\nEtXb+47puc2HVN3EhLQYWcJ2p7bmTtfW3OkaG6zXEt9ezW89qKxo6PiErqkxmepQRGWX1+lRwJYl\nv8OtzTnTtSlvprzOoU/Im6kIVgAAAAD0KxiO6PVdFVq75bC8ren/4REYqqqsEj2XtVwvFS7RgtaD\nWtqyR6NDjXIa7RM2B0xcXjjZAnaXorKp1lWoiM2h8qwx2pg7U7tyJg94qemRjGAFAAAAQK/aQmG9\ntrNCa7ccUnNbaqzAAaSSoD1L7+fN1vu5szQxWKMFrQcVsDl1xF1qddcGbEfOFJWGvArZHNqSM101\nWcVWdymtEKwAAAAA6MYfDOuVHeV6futh+QIEKkC/bDaVu0tVnkaBSkyb3a3nipdb3Y20RbACAAAA\nIK4tFNZL247o+a2H1RoMW90dAEh5BCsAAAAAFAhF9MqOcv1ryyFGqADAIBCsAAAAACNYMBzRazsr\n9Nzmg8yhAgAJIFgBAAAARqBINKp1uyv19MaDamSVHwBIGMEKAAAAMIJEDUMb9lfpyff2q6bZb3V3\nACDtEawAAAAAI4BhGNpypFZPvLtfFQ0tVncHADIGwUofGhoa9NZbb6miokJHjx6VYRgaN26cVqxY\noUWLFpl+vKNHj+rZZ59VeXm53G63Zs2apQsvvFAej2dY2wAAAEBm2Vfl1d/f2at91Y1WdwUAMg7B\nSg/++Mc/6he/+IU2b96saDTa4z7Lli3T//3f/2n58qGv9d3a2qqvfvWruueee7odr7CwUD/96U/1\n//1//1/S2wAAAEBmOdbo0+Mb9mnjoRqru4IRzG6zKctpl8thl8vpkNPe/tjpsMlpt8vpsMtht8lh\nj323yW5r37bbJPvxbbvNJlv8e3u7kmSzSTbZdPx/8Z/FtmwdO9NpIz1Mbhjd5/OFxdOGqSeDd3hz\ntVrqA4qEJE9Bjqlth8NhlVeb2mTCCFZ68Morr2jjxo2SpNGjR+u8887TzJkzZbPZ9Pbbb2vt2rXa\nsGGDzjjjDP3jH//Qhz70oYSPFQ6Hdemll+pf//qXJOmMM87QypUr5fP59Nhjj+nw4cO6/vrr1djY\nqP/4j/9IWhsAAADIHI2tAT31/gGt212pqGFY3R2kKZtNynW7lJPlUq7bqdzs9sc5WU5lZzmVk+WU\nJ8upbJdT2S6H3C6Hsl1OuZ0OZbkc7d+ddjnsdqtLSW97x/T59Akzpw9TRwbvqcodKm9rkt8f1djC\nXFPbDgaDKje1xcQRrPRi8eLF+sY3vqHLLrtMTmfnX9Mrr7yiSy65RC0tLfrUpz6lPXv2qKioKKHj\n/OxnP4sHIj/4wQ906623xp/7/ve/rzVr1uj555/XV7/6Va1atUoLFixIShsAAABIf4FQRM9vPaS1\nWw4rEI5Y3R2kqLxslwo9bhXmuFWUk6WCHLcKPVkq8GQpLztL+Z4s5We7lON2xUeFAOgdwUoPvvzl\nL2vhwoWy95Ksrly5Uj/84Q910003qba2Vo8++qiuv/76QR+nra1NP/zhDyW1jzLpGIhIUk5Ojv7y\nl79o+vTpamtr03//93/roYceMr0NAAAApLeoYeitvUf1+Lv7WTp5hLNJKsp1a3S+R6PzPRqVl62S\nvGwV52arJDdbRbluZTkdVncTMIXL5bK6C5IIVnp0wgkn9LvPmjVrdNNNN0lS/LahwVq7dq3q6+sl\nSV/84hd73KesrEyXXnqpHnjgAf3zn/9Ua2urcnJyTG0DAAAA6WtnZb0efXuPyutZ6WckKcxxa1xh\njkoLczS2oP17aUGOSvKy5XJw6w0yn9/vl9vttrobkghWEtbx9qBAILG/Cjz//PPxx+edd16v+51/\n/vl64IEH5Pf79cYbb+j88883tQ0AAACkn+rGVj36zh5tPlxrdVeQRPnZLk0oydf44lyVFeVqfHGe\nxhXmKMedGn+pB6zg8/nU1NRkdTfiCFYS9Oabb8YfT5+e2GRB27ZtkySVlpaqtLS01/0WLlzY6TUd\nQxEz2gAAAED68AfDenrjAb20/YgiUSamzSRjCjyaPCpfk0bla2JJviaW5KkwJzX+Ig+kCp/PJ6/X\na3U3OiFYSYBhGLrjjjskSTabTR/96EcTaufQoUOSpEmTJvW5X8fnDx48aHob/WloaOj1uVR7QwMA\nAGSqaNTQG3sq9fi7+9TSFrK6OxiiAk+WppcWauroAk0eXaDJo/OVyygUoE9dQ5VE7x4xG8FKAn76\n059q/fr1kqTrrrtOc+fOTaid2NCl/Pz8PvfLy8uLP25ubja9jf6UlJQMan8AAACYa8+xBj28fjfz\nqKQpm02aWJKvGaWFmn78qyQvWzZW3AEGrGuoUlBQoMrKSus61AHByiC98MIL+vrXvy5JmjJlin7+\n858n3FZbW5skKSsrq8/9Ok7IE3uNmW0AAAAgNXl9bXr0nb3asL/K6q5gEOw2myaPztfsccWaOa5I\nM8cWyZPFRy8gUV1DlaKiopRZEUgiWBmUjRs36vLLL1ckElFBQYH+/ve/q6ioKOH2PB6Pmpqa+h2+\n1PF5j8djehv9ia061BOv16vp06fL4WDJNgAAALOEIlG9uPWwntl0UIFwxOruYADGF+dq7vgSzR1f\nolnjipTt4qMWYIaeQpXc3FwFg0HrOtUFZ/sA7d69W6tXr1ZjY6M8Ho/++c9/aunSpUNqs6CgQFVV\nVf3OZtzx+a63/JjRRn+Ki4v73cflcikcDg+qXQAAAHS39UitHn5rt2qa/FZ3BX3IdTs1b8IoLZw4\nSnPHlzDJLJAEvYUqqYZgZQAOHjyo888/X9XV1crKytJjjz2ms846a8jtTp06VXv27NGRI0f63K+8\nvDz+eNq0aaa3YZZgMCifz5eSb3QAAIBUV9fs1yNv7dYmlk9OWZNG5WvRpNFaMHGUpo4ukN3OHClA\nsqRLqCIRrPSrsrJS5513no4cOSKn06mHHnpIF154oSltL1y4UGvXrlVtba2OHj2qsrKyHvfbtGlT\np9eY3YaZYm98s9/wjIYBAACZKhSOaO3Ww3p200GFIlGru4MOHHab5pQV64TJY3TC5NEqzs22ukvA\niJBOoYok2a3uQCqrqanReeedp/3798vhcOi+++5LeGnlnqxatSr+eO3atb3uF3suLy9Pp556qult\nmCEU+mDJP6/XK5/PZ1rb4XC4U/sAAACZYlt5nb73j7f0z/f2E6qkCJfDrhOnlur/O3uB7vj4Wbpp\n9VKtnDeRUAUYJukWqkiMWOlVQ0ODLrjgAu3cuVM2m01/+MMfdNVVV5l6jHPPPVelpaWqrq7WL3/5\nS1133XXdllw7dOiQnnjiCUnSpZdequzsbNPbMEMkEum0MpFZI1d8Pp8CgYCcTt6qAAAgc9S3tOmR\nt3Zr46Eaq7sCSW6nQ4smjdbSaaVaMGGU3C4WZgCskI6hisSIlR61tLTooosu0qZNm2Sz2XTPPffo\nk5/85KDb+elPf6pbbrlFt912W4/PZ2Vl6dvf/rYk6Z133tE3vvGNTs83Njbq4x//uILBoFwuV3xf\ns9swi9Pp7LRK0lBHrnQ9qQAAANJdJBrV81sO6b8fW0+oYjGH3abFk0fr+rMX6vZrztT15yzUiVNL\nCVUAi6RrqCIxYqVHn/vc5/TWW29JkmbNmqV9+/bplltu6XX/WbNm6frrr+/289/85jfatWuXCgsL\n9Z3vfKfH137hC1/QCy+8oL///e/60Y9+pKeeekorV66Uz+fTE088obq6OknSr371K82cOTNpbZgl\n9saPnRCJjlzpelKl0hrlAAAAidhX5dUDb+xSRUOL1V0Z0WaPK9LymWVaMmWMct38GxNIBekcqkgE\nKz2qqKiIP969e7duv/32Pvc/77zzegxWBsJut+vBBx/Ud77zHf3sZz/Tli1btGXLlvjzZWVl+t//\n/V9dfvnlSW3DTEMNV7qeVG63m1uBAABA2vIFQvr7O3u1bnel1V0ZscYUeLRiZplOmTFOo/I9VncH\nQAfpHqpIBCs9uvHGGwe18s/UqVN7/PlXvvIV1dXV9TunSVZWln74wx/qlltu0YsvvqgjR47I7XZr\n9uzZOuuss+Rw9D8c0Yw2zJRouNLTSdXSwl91AABA+jEMQ+/sr9Ijb+1WSxsT8Q83t9Ohk6aP1Wmz\nyjS9tLDbPIQArJcJoYpEsNKjj3/846a089nPfnZQ+xcWFurSSy8d0jHNaMMsgw1XejupCFYAAEC6\nqW5q1QNv7NLOynqruzLiTB1ToNNnj9ey6WOV7eLjDpCqMiVUkQhWkGQDDVcy6aQCAAAjVzgS1dot\nh/T0poMKs3zysMl2OXTKzDKdMWe8JpbkW90dAB2Ew+FuP8u0z38EK0i6/sKVTDupAADAyLSvyqv7\n1+3UUW/iqyJicCaW5OmsuRN18gxGpwCpKhQKdQpXMvHzH1cfDIvewpWujzPhpAIAACOLPxjWPzbs\n1Ws7K2RY3ZkRwG6z6cRppTp73kTmTgHSRCAQkM/XHjpn4uc/ghUMm77CFSlzTioAADBybDxYrQfX\n71Zja8DqrmS8vGyXzpwzQWfNm6iiHLfV3QEwSJn8+Y9gBcOqa7gSk0knFQAAyHze1oAeenOXNh6q\nsborGW9CcZ7OXTBJJ08fK5dzeFe6BDB0Lper288y7fMfwQoAAAAwQFHD0Bu7K/XYO3vlD3afkBHm\nmTehRBcsnKy540u43QdASiNYwbDqOlFRTH9LMQMAAFiturFV963boT3HvFZ3JWPZbTadPGOszl84\nmdV9gAwRCoW6/SzTPv8RrGDY9DT7s9T/UswAAABWikSjen7LYT258QBLKCdJltOu02dP0PkLJ6sk\nL9vq7gBIgkz+/EewgmHR35JamXhyAQCA9Hekrln3vr5DR+qare5KRsrJcurs+ZN0zvyJysvOsro7\nAJLE7XZn9Oc/ghUkXX+hSm+rBaX7yQUAANJXKBzR0xsP6l9bDilqsIiy2fKzXTp/4RSdNW+Csl18\nJAEyWdfJazPx8x9XMSRVf6FKTCaeXAAAID3tq/Lq3td3qKqx1equZJxCT5YuOGGKzpwzQVms8AOM\nCE6ns9s8K5n2+Y9gBUkz0FAlpreTCwAAYDi0hcJ6/N19emV7uRijYq6iHLdWnzBFp88ez5LJACRl\nVrhCsIKkGGyoEkO4AgAArLCzsl73vb5DdS1tVncloxR4snTh4qk6g0AFQA8yJVwhWIHpEg1VYrqe\nXIFAQB6Px8wuAgAASJL8wbAee3uPXt9daXVXMkp+tkurT5iqM+dyyw+AvmVCuEKwAlMNNVSJ6Xpy\n9bT2OQAAwFBsPVKr+9ftlLc1YHVXMoYny6nVi6bo7PmT5HYRqAAYmHQPVwhWYJpwOGxKqBITe21V\nVdVQuwYAABDnC4T0yFu79dbeY1Z3JWNkOe06d/5kXbBosnLcrv5fAABdpHO4QrACUzgcDgWDQWVn\nZ0saeqgSk5ubK7fbPeR2AAAAJGnToRr99Y2davIHre5KRnDYbTpjzgRdtHiqCnP4NxuAoUnXcIVg\nBabouDa5WaFKjNPp7Lb2OQAAwGC0tAX10Prd2rCfkbBmWTZ9rD5y4gyNKWAuPADmScdwhWAFpjI7\nVIlxOnmrAgCAxLx3sFoPvrFTzW3M2WaGOWXFuvTkmZoyusDqrgDIUOkWrvBpFabJyspK2Tc6AAAY\neZr8QT305i69d7Da6q5khAnFebr05JmaP6FENpvN6u4AyHDpFK4QrMAUoVCIUSUAACBlvLu/Sg+u\n36UWRqkMWYEnSx85aYZOnVkmu51ABcDwSZdwhU/CMEUkErG6CwAAAGryB/Tgm7v0/sEaq7uS9lwO\nuy5YNEUXLJqsbBcfGwBYo7dwJZXm4eQKCQAAgLRnGIbePVCtB9/cJV+AUSpDtWJmmdacNF1FudlW\ndwUAegxX8vLyLOxRZwQrAAAASGtN/oAeeGOXNh5ilMpQTS8t1JUrZjMxLYCU0zVcaWpqSplRKwQr\nAAAASEuGYWjDgSo99OZuRqkMUXGuW5eePFPLpo1lYloAKatruOJ2uy3szQcIVgAAAJB2GKViDpfD\nrlWLpmjVCVOU5XRY3R0A6FcsXKmvr7e4Jx8gWAEAAEDaYJSKeZZOHaPLl8/SqDyP1V0BgEHJzc1V\nOBxWU1OT1V2RRLACAACANMEoFXOUFeXqyhWzNXd8idVdAYCEeTweBQIBq7shiWAFAAAAKY5RKubI\ndjl0ydLpOnv+RDnsdqu7AwBDFgqlxn8TCFYAAACQshilYo5TZozTpSfPVGFOakz0CACZhGAFAAAA\nKYdRKuYYX5yrq0+do1njiq3uCgBkLIIVAAAApJTG1oAefJNRKkPhdjp0yYnTdQ63/QBA0hGsAAAA\nICUYhqEN+6v00Ppd8gXCVncnbZ00rVRXLJ+lotxsq7sCACMCwQoAAAAs19jaPpfKpsOMUknUmHyP\nrj51juZPHGV1VwBgRCFYAQAAgGUMw9A7+6v00Ju71BpklEoinHabVp0wVatPmKIsp8Pq7gDAiEOw\nAgAAAEs0tgb01zd2avPhWqu7krZmjyvSNafP1bjCXKu7AgAjFsEKAAAAhpVhGHp73zE9vH43o1QS\nlOt26Yrls3TKzHGy2WxWdwcARjSCFQAAAAwbr69N97+xU1uP1FndlbR16qwyXXbyTOVlZ1ndFQCA\nCFYAAAAwDAzD0Bt7jupvb+1WWyhidXfSUmmBRx8/fa7mlJVY3RUAQAcEKwAAAEiq+pY23bduh3ZU\n1FvdlbRkt9m0+oQpumjxVLmYnBYAUg7BCgAAAJIiahh6bWeF/v7OXgXCjFJJxLQxBfrEGfM0oTjP\n6q4AAHpBsAIAAADTVTe16r7Xd2jPMa/VXUlLbqdDH102Q2fNmyg7k9MCQEojWAEAAIBpolFDL20/\nosff3adQJGp1d9LSwkmjdM2pc1WSl211VwAAA0CwAgAAAFMcbWjRfet2an91o9VdSUt52S5duWK2\nlk0byxLKAJBGCFYAAAAwJJFoVM9tPqRnNh5QOGpY3Z20dMqMcbrilNnKy3ZZ3RUAwCARrAAAACBh\nh2qbdO9rO1TR0GJ1V9JSSW62PnH6XM2fOMrqrgAAEkSwAgAAgEELhiN66v0DWrv1kAwGqSTk7HkT\ntWbZDGW7+Cc5gD7MvNHqHqQslys1RvlxFQcAAMCg7Dpar/vX7VRNk9/qrqSlsYU5uvb0eZo5rsjq\nrgBA2vL7/XK73VZ3QxLBCgAAAAaoNRDSY+/s1brdlVZ3JS3ZbTZdsGiyLl4yTS6nw+ruAEDa8vl8\nampqsrobcQQrAAAA6Nf7B6v14Ju71OQPWt2VtDShOE/XnTlPU0YXWN0VAEhrPp9PXq/X6m50QrAC\nAACAXnl9bXpo/W5tPFRjdVfSksNu00WLp2r1CVPldNit7g4ApLWuoUogELCuMx0QrAAAAKCbqGHo\ntZ0V+seGvWoLRazuTlqaMjpf150xXxNK8qzuCgCkva6hSkFBgSorU+PWVIIVAAAAdFLZ0KL71+3U\n/upGq7uSlpwOuy5ZOk3nL5wsh51RKgAwVF1DlaKiopRZEUgiWIFJHA4mYAMAIN0FwxE9u+mg/rXl\nkCJR1lBOxLQxBbruzPkqK8q1uisAkBF6ClVyc3MVDKbOnF8EKzCFy+VSOBy2uhsAACBBOyrq9cCb\nLKGcKJfDro+cOF3nLpgsu91mdXcAICP0FqqkGoIVmCYYDMrn86XkGx0AAPSsyR/Uo2/v0dv7jlnd\nlbQ1o7RQ1505X2MLc6zuCgBkjHQJVSSCFZgs9sY3+w3PaBgAAMwVNQy9sbtSf39nr1qD/Hc2ES6H\nXR9dNkNnz5vEKBUAMFE6hSoSwQpMEgqF4o/NDlcIVQAAMFd5fbMeeGMXk9MOwYyxhfrkGfNVyigV\nADBVuoUqEsEKTBKJRJSVlRXfNitc8fl8CgQCcjp5qwIAMFRtobCeev+AXtx2RFGDyWkT0T5KZabO\nnj9RdhujVADATOkYqkgEKzCR0+lUQUFB/EQYarjS9aQCAACJMQxDGw/V6JG3dqvBF7C6O2lr1rgi\nXXfGPI0pYJQKAJgtXUMViWAFJou98YcarnQ9qVJpjXIAANJJdWOrHlq/W9sr6qzuStrKcraPUlk5\nj1EqAJAM6RyqSAQrSIKhhitdTyq3282tQAAADFIwHNGzmw5q7ZZDCke57SdRjFIBgORK91BFIlhB\nkiQarvR0UrW0tCSjiwAAZCTDMLT5cK0efmu36lvarO5O2spy2rXmJOZSAYBkyoRQRSJYQRINNlzp\n7aQiWAEAYGCONfr0yPo93PYzRIxSAYDky5RQRSJYQZINNFzJpJMKAIDh1hYK6+mN7av9RLjtJ2FZ\nTrsuXTZTZzGXCgCYJhwOd/tZpn3+I1hB0vUXrmTaSQUAwHAxDENv7zumx97ZqyZ/0OrupDVGqQBA\ncoRCoU7hSiZ+/iNYGaDa2lodO3ZMklRSUqLx48cn5ThtbW06evSo3G63xo4dK4fDYUkbZustXOn6\nOBNOKgAAhsPBmkY9vH63DtQ0Wd2VtMYoFQBIvkAgIJ/PJykzP//Zre5Aqnr//ff1s5/9TNdcc41m\nzJihMWPGaNGiRVq0aJG+/e1vm3681157TatWrVJeXp6mT5+uCRMmqKSkRNdff72OHj06bG0kU25u\nroqKiuLbXq83I08qAACSydsa0J9e3abb/7mBUGWIZo0r0jc/ukJnz59EqAIASZbJn/8YsdKLm266\nSevWrYtvFxcXq6GhISnH+r//+z/dfPPNikajstvtmjJlilpbW1VTU6M//OEP+sc//qEXX3xRixcv\nTmobw6HryJWYTDqpAABIhmA4ohe2HtZzmw8pEI5Y3Z20xigVABg+Lper288y7fMfI1Z6ceKJJ+pL\nX/qS7r//fu3Zs0eVlZVJOc4rr7wSD0ROO+007dmzRwcPHlR1dbWeeeYZjR49WvX19brkkkt6XR3H\njDYAAEBqih6fR+W2R9friff2E6oMEaNUAABmY8RKL37xi1902m5ra0vKcb761a8qGo1q3LhxevLJ\nJ1VcXBx/7sILL9Rf//pXrVq1SuXl5brzzjv1ne98JyltDJeuExXF9LcUMwAAI9HeY1797e09OlTL\nLT9D5XY6dOnJM3Xm3AkEKgAwjEKhULefZdrnP0asWGjjxo3asGGDpPZbjzoGIjEXXHCBVqxYIUn6\n/e9/n5Q2hktPsz93nXMlNqERAAAjWXVTq+55YbPufPpdQhUTzC4r1jcvPUUrufUHACyTyZ//CFYs\n9M9//jP+eM2aNb3uF3vuyJEj2rhxo+ltDIfeltTqaULbTDm5AAAYrCZ/UA+9uUu3PbpeGw/VWN2d\ntOd2OnTNaXP0pQuXanS+x+ruAMCI5Xa7M/rzH7cCWSgWcOTm5mr+/Pm97rd8+fJOr1myZImpbSRb\nf+uU97YUc6YMCwMAoD+BUEQvbDusfzExrWnmji/RtafP1SgCFQCwVNfJazPx8x/BioV2794tSZoy\nZYpsfQxLnTp1avzxrl27TG8jmfoLVWIy8eQCAKA/kWhUb+yu1JPvH1CTP2h1dzJCtsuhy5fP0umz\nx/f5byMAwPBwOp3d5lnJtM9/BCsWii3fXFJS0ud+HZ/vOvGrGW30p69lpvtqa6ChSkxvJxcAAJkm\nahh6d3+V/vn+ftU0+a3uTsaYP2GUPnH6XJXkZVvdFQBAPzIpXCFYsVDsXjKPp+8hqh2f77pcshlt\n9Ke/0KYngw1VYghXAACZzDAMbT1Sp8ff3aeKhsH99xi982Q5dcXyWTp1VhmjVAAgjWRKuEKwYiGH\nwyFJikT6vpe64/NOZ+f/y8xow2yJhioxXU+uQCDQb3Bkir2/6fv5mTf2+tRjtY/1+dLLRl+WSI8A\nABnCMAztOtqgf763X/urG63uTkZZPHm0rj5tropy3FZ3BQCQgEwIVwhWLJSXl6e6ujr5/X0PAW5t\nbe30GrPb6E99fX2vz3m9Xk2fPj2+PdRQJabrydXT2ucAAKSDPcfaA5U9x7xWdyWj5GW7dNWKOTpp\nWimjVAAgzaV7uEKwYqFx48bp0KFDqqys7HO/o0ePdnqN2W30p7i4eED7hcNhU0KVmNhrq6qqEm4D\nAACr7K9u1D/f26+dlb3/gQKJWTZtrK5cMVv5niyruwIAMEk6hysEKxaaN2+e3nrrLVVUVMjv9/d6\nu8uePXs6vcbsNszgcDgUDAaVnd0+WdxQQ5WY3Nxcud0M7QUApI89xxr09MaDBCpJUOjJ0tWnzdWS\nKWOs7goAIAnSNVyxW92Bkey0006T1D7SY/369b3u9/rrr0uSbDabVqxYYXobZui4NrlZoUqM0+ns\ntvY5AACpxDAM7ays111Pv6ufPv0eoUoSnDZ7vL592QpCFQDIcLm5uSoqKopve73e+KItqYpgxUJr\n1qyJTz5777339rhPKBTSgw8+KElasWKFxo8fb3obZjI7VIlJ9oS7AAAkwjAMbTlSq5889a5+/uz7\n2s08KqYblZetL124VNedMU85bv7QAgAjQbqFKwQrSbRnzx5t3bpVO3bs6PH50tJSfepTn5Ik/eUv\nf9Ebb7zRbZ/bbrstPj/K1772taS0YZasrKyUH6IFAIAZItGo3t53TN//x9v61dpNrPSTBDZJ58yf\npG9dukJzx5dY3R0AwDBLp3CFYQC9aGhoUEVFRXw7GAx2em7r1q3xbYfD0eO8JR/+8Ie1a9cuFRYW\ndprUtaPvf//7eu6551RRUaHVq1frm9/8ps4++2z5fD7de++9+tOf/iSpfWTKRz/60aS1MVShUIhR\nJQCAjBcMR7R+71Gt3XJYtc19r8iHxJUV5eraM+Zpemmh1V0BAFgoXeZc4ZNwLx599FF99rOf7fG5\nxx57TI899lh8Ozc3Vy0tLQkdZ9y4cXr22Wd1+eWXa/fu3brlllu67XPppZfqvvvuS2obQxWJRJLW\nNgAAVmv2B/XKznK9sqNcLW0hq7uTsRx2my5aPFWrT5gqp4OB1QCA3sOVVJqHk2ClFyUlJVqwYMGA\n9s3Jyenx57Nnz5bT6VRBQUGfr1+4cKE2btyov/71r3r66ad15MgRud1uzZ49W1dddZVWrVrVbx/M\naAMAAHRW1diqF7Ye1vq9RxWKRK3uTkabXlqoa0+fq7LiPKu7AgBIMT2FK3l5qfPfC4KVXlx22WW6\n7LLLhtTGE088MeB9PR6Prr/+el1//fUJH8+MNgAAGOkMw9COynq9tP2Ith2pk2F1hzJctsuhNSfN\n0FnzJspus1ndHQBAiuoarjQ1NaXMqBWCFQAAAEmBUPv8KS/vOKJj3laruzMiLJ48WledOkfFudlW\ndwUAkAa6hitut9vC3nyAYAUAAIxoxxp9em1nhd7cc1T+YNjq7owIhZ4sXXXqHC2dWmp1VwAAaSYW\nrtTX11vckw8QrACA2pdOPVjTpEO1zVo4cZRKC3ueOwlAZohEo9p0uFav7ijXrqMNVndnRDlz7gRd\numymPFn8MxQAkJjc3FyFw2E1NTVZ3RVJBCsAoFAkqt+/tEWbDtdKkv72trR8+jhdccps5WWnxn2b\nAMxR0+TXG3sq9eaeo2psDVjdnRFlQnGePn76XJZQBgCYwuPxKBBIjf+WE6wAGNGiUUN3P79JOyo+\nGEpoGNJb+45pe0WdrjhltpZNH8uEikAaC4Uj2ni4Rut2VTI6xQIuh12XLJ2u8xZOksPOEsoAAPOE\nQiGruyCJYAXACPfaropOoUpHzW0h/fGVbfrX5kO6YNFkLZ1aqiyno8/2DMNQOGooGjWU5bTLRiAD\nWMIwDB2qbdb6vUe1Yf8x+QLMnWKF+RNG6ZrT5mh0vsfqrgAAkDQEKwBGrCZ/UI+/u6/f/SoaWvSn\nV7frwTd3aXZZsaaXFmpUnkc5bqdC4aia/AEdqWvRkfpmHW3wKRCOSGpfQnRsYY7mji/R0qmlmjK6\nINklpYztFXXafbRBhiGtnDdRJXms+IHh0eBr09t7j2n9vqOs7GOhQk+WPnbKbJ04rZSAGQCQ8QhW\nAIxYT7y7b1ArgLSFItp8uFabj8/FMpD9D9U261Bts57bfEhTxxTowydO1/wJoxLtcsoLhSN64M1d\nenPP0fjPXtx+RJefPFNnz59kYc+QyXyBkN4/WK139h3TnmNeGVZ3aASz2aSz503Sh0+czuS0AIAR\ng//iARiRmvxBvbX3aP87muhgTZP+97mNWjRptD5x+lwV5riH9fjD4aH1uzuFKpIUjkT10Prdyvdk\n6aRpYy3qGTJNWyisLYdr9c7+Km2vqFMkSpxitaljCvTx0+Zq0qh8q7sCAMCwIlgBMCKt212psEUf\nxLYcqdX//OMtXXfGPJ0weYypbTf5gzrm9am6qVVeX0DNbUFFDUNup1MTSnI1p6wkabflvL6rQut2\nV/b6/J9e3a6xBTmayIcuJMgfDGvz4Rq9f7Ba2yrqFY5Ere4SJOVkObVm2QydMWcCE30DAEYkghUA\nI04kGtVrO8st7UNLW0h3P79ZZ8+bqEtPntnvpLg9MQxDR70+7aio194qrw5UN6rRH+z3dbPLinXR\n4qmaO74kka73yNsa0CNv7e5zn3Akqkfe3qN/v3Apcy5gwBpbA9pypFabDtVoR2U9I1NSzGmzx+vS\nZTOUl51ldVcAALAMwQqAEWfz4Vo1+FJjzfuXd5Rry5FarTlphk4awLLOhmHoYE2T3j1QpfcP1qje\n1zboY+4+2qDdRxt0wuTR+sTp81TgGfoHoqfe269guP/RA7uPNmjz4VotnmLuSB1kDsMwVOn1HZ/P\nqEYHa5qs7hJ6MGlUvq4+dY6mlxZa3RUAACxHsAJgxHl73zGru9BJXUub/vDKNj3+7j6dOqtM8yaM\n0qSSPLmOj2IJhiOqqG/R1vI6vXegSscazVnpZPPhWh2seUvXn71Qs8uKE26nsqFF6/b0fgtQV4++\ns0cLJ42Sw25P+JjILG2hsHZVNmhbeZ22ldclFBhieHiynFpz0gydOWeC7HZGngEAIBGsABhhAqGI\ntpXXWd2NHtW1tOnJ9w/oyfcPyCbJ7XLIbrOpdRArFw1Wkz+o/33ufX1m5QKdmMDEsoZh6G9v75Ex\niLszapr8eu9gtU6ePm7Qx0NmiEYNHa5r1s7Keu2sbL+VjVt8UptN0umzx+sjJ81Qvgmj3AAAyCQE\nKwBGlG0VdQqlwYSXhtqXax4O4aih3720VZ+OGlo+Y3Bhx7sHqrWjon7Qx1y75bCWTRvLXCsjRPT4\nfEB7jjZo1/GvwSx1DmtNG1Ogq06doymjC6zuCgAAKYlgBcCI8v7Baqu7kJIMSX9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9qY0H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\nIliBxXJzcy0JV9p+qJxO7t0FAADWMQxDJ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          }
        }
      ],
      "source": [
        "best_params = results.optimizer.max[\"params\"]\n",
        "print(f\"Best fitness: {results.optimizer.max['target']}\")\n",
        "sim = fn_pre(**best_params)\n",
        "sim.plot(z=0)\n",
        "plt.show()"
      ],
      "id": "8f79355a"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can then create a plot to evaluate if the Bayesian optimization has\n",
        "converged on a fitness value. The first 30 iterations are from the\n",
        "random initialization, so the fitness values are expected to be more\n",
        "varied. The fitness has converged before the end of the remaining 70\n",
        "iterations; an early stop criteria could have been included to finish\n",
        "the optimization sooner."
      ],
      "id": "3fcae5b9-a19d-4253-b077-a2a963dace64"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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S2YJ5xLS+tWBmqqZigC44/qt66//+o5vO/Zmmjdoj4f1fPfwUgjIAAIACRlgG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\nKGln1drxxx/vuHYq1kD6OQVjfgKEpDkN+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          }
        }
      ],
      "source": [
        "ax = df[\"output\"].plot(xlabel=\"Simulation Number\", ylabel=\"Fitness\")"
      ],
      "id": "9a4b2738"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The mean power at the reflected and transmitted monitors can be\n",
        "calculated and compared to the paper from `Gao et al.`"
      ],
      "id": "720e1e79-fac2-4509-947d-f2ba80ac5a0d"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Reflected Power: 0.001 (Paper: 0.004)\n",
            "Mean Transmitted Power: 0.483 (Paper: 0.460)"
          ]
        }
      ],
      "source": [
        "idx_best_result = df[\"output\"].idxmax()\n",
        "best_sim_filename = results.task_paths[idx_best_result]\n",
        "best_sim = td.SimulationData.from_file(best_sim_filename)\n",
        "\n",
        "power_reflected = np.array(\n",
        "    np.squeeze(np.abs(best_sim[\"mode_11\"].amps.sel(direction=\"-\", mode_index=0)) ** 2)\n",
        ").mean()\n",
        "power_transmitted = np.array(\n",
        "    np.squeeze(np.abs(best_sim[\"mode_12\"].amps.sel(direction=\"+\", mode_index=0)) ** 2)\n",
        ").mean()\n",
        "\n",
        "print(f\"Mean Reflected Power: {round(power_reflected, 3)} (Paper: 0.004)\")\n",
        "print(f\"Mean Transmitted Power: {round(power_transmitted, 3)} (Paper: 0.460)\")"
      ],
      "id": "fbc52841"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Final Optimized Design\n",
        "\n",
        "Finally, we can simulate the optimal solution with an additional\n",
        "`FieldMonitor` to visualise the field distribution throughout the\n",
        "splitter."
      ],
      "id": "5e34bd04-d968-478c-aa60-2073df8d1da8"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "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\">08:53:53 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'BO_notebook_final_sim'</span> with resource_id              \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-954d6d79-e621-480f-90d7-1898454dc380'</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-954d6d79-e621-480f-90d7-1898454dc380\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-954d6d79-e62</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-954d6d79-e621-480f-90d7-1898454dc380\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">1-480f-90d7-1898454dc380'</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\":\"154081747a6d497ba50bef325b541e20\",\"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\">08:53:54 UTC </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.115</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\">08:53:59 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\":\"2bb710d4ba674ee2b1a217fc0654eaa1\",\"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\">08:54:06 UTC </span>status = preprocess                                                \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\">08:54:11 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\":\"3cca855196bc4a4090433076061e1af0\",\"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\">08:54:19 UTC </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">76</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\">08:54:20 UTC </span>status = postprocess                                               \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"5f12ae4fbb4e4a9d82478a4d6960a323\",\"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\">08:54:22 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\">08:54:24 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-954d6d79-e621-480f-90d7-1898454dc380\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-954d6d79-e62</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-954d6d79-e621-480f-90d7-1898454dc380\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">1-480f-90d7-1898454dc380'</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\":\"139f162a7ec945c6bd380ecd1a6b91e3\",\"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\">08:54:26 UTC </span>Loading results from simulation_data.hdf5                          \n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "final_sim = fn_pre(**best_params)\n",
        "\n",
        "# Define a field monitor to help visualize the field distribution\n",
        "field_monitor = td.FieldMonitor(\n",
        "    center=(0, 0, 0), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\"\n",
        ")\n",
        "\n",
        "mode_12 = final_sim.monitors[1]\n",
        "\n",
        "final_sim = final_sim.copy(update={\"monitors\": (field_monitor, mode_12)})\n",
        "\n",
        "final_sim_data = web.run(final_sim, task_name=\"BO_notebook_final_sim\")"
      ],
      "id": "e2f4b4f9"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Plotting the field shows how it splits evenly between each branch."
      ],
      "id": "3db091b5-a6f8-4894-9ec5-0949c48f66c6"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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8+OGH9tg6bWsYhjj22GPj/huSD4uJ//jHP+xx77rrrrTjEhH1Jz/++KNwueQ/\nmDz11LUiGntHRGPviI2b5oiKCnkn0p/+9KdcT5P6KUMI1f6QiIgGpUAggDVr1qC5uRnl5eWoqamx\nm1+k8sknnyAajcLhcGCXXXbp0Xmbm5uxdu1aBAIBVFdXo7a2tkddNEOhEJYvXw6fz4fi4mKMHj0a\nBQUFfT5Grs5VX1+PDRs2IBAIoKKiAqNHj45r/NEdlmVhzZo12LRpE7xeL4YNG4aKiopujbFx40b7\nc9V/xZg8eXJc45CVK1di9erVALrXfXlzvmeWLFmCjRs3AgC22WYblJaWdrn/559/jmAwCACYPn16\nRudIx+/3o66uDpFIBEOHDkVNTU3c/BcvXoympiYAwA477ACv15t2zEyudzqBQCCjTtKJPB4Pdtxx\nx24f15tisRjWrl2LjRs3oqysDLW1tfB4PBkdGwqF8Nlnn9mPp0yZkvHP5uacty/G68ppp52GRx55\nBIZh4Mcff8S4ceOS7rftttvajVr0f78A2Rhm3bp1qKmpQU1NTdwx0WgU33//PYYNG9bp57w7P5ON\njY347rvvAAA1NTXYYostunxPH3/8MYQQcLvdcY1mEs2aNQvz5s1DSUkJVq1alfa/C0RE/clxxx2H\np556CtOmb4MPPvhrXNOuO+54Gpdd+jdUVJRgyZLl3f77HhEXE4mIiIiIBqkvv/wSO+ywA4QQuPLK\nK+M6RneUajGxv/rhhx+w1VZbQQiBiy++GHfccUeup0RElDX//e9/MW3aNADAu+/dhT322C7u9VAo\njMnbnIply9bhsssuwy233JKLaVI/1v0ICBERERERDQjbb789jjrqKADAfffdh9bW1hzPqG/cfvvt\nEEKgsLAQV1xxRa6nQ0SUNUIIXHrppQCAI47Yq9NCIgB4PG5cf/1ZAIC77roTy5cv79M5Uv/HxUQi\nIiIiokHs+uuvh8vlQmNjI+69995cT6fXrVmzBo888ggA4KKLLup0ezYRUX82Z84cvP/++3A6Hbjx\nprNT7nfMsbMwdepWCIUiuPrqq/twhjQQcDGRiIiIiGgQ23rrrXHhhRcCAG666SY0NzfneEa969pr\nr0UwGERtbS1/gSaiASUSidhp63POmY0ttxyVcl/TNPHHW84DADz++OP49NNP+2SONDCwZiIRERER\n0SDn9/vx5ZdfAgC22mqrTsX4B1LNxE8//RSRSCSjZi5ERP3Jvffei5///OcoKSnEd9//E0OHpm+s\ncsThV+Pll+dj1qxZeOutt+IatRClwsVEIiIiIiLq0kBaTCQiGoh8Ph8mTBiDDRsa8fs/nIlf//qk\njI5btGg5pmx/BizLwssvv4xDDjmkl2dKAwFvcyYiIiIioi498cQTWLBgARYsWIDCwsJcT4eIiBLc\neuut2LChEbW11bj44p9lfNykSWNw5pkHAwAuv/xyRKPR3poiDSBMJhIRERERERER9VNr1qzBxInj\n4fcH8eCDl+O00w/q1vHr1jVgqy1PRFtbEA888ADOOuusXpopDRRMJhIRERERERER9VO/+93v4PcH\nsd1243DyKft3+/iamkr86tLjAADXXHMN2trasj1FGmC4mEhERERERERE1A998803eOihhwAAN918\nLhwOR4/GueSSY1BTU4m1a9fi9ttvz+YUaQDiYiIRERERERERUT90+eWXw7Is7LPPzjjggF16PE5x\ncQGuvfZ0AMAtt9yEdevWZWuKNABxMZGIiIiIiIiIqJ95++238eqrr8IwDPzxlvNgGMZmjXfa6Qdh\n0qQxaGsL4rrrrsvSLGkg4mIiEREREREREVE/YlkWLr30UgDASSfthx12mLDZYzqdDtx087kAgAce\nuB+LFy/e7DFpYGI3Z9os0WgUK1euBACUlpbCNLk+TURERERERNlnWRZaWloAAKNGjYLT6czxjHLn\n8ccfx0knnQSPx4XF3/0To0YNzcq4Qgjsu+8leHfe5zjssMPw4osvZmVcGli4mEibpa6uDuPGjcv1\nNIiIiIiIiGgQWbp0KcaOHZvraeREMBjEVltthRUrVuCyy4/HTTedk9XxP/30O0zb9TwAwLvvvosZ\nM2ZkdXzq/xgjIyIiIiIiIiLqJ/7yl79gxYoVqKoqxZVXnpD18XfeeSscf/w+AIBLL70UlmVl/RzU\nvzGZSJulvr4e1dXV6pEJYPMKvhIRERERERElJwDIha1Nmzahqqoqt9PJgfr6eowfPwbNzW24484L\ncOGFP+2V8yxbtg7bTDoF4XAETz75JI499theOQ/1T1xMpM3S2NiIyspK9cgBLiYSERERERFR7xAA\nYgCAhoYGVFRU5HY6OXDJJZfgjjvuwPjxI/DV1w/D7Xb12rkuu+xvuONPT2Ps2LFYtGgRPB5Pr52L\n+hfe5kxERERERERElOeWLl2Kv/71LwCAG244u1cXEgHg178+CRUVJairq8M999zTq+ei/oWLiURE\nREREREREee7Xv/41IpEopk3fBj/92cxeP19FRQl+ffVJAIA//OF3aGxs7PVzUv/AxUQiIiIiIiIi\nojz23//+F0899RQA4JZbzoNh9E2Jsf/7vyMwduxwNDb6cOONN/bJOSn/cTGRiIiIiIiIiChPCSFw\n6aWXAgCOOGIv7LHHdn12bo/HjeuvPwsAcNddf8ayZcv67NyUv7iYSERERERERESUp1566SW8//77\ncDhM3HjT2X1+/qOP2RtTp26FcDiC3/zmN31+fso/XEwkIiIiIiIiIspD0WgUV1xxBQDgnHNmY8st\nR/X5HEzTxB9vOQ8A8Pjjj+PTTz/t8zlQfuFiIhERERERERFRHnrwwQexePFilJQU4rfXnJqzecyc\nuQMOPXR3AMBll10GIUTO5kK5x8VEIiIiIiIiIqI84/P58Lvf/RoAcNnlx2Po0Iqczuemm8+Bw2Hi\nnXfewauvvprTuVBucTGRiIiIiIiIiCjP3HbbbdiwoREjRlTj4ot/luvpYNKkMTjzzEMAAJdffjmi\n0WiOZ0S5wsVEIiIiIiIiIqI8smbNGtx22y0AgOuuOx2Fhd4cz0i65nenoajIi2+//RYPP/xwrqdD\nOcLFRCIiIiIiIiKiPPK73/0Ofn8Q2203DqecekCup2OrqanEry49DgBwzTXXoK2tLcczolzgYiIR\nERERERERUZ745ptv8NBDDwEAbrr5XDgcjhzPKN4llxyDmppKrF27Frfffnuup0M5wMVEIiIiIiIi\nIqI88e6778KyLEyduhUOOGCXXE+nk+LiAlz8y6MBAG+99VaOZ0O5wMVEIiIiIiIiIqI8U1lZCsMw\ncj2NpKqrynI9BcohLiYSERERERERERFRRpy5ngARERERERERESVhWbmeQXJC5HoGlENMJhIRERER\nEREREVFGuJhIREREREREREREGeFtzkRERERERERE+UaIPL7NOU/nRX2CyUQiIiIiIiIiIiLKCBcT\niYiIiIiIiIiIKCO8zZmIiIiIiIiIKO/k823O7OY8mDGZSERERERERERERBlhMrGb7rjjDtx99932\n43fffRe1tbWbPW5rayseffRRvPLKK1i1ahU8Hg8mTpyI4447DrNnz+6zMYiIiIiIiIiIiFLhYmI3\nLFq0CFdddRVCoZD9XCQS2exxP//8cxx11FGoq6uLe37hwoV44okncNBBB+Hpp59GcXFxr45BRERE\nRERERHlCIH9vJ87TaVHf4G3OGbIsC2eccQZCoRCGDBmStXFXr16Ngw46CHV1dSgrK8Odd96Jzz77\nDB988AHOPfdcAMDcuXNx7LHH9uoYRERERERERERE6TCZmKE777wTH330EXbYYQfss88+uP3227My\n7lVXXYV169bB6XTijTfewC677GK/tscee6C2thbXXHMNXn31VTzzzDM4+uije2UMIiIiIiIiIiKi\ndJhMzMCPP/6I3/zmNzBNE/fffz+czuyswa5btw6PP/44AOC0006LWwTUrrrqKrsm42233dYrYxAR\nERERERFRHrKs/Pwj8rTLNPUJLiamIYTAmWeeiUAggAsvvDDpYl1Pvfjii7BUm/eTTjop6T5OpxPH\nHXccAFn/cNWqVVkfg4iIiIiIiIiIKBNcTEzj7rvvxnvvvYeRI0fi+uuvz+rYCxYsACAX+6ZNm5Zy\nvz333BOAXNj8+OOPsz4GERERERERERFRJlgzsQvLli3DVVddBUAuKma7E/KiRYsAACNHjoTX6025\n3/jx4zsdk80xiIiIiIiIiCjPCCFvKc5H+dplmvoEFxO7cPbZZ6O1tRU//elPcdhhh2V9/PXr1wMA\nhg8f3uV+I0aM6HRMNsdIp7GxMeVrTU1N3RqLiIiIiIiIiIj6Ly4mpnD//ffjzTffRGlpKe66665e\nOYfP5wMAFBQUdLlfx9f1MdkcI53Kyspu7U9ERERERERERAMTFxOTWLVqFS677DIAwM033xyX6ssm\n3TjFNLsuXelwOOyvY7FY1scgIiIiIiIionyTx7c5W7zNeTDjYmIS55xzDlpaWrDbbrvh3HPP7bXz\nFBUVoampCcFgsMv9AoGA/XVi3cZsjJFOQ0NDyteampowbty4bo1HRERERERERET9ExcTE7z44ouY\nO3cuXC4X7r///rSJv81RWVmJ1atXo76+vsv9Or5eUVGR9THS6e7+REREREREREQ0MHExMcHKlSsB\nyNuGjzjiiKT7dFyY23vvveF0OlFZWYn//ve/3TrXlltuia+++grLly+HEAKGYSTdb9myZfbXW221\nVdbHICIiIiIiIqI8I5C/tzmzm/Og1nuxu34uFAphyZIlSf907GC8fPlyLFmyBEuXLu32OXbccUcA\ngN/vx9dff51yv48//tj+eocddsj6GERERERERERERJngYmKCk08+GT/88EOXfzrWUZw3bx5++OEH\nLFy4sNvnmj17tv31Cy+8kHI//dqYMWMwZcqUrI9BRERERERERPlGNWDJxz8iTxOT1Ce4mJigrKwM\nEyZM6PJPeXm5vf+YMWMwYcIEjB07ttvn2n777TF9+nQAwF//+tekdQ9fffVVe6Hy7LPP7pUxiIiI\niIiIiIiIMsHFxF50wAEHYMKECdhpp51S7nPbbbfB4XBgw4YNOOigg7B48WIAgBACL774Ik466SQA\nwBZbbIGLL76418YgIiIiIiIiIiJKhw1YepGup1hWVpZynz322AP33nsvzjvvPCxcuBCTJk1CTU0N\n/H4/WlpaAABDhw7Fyy+/jKKiol4bg4iIiIiIiIjyiACMfL2dmA1YBjUmE/PAWWedhfnz5+PQQw+F\n2+3GunXr0NLSgqqqKvz85z/H119/jcmTJ/f6GERERERERERERF1hMrEHLrvsMpx11lkAgJEjR6bc\n7z//+Q/C4TAcDkfaMXfddVe89NJLiMViWL9+PTweD6qqqro1r2yMQURERERERERElAoXE3ugqqoq\no0W60aNHd3tsh8OBESNG9GRaWR2DiIiIiIiIiHJJdXPOR710m/Nbb72Fxx57rFvHXH311Zg4cWLK\n17///nvMmTMHy5cvh2maGDduHI488shurdlkY4yBhIuJRERERERERESUc4sWLcIjjzzSrWMuuuii\npM+3tbXhwgsvxD/+8Y9Or11yySW45JJLcOONN8LlcqUcOxtjDERcTCQiIiIiIiIiopzbd999ky7c\nddTS0mIvII4fPx477LBDp31isRiOPvpozJ07FwBw0EEH4cADD0QkEsG///1vzJ8/H7fddhsaGhrw\n97//Pel5sjHGQMXFRCIiIiIiIiKifCMAWHnaNbmXbnPeeuutsfXWW3e5z4MPPmh/fdppp8EwjE77\n3HffffYi4PXXX4+rr77afu2SSy7B//3f/+Hee+/FQw89hCOOOAKzZ8/ulTEGKnZzJiIiIiIiIiKi\nfuHhhx8GAJimiVNPPbXT65Zl4YYbbgAA7Ljjjvj1r38d97phGLjzzjtRW1sLAPj973/fK2MMZFxM\nJCIiIiIiIiKivPfjjz/iww8/BCBviR41alSnfT788EOsWbMGAHD22WcnTS56PB6ccsopAIBPPvkE\ny5Yty/oYAxkXE4mIiIiIiIiI8o7q5pyPf3rpNud0OjZnOf3005PuM2/ePPvrWbNmpRyr42vvvPNO\n1scYyLiYSEREREREREREeU0IgUcffRQAUF5ejiOOOCLpft988w0AwOl0YuLEiSnHmzRpkv31t99+\nm/UxBjI2YCEiIiIiIiIiom6LRqNobGzscp+KioqsnOvtt9/GihUrAADHH388vF5v0v1WrlwJABg+\nfDgcDkfK8UaMGAHTNGFZln1MNscYyLiYSERERERERESUbwTkLcX5SHWZnj9/PiorK7vcVWTplmjd\neAUAzjjjjJT7+Xw+AEBRUVGX45mmiYKCArS1tdnHZHOMgYy3ORMRERERERERUd7y+Xz497//DQDY\ndtttMXXq1JT7hkIhAIDL5Uo7rtvtjjsmm2MMZEwmEhERERERERFRt+2+++54+eWXe/08zzzzDPx+\nP4DUjVe0wsJCAJkt7gWDwbhjsjnGQMbFRCIiIiIiIiKivCMAkae3OUPeuux0OrNWE7Er+hZnp9OJ\nk046qct9y8rKAADNzc1d7hcOh+2FwNLS0qyPMZDxNmciIiIiIiIiIspLS5cuxQcffAAAOOSQQzB0\n6NAu9x83bhwAYMOGDV0mC1euXGnXcxw/fnzWxxjIuJhIRERERERERER56eGHH7YX7LpqvKJtt912\nAGTjl88//zzlfp999lmnY7I5xkDGxUQiIiIiIiIionyjuznn65++uARC4LHHHgMADB06FAcffHDa\nYw466CD769deey3lfvo1l8uFffbZJ+tjDGRcTCQiIiIiIiIiorwzb948LFu2DABw8sknw+lM3/pj\n6623xs477wwAeOCBB9Da2tppnzVr1uCpp54CIG+dTqz7mI0xBjIuJhIRERERERERUd7RjVeA9F2c\nO7rxxhsBAKtXr8bJJ59sd4IGgPr6ehxzzDFobW2Fw+HA73//+14bY6BiN2ciIiIiIiIiorwjAEvk\nehLJ9cG0Wltb8dxzzwEAdt11V0yePDnjY/fff3/85je/wfXXX48XXngBo0ePxowZMxCNRvHOO+/Y\nScO77747Za3DbIwxUHExkYiIiIiIiIiI8sozzzyDtrY2AN1LJWp/+MMfMGHCBFx99dVYvXo1nn/+\nefu1CRMm4I477sChhx7a62MMRFxMJCIiIiIiIiKivDJx4kT84x//AAAcddRRPRrj1FNPxcknn4z/\n/e9/WLZsGUzTxPjx4zFlypQ+HWOg4WIiEREREREREVG+0d2c85Ho/Xntueee2HPPPTd7HNM0MXXq\nVEydOjWnYwwkbMBCREREREREREREGeFiIhEREREREREREWWEtzkTEREREREREeUdkce3Oedpl2nq\nE0wmEhERERERERERUUa4mEhEREREREREREQZ4W3ORERERERERER5xhCAkae3ORsWb3MezJhMJCIi\nIiIiIiIioowwmUhERERERERElHdEHjc6ydd5UV9gMpGIiIiIiIiIiIgywsVEIiIiIiIiIiIiyghv\ncyYiIiIiIiIiyjcCQJ42YMnf26+pLzCZSERERERERERERBnhYiIRERERERERERFlhLc5ExERERER\nERHlHZG/tzlbvM15MGMykYiIiIiIiIiIiDLCxUQiIiIiIiIiIiLKCG9zJiIiIiIiIiLKNwL5ezsx\nuzkPakwmEhERERERERERUUa4mEhEREREREREREQZ4W3ORERERERERER5J4+7OfM250GNyUQiIiIi\nIiIiIiLKCBcTiYiIiIiIiIiIKCO8zZmIiIiIiIiIKN8I8DZnyktMJhIREREREREREVFGuJhIRERE\nREREREREGeFtzkRERERERERE+ShfbyfO13lRn2AykYiIiIiIiIiIiDLCxUQiIiIiIiIiIiLKCG9z\nJiIiIiIiIiLKN0KwmzPlJSYTiYiIiIiIiIiIKCNcTCQiIiIiIiIiIqKM8DZnIiIiIiIiIqJ8ZOXp\n7cS8zXlQYzKRiIiIiIiIiIiIMsLFRCIiIiIiIiIiIsoIb3MmIiIiIiIiIso3+dzNOV9vv6Y+wWQi\nERERERERERERZYSLiURERERERERERJQR3uZMRERERERERJRvBPL3Nmd2cx7UmEwkIiIiIiIiIiKi\njHAxkYiIiIiIiIiIiDLC25yJiIiIiIiIiPKOyN+uybzNeVBjMpGIiIiIiIiIiIgywmQiERERERER\nEVE+EmzAQvmHyUQiIiIiIiIiIiLKCBcTiYiIiIiIiIiIKCO8zTkFy7Lw+eef4+2338b333+PNWvW\noLm5GWVlZZg0aRIOPPBA/OQnP4FhGFk5XzgcxgsvvIBXXnkFq1atgsfjwcSJE3Hcccdht91267Mx\niIiIiIiIiCgPCLABC+UlLiYm0dzcjPHjx6O+vj7p66+88gpuu+02TJ06FY8//ji23HLLzTrfDz/8\ngKOPPhpffPFF3PNz587FXXfdhZNPPhkPPPAAPB5Pr45BRERERERERETUFS4mJhGJRNDS0oK9994b\ne+yxB8aNG4eRI0cCAJYvX44nnngC8+bNwyeffIIZM2bgyy+/xNChQ3t0rk2bNuGAAw5AXV0dvF4v\nLr30UsycORNtbW147LHH8Nxzz+Gxxx5DJBLBv/71r14bg4iIiIiIiIiIKB0uJiZRXl6OjRs3oqys\nLOnrZ599Nv70pz/hV7/6FdavX4/bbrsNt9xyS4/O9dvf/hZ1dXUwTRMvv/wy9tlnH/u1ww8/HL/6\n1a/wpz/9CU8++SROPvlkHHzwwb0yBhERERERERHlEwFY7OZM+YcNWJJwOp0pFxK1iy66CBUVFQCA\n+fPn9+g89fX1ePDBBwEAxx9/fNwioHbjjTfaqcebb765V8YgIiIiIiIiIiLKBBcTe8jhcKCqqgqA\nXHzsiTlz5iAajQIATjvttKT7eDweHHvssQCADz/8EOvXr8/6GERERERERERERJngYmIPLV++HEuX\nLgWAHndK/uCDDwDIhcmuxpgxYwYA2WF6wYIFWR+DiIiIiIiIiPKM7uacl39yfXEol7iY2APz5s3D\nYYcdBsuyMGrUKFx++eU9GmfRokUAgNraWhQVFaXcb+LEiZ2OyeYYREREREREREREmWADljQ++ugj\nXHzxxQAAv9+PVatWobGxER6PB8cddxz+/Oc/27UTu2vt2rUAgBEjRnS5X8fX9THZHCOdxsbGlK81\nNTV1aywiIiIiIiIiIuq/uJiYRlNTEz7++ONOz0+ePBn777+/XTexJ3w+HwCgsLCwy/06vq6PyeYY\n6VRWVnZrfyIiIiIiIiLaTCKfuznn6byoT3AxMY3ddtvNrjEYDAaxdOlSPP3003j99ddxxhln4Omn\nn8bTTz+NkpKSbo+tG6c4HI4u9+vY4EUfk80xiIiIiIiIiIiIMsGaiWmUlZVh+vTpmD59Ovbee2+c\nccYZeO2113DfffcBAF577TX7Nuju0jUOg8Fgl/sFAoFOx2RzjHQaGhpS/tFNaIiIiIiIiIiIaODj\nYmIPnXPOOTjwwAMBAI8++miPagfqWosNDQ1d7texZmF5eXnWx0inoqIi5Z/ujkVEREREREREGcp5\n1+YUf0SuLwzlEhcTN8MBBxwAQN42/PXXX3f7eN1hefny5V3u1/H1LbfcMutjEBERERERERERZYKL\niZvB6/XaXwvR/WX5KVOmAABaW1uxePHilPstXLiw0zHZHIOIiIiIiIiIiCgTXEzcDF9++aX99Zgx\nY7p9/KGHHmp//dJLL6XcT782YsQI7LTTTlkfg4iIiIiIiIjyjZBdk/P1Dw1aXEzsoSVLluDRRx8F\nIJN+o0eP7vYYU6dOxfbbbw8AuOuuu+Dz+Trt89577+H9998HAJx22mkwDCPrYxARERERUe4Y/F/a\n/5GU68+BnyERAVxMTOq2227DPffcg5UrV3Z6LRgM4vHHH8dee+2FtrY2GIaBP/7xj0nHOe644zB9\n+nTst99+Kc916623wjAMrFq1CkceeSTWrl1rv/bBBx/guOOOAwAMGzYMl19+ea+NQURERERERERE\nlI4z1xPIRx999BGee+45nH/++SgsLMSwYcNQWVmJtrY2LFmyBJFIBICsmfi3v/3NbsSS6PPPP8d3\n332HsrKylOfaf//98cc//hGXX3453nrrLYwZMwZbbbUV2traUFdXBwAoKSnBnDlzUo6TjTGIiIiI\niIiIKI8IyM7J+ShPp0V9g8nEJK644gpceuml2G677RAIBFBXV4dPP/0UixcvRiQSwahRo3DhhRfi\n66+/xmmnnbbZ57vsssvw2muvYfr06YhEIvj6669RV1cHr9eLo48+Gl9++SV23XXXXh+DiIiIiIiI\niIioK0wmJrHLLrtgl112wa233opIJIJ169Zh48aN8Hg8GDZsGKqrqzMa56mnnkIgEIDTmf4yH3DA\nATjggAPQ0NCAVatWwePxYPTo0SgoKMh43tkYg4iIiIiIiIiIKBUuJqbhcrkwatQojBo1qtvHTpky\npdvHVFZWorKystvHZXsMIiIiIiJq1ysNJLLeGLE/3HjWsw6wGV1/0Q/vu8xZc8ze/F5J/hnrz1Cw\nC3Dm8vo25zydF/WJ/vD/NkRERERERERERJQHuJhIREREREREREREGeFtzkREREREREREeUcAVp7e\nFs7bnAc1JhOJiIiIiIiIiIgoI0wmEhERERHRgLfZDVS61Sij68xGxnMx0mU/MsuGGGnH6Xs9b8LR\nxXGpLms+Nfzo8WexeZ9hLr8H7M9aRNVkEp4non6Hi4lERERERERERPkmn7s55+u8qE/k3z9RERER\nERERERERUV5iMpGIiIiIiIiIKB/lawIwT6dFfYOLiURERERElPd6v+Zh8pu2Up43aQ26+Oc616lz\ndPm6kfb45K+nGyf1eJm91pVU5xJd1TZMtn+K+nnJnk83drpafH1Vq29zrneq65rx8Vn+PDvq6Wcb\njQXVtlm9EFbjcVWKqL/hbc5ERERERERERESUESYTiYiIiIiIiIjyjRCAladdr9mNe1BjMpGIiIiI\niIiIiPKetRmLq0Js/i312RhjIOBiIhERERERERER5Z2NGzfi97//PaZOnYqysjK4XC7U1tZiv/32\nw3333YeGhoYuj1+7di0uueQSTJgwAR6PB16vF5MmTcJvf/tbNDU1ZTSHbIwx0PA2ZyIiIiIiyoke\nNVXp9UYqyZua6OYpyZqdtD8Xf6y9Tft8/NgOw5mwn6PL48zE8dX+DsPVaa4d9++4b+I+mmmkeT3h\n+HQsxJI+n9jUwxKxlK+LhDGshGMTxxIi+TkTj8sWM8n3oJHmOqY6dnM/n/ZxM/ucTJE+b2QZmV03\n/VnHRAQA0GptBAA0+esAABGxSe7IpFfX8vX69MG0nnnmGZx77rlobGy0n3M4HFizZg3WrFmDN998\nE4sWLcKdd96Z9Pj3338fRxxxhL3gaBgGhBBYvHgxrr/+ejz88MN4/fXXsc0226ScQzbGGIiYTCQi\nIiIiIiIiorzxr3/9C8cddxwaGxux7bbb4plnnkF9fT2i0Sh8Ph/mzZuHn//856iurk56/IoVK+xF\nwOHDh+PFF19EIBBAa2srHnnkEZSWlmLVqlU4+OCD0dzc3GtjDFRMJhIRERERERERUV5YtmwZzjnn\nHFiWhX322QevvPIKPB6P/XpxcTFmzpyJmTNnphzjt7/9LRoaGuByufD6669ju+22AwB4PB6ccsop\nGD58OPbff38sX74ct956K66//vpeGWOgYjKRiIiIiIiIiCjfCAFYefqnF2+/vvrqq9Ha2oqSkhI8\n9thjcQuJmWhsbMQTTzwBADjhhBPsRcCO9ttvP8yaNQsAcO+99yIWi2V9jIGMyUQiIiIiIsqqHtVC\nBFLUQ8ywBmKK2oftQ+vHqeoPdl330EyoY2iqeoSGYdo1DvVzel+H/dgV99ihfg0zIR+7DK98Xsjn\nXZC/ODvVY72/Q8i5u/VjuzaiqY5TW1NtdQ1G01D76XdodLrUahf7ujrV5dDHOIz4MRxmx6vV/lhL\n/CQTlx0s9UTieoSV4nX9vBAdvxbx+yYc2+lcCbNI3C9R4tzSles0E78l1VXo+Hz7dU5xjDqJaT9G\n8seJn1+a1xOnnvh64py7kngdEyV+Hv6o3C5vDQEAPimUs93g88n9RCDtOWnwqK+vx3PPPQdALuIN\nHz6822O88soriEblN97RRx+dcr+jjz4a77zzDurr6/HBBx/EJR2zMcZAxmQiERERERERERHl3Ftv\nvYVQSC48H3bYYXGvWVZmzX8+/fRT++vp06en3G+33XZLeky2xhjIuJhIRERERERERJSPcn07c8rb\nnHvn7S5cuND+epdddsFHH32En/3sZ6iqqoLD4UBZWRlmzZqFv//973ZyMNHixYsBAKWlpaiqqkp5\nrnHjxnU6JptjDGS8zZmIiIiIiHqk27czp7tPtEPWIdPbmBNvX24/VfztyYn7d75tWd16bOpbkru+\nddlpeOxt4u3KblGgtm619aitHMOtxvAY6rZlNQePI37rdcpr4FZv0ZWwdZv6dflbfbF6q0VOmd4p\nclhqPEvNT6j30L4KYKqv9dV2qrGchqXmIGuAFTrlL+2F7rAc0y0fe7xy63TJ/ZweNbaZYqXBkmcS\nKmAkRMJjtbVipnq98+OY+trSY6kxYonHiITXrfjvofbbpeO/1yzR9fepacS/N0M9NhMf62trCPtr\n09H5NQAw1fVyqM/Kvl051WN9m7kar/0ufX0e/Rhxr3f6EUx4vTtEYkgs4bGlysf56+X3+wdLRwAA\nQj/uDgB417EEABC2eJtzfxaNRtHY2NjlPhUVFRmPt2LFCgBAQUEB/vWvf+GXv/ylnUg0TRMtLS2Y\nN28e5s2bh3/84x+YM2cOKisr48aor68HAAwZMqTLc5WWlsLtdiMcDtvHZHOMgYzJRCIiIiIiIiIi\n6rb58+ejsrKyyz/d0dzcDACIRCK4+OKLUV5ejn/84x9obm5GNBrFt99+i2OOOQYA8OGHH+KEE07o\nNEZraysAuSCZjt7Hp2p4ZnOMgYzJRCIiIiIiykj2kogJzVE6jps2gajFN1JJl0A0zfikoSPF8x0T\nh/FbmTp06ZQh5C+PHuFFgX5OjVGgxvY645OGhSppWKC2XpUe86quJkVOmSorVdsyV1Q9LyNeHlMl\nDFX6zOuQrxe7IwCA6rI2edwIWW/MPUo1iakslNtCt7pERnsHDkvEb6MqThaV5xIxK+6x3k/ouwvD\nahzVHMZOq+njw2p/Pb4aXkRF/FYnE+3XrYTn5XmsmJE2xdiedjSSPq8TjVqqprSJiUXDSL6jnRo0\nkycWDQMwzPgEon5s77OZScOEb38k9BNqn1NiBxgk369bEhOKOpyqPlu3X3//qtRrTxs0DUYCQIZ1\nAvtcp2hqdugUYjQahcPhwKuvvopp06bZr0+aNAlPPfUULMvCs88+i9dffx1vvPEG9ttvv85TzKDj\ntN7HSPH/V9kYYyDiYiIRERERERFtHsNUq2b6j1DPQT4WAGAAeoEupp6PxT9v6IW+mCGPtfR+8rER\nM2Ak3BoNdfuy/pXfXgRMsZhoiF5aTExx+7NhoH1R0EhcTFT72ouAyRcTExcHOy0mJiw2tt9znTDZ\nPlxMREzf/q0W01tkQq0mLA8YFxgDAAhHijM+VSwWw/LlS3o+V8q63XffHS+//HLWxisubv9+OOSQ\nQ+IWEju67rrr8OyzzwIAnnvuubjFRD1GMBhMe75AINDpvNkaYyDjYiIRERERESWVcRKxG7UQk46b\nJEaVWAtxcxOIDtMTt59TPXYZMrXnhHzsgXysk4cFqv6hx5KpPq8ht4XqPAUuE0UqgagTh0X2Vi6m\nVLrltlzVFSxz6TqEMbWfTBZWFspfSIeNkrfKFUySaUhzeKl8ywU6WahWjpwudbGK5NZdrV5X1y4x\n0RSQiUWEox0SiFHAMCCClvwcw/J4EZLbmM8CTBPRFgMwTYT9LhimgWDIDRgGghEPAAOhqJxTyJLb\nqFrga9+quoXqs29fb0pY8FOvW/ZjxL1uddgHSV6Lfz5F0ijps+2sFDuYmf44JD7uWKOy02uJx8af\n3Ey5X9dzSxwn1fnSMdH5uqaT+Fm2ReW7+DEsty3lywAAJS75/R2LlWc8dqqGG5Q7TqezWzUR0xk6\ndKj99V577ZVyv2222QaVlZVoaGjA119/HfearnO4fv36Ls/V2NiISCQSd0w2xxjIuJhIREREREQ0\nEBkOubBoOOQf4ZELty6hEn+mnR5DSC3ShGJxjw1137Ghblk11C3FRkwtaEYGz219RH1PpF7dzrVe\nuvt6m222sb9Ot0hZUVGBhoYGtLS0xD2/9dZb49VXX0Vrays2bNgQt0DZ0dKlS+OOyfYYAxkXE4mI\niIiICEA2koiZJhDj9++YQtRfG/Zrqboud51A1Ftd89BtyIShTiJ6hNx61bZAyJRUodpfJw+LXA61\njU8dFqvH5W6BKrdccKtQicNSl0ypVBXI2+Nqa5rk81PUnLdQDQkK5bmgElrwVsltgXqsE4ZBlShs\nU11v24LyWjYHANMJEYgBpgOxZj/gcCLcaAKGiUCLG0AM8g68GAIRef6I5bSTgpb6jGKq7qNOlaX6\niFMl3nQKTSQkChPXQUz1ij6vU6X2dIjQvuVY36mMhOfROVmYaq1FpJprltdm0qX9kr2cLkmYaszO\nycYUCcSup2R/Dr3BMnT6VJ5Ddx93mIk/21yIps523XVX++t03ZH164mLjh3HWLBgAQ4//PCkx8+f\nP9/+epdddsn6GAMZuzkTERERERHlI8METBfgLADcJUBBJVA0BCgbAZQNB8qGASVVQFE5UFACeAoB\npzvJoi0RUf8wbdo01NbWAgDeeeedlPt9/vnnaGpqAgDstNNOca8ddNBBcLtlWYgnn3wy5RhPPfUU\nAGDYsGHYbbfdsj7GQMZkIhERERHRINV3ScTOCcSOj40Or+skYrYSiF4hC+IXqARioSVfLzblL4lF\nLjleiYpP6cRhqdpWeWS6qlqlD8tVh+VKj0wLjqpsxtAdwnIOE1Q6pkglC4vU45KR6rGcA3ytcrup\nSW3bAIcLVnMz4HQh2mAALhcCDXJu/jb53vxhlSyM6bqE+tc5fY1U/UfV9Tkx7abTg6Yh7NCjKXST\nDB1JjDukvamIbjKS+Hwalu6gnPiCSL5f4ly7OizxmM77di/5liqx2NMAXbIUZ6rrluoUqZKgaa9/\nhnPO5rJzqs/YCfmzW+SU36duVZ+UMiCQv7c5ZzviqxiGgYsuugiXX345Xn/9dbzzzjuYNWtW3D6W\nZeHqq6+2H5900klxr5eWluK0007D/fffj2eeeQa//OUv45KGAPDiiy/iww8/BABccMEFME0z62MM\nZIPnnRIREREREeWUIZOGrkLAUwqUDJUpw/LhQEk1UFwOeIsAt7vnK1hERP3cL37xC0yZMgVCCBx2\n2GH485//jJUrV6KlpQULFizAoYceildffRUAcM4552DnnXfuNMZ1112HYcOGIRaL4aCDDsKjjz6K\nDRs2YM2aNbjrrrtw/PHHAwC23HJL/PKXv0w6j2yMMVAxmUhERERENEjkSxIxsSNze/rQ2anbsn7N\naarUXUIC0WOUyMeq63KRkJ2NExOIxW45h1K9VY2Qq1RIapjHUo9lyrBaJQ/HDWuUz++iEnwjVGdl\nnT6sGgsUq27KDc1yu6YeMExYy32Ay41okw9weRBoagMAtLXKk7aG3ADCCMfkZCwh5+p2yLm4HDF1\nXeSwujuxTqU5VQ8UzWnI4xymfN2hHiem2CxhZJza08k409RjqeftbffSSSk7LKcZpstwVpqEYqZS\nJR0zTWF2ksFxqTpEp63DmOGcuvv5ZJP+rL1BleoNqo7o6meT2SZKxePxYO7cuTjkkEPw2Wef4eKL\nL8bFF1/cab9TTz0Vf/nLX5KOUVNTg1deeQWHHXYY1qxZg1NPPbXTPltuuSXmzp2LoqKiXhtjoOJi\nIhERERER0eYyXYC7CHC4gHI3YDoAyy9fC0ZzOzci6r8G2W3O2vDhw/Hf//4XDz30EJ566il89913\n8Pl8qK6uxvTp03HWWWd1uv050c4774yvv/4af/nLXzBnzhwsW7YMpmli/Pjx+OlPf4r/+7//Q2Fh\nYa+PMRBxMZGIiIiIaIBLm0jsbhIxTVdmQ9U3NDo9n7weol330HTDYeiv5dbuvmzI2oe6BmJiArHE\nIVNPJaoGYqlbnrPCI+c8RNU+rPFG1WOZQBxT1gIAGL2LTA06t1DJwwqVMqkaH/+e126SqcPVfsDt\nQeyHJvgbw4AB+HweAGG0huXco7FS9X7loW6VNNRpMZ3600lEzaUe28lClQr0qN/e9NpCYkpQ7+fQ\nW6eIO59OynX8uFN1bzYS9rU/cn0uR/yY7cehRzZrXSJLycQe60H6L23yMNPQXk/fem+EAjsVTZS8\nLfJnrcqvOqcb8mfVYDKR0nA6nTjnnHNwzjnn9HiMiooKXHPNNbjmmmtyOsZAw8VEIiIiIiKirhgm\n4PQCDjdQ6gQcDiComqj4wz1f0CEiIuqHuJhIRERERDTA9F4SMdOuzImdmOO3zoROzC5TdWA2Czsl\nEAv1ViUQS9UxpW45VplHJRDdcq7DvJbaygRijTcIANi6dhMAoHKqqjs4RCUPa4bIbaHquLxiA2CY\niP7QAngKEPnCBzid8DXI8XwBD4AoApFi+zrpZKGuredQES3TYaj3aan3rV4342shJtYfdKgEo1Mn\nC3V35oT97MeqdqLp1OdVz6vf9lRQtP1jN2XH1KQ67BN3UPy3QPvxjvjCjUbicX2ht2637I330N0x\nexreS1WMsS+pCK2zIQIAqNok65B6TVlv1DAcyY8jmxCAyNPbnHv5LmfKc1xMJCIiIiKiQc6QqUOH\nByg1AKcL8PvkS74U924SERENUlxMJCIiIiIaALpMI/ZSElEnDVPVQjRVJM7lkEkkXQ/RZSbUQYRO\nIZag0JKvlRjymDKXHKPMI89dpWogDvXKWMxwlUCsLQgAALYZu0EeP0UeZw5XNRCHbiG3zbI2YuzH\nBsBTgNAPrTC8BWhpCAEIocUv5+iPVsZdG49TpQmhOym3pxBdKhGouynrGnoOddnak4Y6OahqGqox\n7GShemzqRGJislAnDu1UoJqcSkAa6oSGS53YqR874vaTqbX47wnD7PhaxxcQ/7z+Xki1v2kmfz5x\nvIHEzGLtv95OdPZmYjFFgs4olAvzlT/IlHCBQ/6Mp0zHElHe42IiERERERENDg4XUFAm6x8OkbdN\nG62xHE+KiCgVkb/3E+frvKhPcDGRiIiIiKgfS5pI7OMkosOU3VlNUyURVQ1EXRPRY8rkoU4iFgiZ\nFtQdmUsgt2UON8pVArHSTiDKmYxQCcSRhTLdtO0W6wEApVPUXIaXyB2rRsvt+mYABsKL2oCCQrR9\nHoNhCjT5CgBE0RIsBwBEhXxvblXHUNc19Dh0XUP5OFWHZadDwKn21d2THTrFqH7bsmsZuhG/deo2\nzypZqBKFhlNHGvXzyZKFgGHG72fXL9TH6+NceiJm+9aRkKbrlEhMKOho72fGP28nFBPaPacaN5Vs\npvuyLZcJunyofdhTKqlouOV/F4pLVgEAPPr7mt2cifotLiYSEREREdEAYgAuL1DiAlxeGE3yFkuj\nhQsXRERE2cDFRCIiIiKifqR7nZp7lkRsTyDGd2e2E4gJXZmdDt2NWW51AtFjyLRgoZDb0pjclqhu\nruUqsVShOjIPLQBqC2SKb2SBTCBOrpFdmIdOU+m/ETqBOAoAIFY1AIYB/xchGIWF8H3qBgwLzW2y\nLltbtBoAYAn53t06cagSiF7E1NzVNqH+oa5r6HLKZKTTlVD30CXgcKsElqptqC4LDJU8NNzqOnrU\ndfXoxKBKfuoahzpB6Ix/3U4RmglbR2L9wsRx1FYnFtVWOMzOtQ+1lLUOExZjjcREYor9EtdwE1/P\nItGLY2+2/pww7CmVTNSfirt0BYD2ZGJvfi8MGAIpa1HmXJ5Oi/oGFxOJiIiIiKgfUgnE0iGA2wuz\n3i+f9uV2VkRERAMdFxOJiIiIiPJY95KIQMcoWE+TiGZCAlHXPnSq552mTiLGd2UugKyFWCzKAACl\nlqyFWOqUx1UWyPGqvXJew+UwGFUQBgBMKG3FVrs2yHOMkwlEY6hMFoqNcpUw8GUARlExGj/1wjAM\nNAXkudoixeo9y7hMew1EnTQU8Y9VstCZkEh0uXT9Q/W6WyUQPSplZdc9bK93aLhVwlAnEN3qeqrn\noR7D64p/7E5IELp1tFHXPkxIJurPTX3mIrHDsq6VqI93yHFFYkLRNDrUPuxmOixlOc40CcVO+w/C\npN4gJUIyZewslJ+5kx999+RtMjFP50V9gouJRERERESU3xwuoKgc8BTBGCITiEYzVySIiIhygYuJ\nRERERER5KGUisYskon1cLyURdVdmt0oiFqECAFBsqZqIhkwqVrpUEtErz6c7Mo8qlCm/MYUBAMCU\nMbIjc/lOah7jqoHIEABAaGE9UFSMpi/cMNwuNLTKsVtVAlHTCUSPGVXvQT2vaiMmJhDdKnnoUrUP\nnTqJ6FEJRDVXh0pPQm1NT0La0KPrHToA9Zpdq9CVkDx0qcShxx33WOgkol3jUD1WSURht4NO7LSc\nohNzYk1Fu7aiA0Q5Y39/6yRt7qZCRNnBxUQiIiIiIsofLq9MII6Qi4dGqyvHEyIiyhEh8vc253yd\nF/UJLiYSEREREeWBntZG7HScYdrdl7ubRHSpWJ5T10I0dVdmlUQU5QCAUkvWRixTicUKVQtwiFeO\nP0zVQhxTKNOCE0tbAQCTd90ozzNR1jk0qoYBAGLf1wMFxWj8yINNLXJsn0ogxlQXZpeqdai3DrX1\n6NqIuuuyQ54z4wRigeq4XKiShqquo+HVnZfVtdSpQndCfUOXoz156ExIJqrHwqP3lddZpNhP1zhk\nPUEaUGKW2qrFJ65BEfV7XEwkIiIiIqLccBUAniJgiFzZMxxcRCMiIsp3XEwkIiIiIsqhzaqNCCSp\nj+hqTyDaiUSdQHSrbddJRK+huzKXAwBKY7ImYrlDJRELZNJuqEr1jSqUUaMxhbIr87bV9fL5PWUX\nV8eEKjnFcBVgOhD8JgIUFWPT5x4AUTSFdEdmp92Q2GHoLsxy69bdlnWNRIdOHiZs3d1NILrU8yp5\nWKC2OonoSV7v0E4dupztNQn1Vr+mHycmF/tjDUN9S6OwEp63Ou/b3yR2ou4Lm3vdepruS/z8+oDR\nJpsmWSE56SiTiZkTyN/bifN0WtQ3uJhIRERERES9z+kB3OWA2wuUNud6NkRERNRDXEwkIiIiIupD\n2U8iticSAZlGTJVE1N2ZXQ5VCzFFErEsJpOCFSrWV1UkxxtWIM81WnVlHl8suzLvMGYdAKB8uprD\nSNnl2VoWBQqK0LzQA7hcWF8vk42tqmNzYj1Er8PqUBtRdV9WiUSPS9ZCbE8gysdOb4oEoqrfaCcQ\nC1UC0asTiCotWKAOLPDEPRZ2jUS1VZ2WRWK60Olo/0z0R9jXtQ9j8pp0Srt1lRzSCTV9TGLyUD02\n9GP7+YTjtXRJO5EHMaZOP2N5oLvXJdNk4eam2TbneP19r8dobAIARHzyeTuZmIOUJBFlBxcTiYiI\niIgouwwH4C0GhhTKhTa1AEhERN0j8vY25zydF/UJLiYSEREREfWize7SnEESUT522o8ddgKxQG0z\nSyJWOmUqb0hhfBJxiyK5GDih2AcA2GnbtQCAgp3lOEblUABA5Ot6oLgMG98vAiBQ3ypfb4vqucl3\noushuhM6MXscMbsbs0clD3UNRHeBTiDKfR2FaWogenXyMKHmoU4gehKSiN7454WdSNSdl9U4uaAX\nE6LymiCmthG5NSz9fELaUCRPG8p9Y+pYEb+vTjnqx1G9ECzix06cm5ZugSEXNRazXRMx8We2J4sq\nmV6HdGNneu5cLEjp77sG2c29rUX+TAXV96kAk4lE/RUXE4mIiIiIaPO4vICnGMYQT65nQkRERL2M\ni4lERERERL0gW7URdeIQkAm81B2a29OITlMmEu0koimThyWiEkDqJOLwQpVELJRptK1Uo5Qddl4v\nx9tF1jqEqwaAgeBnrTBKy7D+80IAFlpCslaiTiK6TJmGcqs6iA619aSoh+h2R+0kokvVZVShSphq\nbkaRfJ+Zd2H2xD0W+rFKHrY/1tteSCB2qkeYorahThzq1KBOCepEYjQinw9F4p9P3M9KkTKMdah/\nGE1RM9E+Rs2pUzIxxXuz30uKtFmmybh0u/Wk7GF3a1cam5lkzKQWYLrrke46pDg+81ti+y6paNXL\n2qpNbTKpHFTfe4K3yabHbs6Up7iYSEREREREmTMMwFMCeIthVDTmejZERETUx7iYSERERESUBZnX\nRsy0S3OKmohmfCLRpVKITlUf0W0Wo9CUCcEiVROxXCcRHYUAgOqi+CTiOFUTcesylUScLpOI7qk1\nckqxoYBhIvBZCEZxCVavLgAQQ2u4GgAQtkw1F5k4KnCo5KFDdWZWjz36ebdOIqoUolfVTiwUcBTL\n62GqORrFKolYqJKDRbrWoU4g6uRh/GPhUr/qJNQ+FKors51AzGYtxEjyxKCdMNTJQJ1AtHSiUD5v\nJCYNI7o2oho3HEl4Xm/1OLH44+20odwKnUKMCnsuwp6T2ieWUENRjWGHyKyEbaJUabN0Yb1sJBIz\nDRT2tKtz4vjdKfmXtpZkquNSDdez65yLMGC4SV64+qD82Qyo73/Bbs5E/RYXE4mIiIiIKDXDARSW\nAu4iGKVMIhIR9R2Rv7c55+u8qE9wMZGIiIiIaDN0tzZiT7s06ySiTiDqRKLHIeuQ6Q7NRahAeawc\nQHsScUiRHLNGdWceVyyTa5NUTcQdp6kk4q4qiRgZApgOtH0SgVHswapVhQAEWiOyZmJMGGoOKWog\n6iSiUyUQdU1Er+rQrDszF6kroBKSZrHLroFoFOskoqzriEK1LZLvSSQkEeFKSB46HGqrfuVxmPHP\nby6d/guHYYTD9tcA7GSiEU5IKurXdaJQJwV1stDeP6EGYkglufR+IbkVYbXVNegi8UlE6PKHduoQ\n9lYkhCNFTH6mwtJb9bwdVIz/fk6ZcBOZxQMTx0vHMLpYuEjxWqoAYpdj9QL5XjO9fsknnep6pRon\n5f45CAO2qUTihpD82fSLNjkX/Q1KRP0OFxOJiIiIiKidqZKInkIYxU25ng0RERHlGS4mEhERERF1\nQ293aTYTujM7TJnqcTlkMs9td2iWScRiyLqFZZask1hhFmKoSvfpmohjdU3EUh8AYOepawEAnt2G\nySmEq2QScWEMRpEXK9bIyKBfJREtlXJyqiSi24yviehxqiSiSiB6PCqBaNdCtNRWjmMUqfdcqDoy\nqw7NKPa2JxDtJKJMYIoC9dirHifUQrQTiN3t3JtKijqHnVKIoRCMkPo6GFLP6X1S1DpMlTSMWHHP\ni5CIe95Swws1XCws36sVNdSUdapQXl9LpQzths2qtqVlmfa+MZUk1J9xTCcTE5s096iNcmpWwnhm\nhq1hjQ77ZZo8TPctke2kYlepy07NrzNMHKa6/onXMdVltDJMgvYkuJgqi6rHaovIn83GiNwzaAQ3\n42yDTF53c87TeVGf4GIiEREREVEXMm+soqW7nbnr25jtxir6dmZH/OJhEeSiYZmoBABUQL5e7ZWL\njrVFJrYokr/kbV3SCgCYOmUNAKBwxlA5lWgVAKD19Q2Aw4EVLWPhLC2CP+YE6tsXmJyGWjzUty2b\n+vZltXXHLx66C+XzjgJ5fkexun25SC8aqm1iM5WOC4de+bVePBRq8VA/bzdO6alUzVB0s5RowsJf\n4i3LehExoBZEQpEOX6tFQn84/rHaWkF1zrC8PpbeqkVCvTgYi6hFwJi8flG1GBONOdSU5PMRtWgY\n1QuD0IuF6nj1ltsfty8gRvUCo15EhF5EjD82lXQ3M/d0ga7zT1PncRL3Sbd42Hn/TOeS/D1kurCa\nbK0l8amMFxcTj0u8bbrTuVMtPnZPsvfQ3f41fvX92qJ+hIKGus2Zi1FE/RYXE4mIiIiIBiPTAaOy\nCkZxMZxGUa5nQ0RERP0EFxOJiIiIiFJImkrMcmMVp7p9Wd/O7FZdSXQSscCUScRSSyYRK1EOABji\nlvsPL5TpNJ1GnFQawC5brQYAlOxXraYiE4n+eRsA08TyTVvAWVECf9QFNLQ3VGlPIqqkoSMWt/V6\nZLTInZBEdBar0+gkYrFbbXXyUG0L1K3JOomom6cUtKcRhUffzpylJGIk/lZjO3kYjr8V2YgkNEdJ\nvFVZP/arGGFQPi8CEYiA/Nryq9uRg3ord42F5PWNhhOShjphGJGfoU4aRnQi0dJbdbzQiUT5OCLi\nX9efY+eUmpqHnUxsP8ZKOCYxuaa/m9OlAfXr6VOBydNo9nn0j1HCu+iYhGzfJ2GMTudOmGOKRF1m\nLWPSS5X66xjAS0w1dkomdnFssnMkjpdu//b9uo4XZpIZzDSg6FfJWb8O+xqRDI8kAPl7O3GeTov6\nBhcTiYiIiIgGA8OAUVoGo7QMzlhJrmdDRERE/RQXE4mIiIiIlC7rI9qxpq4bq2RaG9Fp10SUCTyP\nQyYRvUYZAKAYsq5hZUwmE4c4ZWJxWKEcZwuVBty6RCbldh29Tu43uxgoGg4ACH2wBjAM1K0YDVdl\nCdpiHqC+PeGmk4heR3xtRK9T10LUSUSVUExMIpaqRioqiWiU6NqHCbUQCxISiDqRqJqoCJdKLHrc\ngP66p41UEpOIIZUkDATiH9vPJzRNCcYnEXXqUARV/cOAqn+oUoixABALqKRgSCUHw241FflZhaJy\nqxOHYSs+eRhSz0fs5KHcxnSiUG2jKk0WtZOJRtzzdlIozaWzBOyaifqQxJCRHsKRMJb+WEz18+BQ\nSUOHPY587FT7OU39WG5d6rFbfe/px7q5j66RaNrP6+Pl6w5TwNTnVM9BP1Y/gjr92Ck9meL5xPec\nTqpAVmLar+Nj/XX7Nv5YO6Goa1baidFUaVMj7rhU4+rj0zVqybRBSyb056PHbFPf/4GY/Pkv9pcD\nAIzuFl8korzBxcQUQqEQPvroIyxYsACrV6/G2rVrIYRATU0Npk+fjqOOOgpFRdmrLWNZFt588028\n8sorWLVqFTweDyZOnIhjjjkGkydP7rMxiIiIiGgAKSyGUVYOV0AuUCKW2+kQEVE3CEDkadPrfL37\nmvoGFxOTuO6663DzzTcjGAwmff2ee+7BL37xC9xxxx047bTTNvt8K1euxPHHH48PP/yw02t/+MMf\ncMEFF+BPf/oTnM7UH1c2xiAiIiIabLrXqbm7tRFV3UBT1UY0ZSonVZdmnUQsU7URh5jyVuShqgPy\nqCJ53kmlMhm3Y3U9AGDcbNWFeKSsixhZuBrLvx4GZ3U5WsRwYB0QETI35jJU8jCxFmJ3k4glMllo\nlKrkYXGHbswAUFQIABC67qHqNG0nEF3q76T676YOte1OGlFHslS3Zai/uxshtdUdlu2tTCaiTT0O\nqkSiv732IQBYAZ1AlO891ibPE1OHRYPx6cNw2IlQVF6XoEpghaM6gSifD1nxtQ51klA/Duut7qyc\n0I058S3rpGLEin+sL5++ik71LapTgh6H3LHIIeBVScACp0oIJiYF1WOdDNRJQYealV230E4FCkAI\nmMKC6LA1hAV02Mo3YsHhUM8b8jj9x2EKAAKGfrOWfKzPb1jqGIj2pKFOJOoLlvA8OiUUkRWdFlMS\nk4lW+/P6a3urk4RWfKIwVYLRTiLax8c/3ynRmDBuYvKwvWt3iu7RPUgqpkp8RkIqqVsv/5u2cW0z\nAGBZ2yr5fLQx43NYVp6urBENMlxZSqKurg7BYBCGYWDmzJk44IADMGHCBBiGgf/+97+4//770dTU\nhNNPPx0tLS34xS9+0eNzNTc344ADDsCiRYvgdDpx3nnnYebMmWhra8M///lPvPnmm/jLX/6CYDCI\n+++/v9fGICIiIqIBwOECqmrgqlWNV5L/2zhRcgIQkRhEzEIMUYioBYgohGXBFDGImIBDPTaEAGIW\nDFgQlgWHir3q257NhFuKzcSFJqcV97q9dSQ81mtlDn1LMwC1sGgvEsav67cvatnPJ5w7W3fXJq6d\nWQkDd1g47PZiYnef7+liYopFw2wuJsZCcoE+1CL/YaSlyQ8AaGxtAtC9xUR2/SDKD1xMTMI0TZx0\n0kn4zW9+g6222irutZ/+9Kc4//zzsddee2HFihW44oorcOSRR2LUqFE9Otd1112HRYsWAQCeffZZ\nHH744fZrp556Ks455xw88MADeOCBB3D88cdj1qxZvTIGERER0WCSMpGYolNz3DF2AlH/VVqmz3Qt\nxFS1EVN1aS6z5MJfFeStyENUPcGRqjPy+GK5+jClvAUAsON+MpHomFwDAIh9sw4wHVj7iQNmSQEa\n/BUIxuQcXCp95lHdmb3ZTiKWqLI/hfI9Cp1M9KrHqiairo0Ih66s1w0xdW92TK3ChGWi0LC7LKdI\nIrbKBQu7+3KrfN7yq1qIbfEdmGNtcjedPIyGVKowpOoehnX9Q/V8TKcOnQjG4pOHeqsTiWE7kSjP\nYafB1OOwFb/V9Qz1t6NLfRu61bbIKQ/0qkUyj5lYf1CnCNvThCISQywSgxWNIRaxYEUtiGgM/qiA\niMZgxSzAaq9H2N6dWS8GogP9c9H+eerOyYndhRM7TetcmX4cS7NoldjtOG4WRvw+qYKtiV2dE4/v\nrlRz6qpTs127MEWtxMTX7VBjwvWxOh2nHycsFqaYa6du0CneS6rEYjJmmgU+3c35u2a533pjhZpb\nKONzDF6i6x+CXOJ9zoOamX6Xwef222/HY4891mkhURs9ejRuueUWAEAwGMSzzz7bo/M0NTXhnnvu\nAQD87Gc/i1sE1O68805UVspbXW644YZeGYOIiIiI+isDKCkHhtTCLMlePW/qX4QlEA1GEPYFEGxo\nRWBdE9pW1qN12Qa0/LAWrT+sRduyDfCvqkdwXRNCm3yINLUh2hpELBCGFYnl74IFERHlHSYTk6io\nqEi7T8d033fffdej87z88ssIqS5yZ5xxRtJ9CgsLccwxx+Dee+/FvHnzsGnTJlRXV2d1DCIiIqKB\nrnu1EYG4+ojd7NKst27VfdltypifTiLq2ohVMbkd6pIpvhrVpXmcSgVuUypr/E3fVtYVK541BAAg\nGmUqcONLATgqyrGqaRgAIKTq87lMC16VRPSoJKLHqRKJKono8crXM+7ObCcRC9Vj+d50ElEUqIVM\nXSPR7UKPxGLtNRBVV2Y7gRhRicSg7rqckETUtRBVAlG0yv2tVpVEbJXvNdoqF82iAZUaDMq5hkJq\nm1D/0H6s0odBdZ3b04cGwuo1NXM7sGPXQkxR29CjvrUKVdKwwozveOxSqUCPw4IQgBmJIBaKIBK0\nEAtF4A9YsCIx+NQl0snH9np4JgDTnmtQbX0RoFVN1h/Vc1VztHTCMLlUP0mpUn7p1ig3ZwkzXXYu\n0+Rhpl2FRYZJrMT3LLp4rfO+Iunrnbo/J+yXOLfOCcTkc++NNeTE6x5SdQ5XWw0AgKbocjknK5z9\nkxNRn2AysYcCuoAz5G3RPfHee+/Zx++5554p95s5cyYAIBaLYcGCBVkfg4iIiIj6EZcHqBgOR3VV\nz24bprxnhWOI+IIIbvKhbXUDfEs3oOW7NWit24DAmkYEN7Yg0hJALBiBiLEhBdGAJSBXfPPxD29z\nHtSYTOyhN954w/568uTJPRpD1zkcMWIESkpKUu7X8Xbrb7/9FrNnz87qGEREREQDTU+TiJ2ON5xJ\nujTLv0KbCUlEl6m7NKvaiA5ZA1EnEctVbUTdpXmY6tI8uliea5tSGS+bVrsOADD8UFWn0CwHADS/\n2QyzshJ1jaMBtHdodqgWth5T10OMtScR3TqJqLZF3UwiFqsaiDqJqGojolB1ay5QScXNSSICQFgm\nlIxQEAglJhBVXTWdRPTrrXxeJxCFSiBaKnIXa5XXJdqm0oEBpxpGzjUYiU8e+nUSUaUMdb3DkE71\n6bqIIr62XVQYCKq3oX+19tg1DuUzJU6dMNS1DvVnZiEWisIKhWEFI2hpk4nDlrARd66IZQJwI6Bq\nz20KyW1jWI7Xqto6h1X0Maa6fESFfhy/jQgLIaGuk2qcElVbob6fEpNvmTKz1t1EMkTmwQ2zm1mZ\n7s61u9fE6pQPbL++6cZMPFYkGQsArE7jdb24nO71RKnOCwBGhtc7Cvkz2gz537ZApFGNHevWXIgo\nf3AxsQcCgQBuuukmAIDX68VRRx3Vo3FWrZK3rNTW1na5X8fXV69enfUx0mlsTN1dq6mpqVtjERER\nEVE3GQZQUAZHbYX8ujuNTymvWJEYooEIrJBMFYYiIUC0t+2IxXTSNLsLckRERNnExcQe+OUvf4ml\nS5cCAC699FIMHz68R+O0trYCkDUNu1JU1F5MWx+TzTHS0c1biIiIiPJd5l2atQ61EYGk9RFT1UZM\n7NLsccguzYUJXZorUQ4AGOaRab8RRXLsLUtk4mfnyiYAwLYHNMvxt5DHhz7eABQWY/n60TAcJvxR\nmajT3W9dhurA7JTj6A7NXlcUXpVEdBfJ51zqBpZO3ZmL5bZTElF3Z9Z/xyzQtRHVfuq99Lgdrp0+\n1ClD2XnZCAQBvyon1Ka2uhuzTiK26QSi3qoUnvorbtivuiwH5XsLhOTnFVDJw6BKIgZ0V+YUCUR9\nnXUdwmAs/rHurOxxCDt5WOCQcylWtSqdkRCi/hDa2qKI+cNoUp2i/br+YkzOrV4lEdept9yiChiG\n1C3MuuZc2JLjBlSqMAR5DcJGSG3l4xjk569TZVFDPVYpthgiEOr7RyfV9L5CxD+fSqYpQMPYvFvx\nDaP3kom9JZP0X6rEn77+GY9pd3XuOuUnRO5uh48J+f0XivkAANGY6rTO22QzksOPrmv8+AY1LiZ2\n03333Yf77rsPADBt2jRcc801PR4roopKO51dfwwuV/ttI+FwfJHabIxBRERERHnG4YYxdDjg9sDY\nlB8LJJSGAGKBCCL+EMywH9FAGF5DLvpF7cQhP0siIur/uJjYDS+88ALOP/98AMC4cePw/PPPxy3S\ndVdhYSGam5sR1P8im0LHZi8dE4bZGiOdhoaGlK81NTVh3Lhx3RqPiIiIKFs2q0szkCSJ2Lk+YmJt\nRJ1EdDlk4UGvKROJpRgKAKiIybs6hqhuzrWFcqyxKh24rerSvPuOslxN4XS5f3RpDDBNrJhbALO0\nCE0Bmf4LWfKv7DqJWKjqIbpU+q3ApRKJui5iYRSuMhkZcZTI92OWybGM0oSaiKobM4pUDUTdjVkn\nET0quahrIvb0777qH8B152Uj0Ca3On3YKh+jLdChG7PeqgSiT9X2a1PdmNtUojCgkodBeZ0CYTlH\nnUQMqIU83YVZJwzDKpEYtXR9QsS9rnlVncMKt4wH6fRhiTMKKxyFN9yKtsYoYoEwGoPyHGujDgBO\nNETk9Vulglj1QTlWW9RSc5Sfnd+S16cN8j0HTXldQpDbmFqUjBjy9ahKIloq8aWTiDFL7qfTbe1p\nQ50+jNmPE5NqnWr0pYlDdScxCPQ8Ndjd82Tr2J7oTvov033TpRy7mzhMlXzsTXZCNia/fy0r1Odz\n6Ld0A5Z8lK+JSeoTXEzM0JtvvonjjjsOsVgMI0aMwBtvvNHj25u18vJyrF27Nm3dwY6vl5WVZX2M\ndCoqKrq1PxERERH1QFEJUFIBc2PX5Wsod4QlEPEHEWkNwQj5IGIWTFcEsRh/rSIiosGD/6+XgQ8/\n/BCHH344QqEQhgwZgjfffDMrabwJEyZg0aJFWL58eZf7dXx94sSJWR+DiIiIqL/IXpfmzrURAXSq\nj+h0eOEwZbossTZikSm7NOvaiEMg/8F2mKorOLpYjr1tmUyNTR+xHgBQe4i65TUsFw03vRGBWV2J\nFS1DALR3E/aoBJzHIdNmuluznUT0yHG9ui5isdzfUWbCUaZqIpap5GGp2paoO1R0TUSdTFQJRKFr\nIbpl2g+OHta700lEXRtR10RUW7SoAoc6mdgit8IXhNUij4n5EmohtiXvxuxXSUTdjTlo6XqE8duI\nSiBG1RRDKoGoyz56VefloV55nYtVArRU+BFtDQAtAViBMJrCbrgANEWcAEzUtXmxTKUkNwblnH2q\nu3JbVL4XnyUTWW2GfJ9BQ16HkNpGTblfRKjHKrmla81ZqkaiTiLqNFpMpEkiJqTWOtbpS5Vo627S\nrbvpv81NC2baQTjfdNUVucvjup08zMe4mPp+tVS5Lf19y6J7RP1W//wvcR/65JNPcPDBB8Pv96Oi\nogJvvPEGJk2alJWxt99+ewBAS0sLfvzxxy7nkHhMNscgIiIiohwwTaCkCo4RNTD04h3lhag/jMCG\nZviWrEdg2XpENrXACrDuOBHlgJWnf7gWPKgxmdiFr776CgcccABaWlpQWlqK//znP5gyZUrWxj/k\nkENwww03AABeeeUVXHTRRUn3e+WVVwAAQ4cOxS677JL1MYiIiIjyXV/URgRg10d0mjKh53IU2LUR\nC81yAECJqo1YGZOlYIa5ZNpvRJEcY8sS+RvWjhWyO/PO+26SY24hk4v+jxqA4lIsrZ8AmAZCCbfI\nenUiUSXjCnSXZrdKIhao2ojFqmNwqUrYlcm5m+VeoEzdKl2qkojFciv01qsSibpGoruHtRB1La+I\nXGizuzMnJBLRqrY+VRtRJxGbVU3AZtWFuMVCpFXVQvQnr4Wou1rrbsw6iRhQCcSwXQPRiHus6QTi\nMI+8riXOGASA8lgzYr4AYq0BwLJQH/LCAWBlUF7L5X45j5XqLTSrjsst4Sh8MTn/FkO+GFCJw6Ah\nY5URUz4OC/l+7eShFV/7MKqSW3ZHZTt5mNhxOTF5mD6JGPd8H6bXslW3sL8mEhP1NKHYaZy8TCCm\nomt16kRi39dtJKLsGhj/Re4F33//Pfbbbz80NDSguLgYc+fOxdSpU7N6junTp2PrrbcGANx1111x\nTVK0hQsX4p133gEAnHzyyTBNM+tjEBEREVEfcbph1IyAWVHZfn8t5UykLYTA2ka0/rgO4dWbEGtp\nA6z+tEhDRETU95hMTGL58uXYZ599sH79ehQUFODll1/G7rvv3u1xzj77bKxcuRLFxcV49tlnO71u\nGAZuvvlmHHHEEVi6dClOOOEEPPzww3aDlK+++grHHHMMhBCoqKjAlVde2StjEBEREeWbbNdG1DUQ\nAZlmS0wi6k7NTodM7On6iF6zFAWmTCBWWDKRWA1ZM3FEkawzuEWxPNe2ZTJVttdE2aW5fG85Rmyl\nAAwDS18vg6O8GPWqS7Ou4adrI7pNnUiU6Z1Cd0KXZl0bsVQm68xS9V4qdKdmlUYsK7JrI4pSmaoU\nBSqhqOo5bnZX5rDcGiqBiJDq0uxX/7DdppOI8Vuh6yHqJGKzfM/hFlXfMOCGPyiva0DVRGxTScTE\nGohBnUBUW51Q1GFJt0ogVrplCqrCJedciRbEfG0QTX7AsrDRXwAHgB998lr90CrPt1pNvTkk59gU\nia9/2KpSiH7Dh6CpEoiq5mHYUonLhARiLKH2oWXpx/EJxMSkYWIisV18wit1Wi13C6RC9F6Qoa+7\nNXdX76YH+9+it91JWvD+2EwJIRs/5SPBz3FQ42JiEj//+c+xapX8S2B1dTVuuukm3HTTTSn333HH\nHZO+/v777+O7777rsnvy4Ycfjquvvho33HADXnjhBYwcORI77LAD2tra8Pnnn0MIAa/Xi3//+9+o\nrq7utTGIiIiIqJcUFAGllXBsKs71TAYtKxRFqKkFkZYAij0+AIBp8BdhIiKinuBiYhJ+XdsFwMqV\nK7Fy5cou949Go12+ns7111+P7bbbDtdddx0WLVqEDz74AABgmib23Xdf3HHHHZg8eXKvj0FERESU\na7msjQi0d2rWacRiVKEqJrs2D3PL5N/IIpkI3LpUJoN2qWoEAGx7YIs8tUeOvfHtKBzVVfjRNxwA\nEFUJLY9KIOraiLomYqFKznXVpVluEzo16/qIulNzSTFEsUokqm2PayLGVJIoqGogBlXyUJXWMdri\nH6NVd2eWf5+2fKomYJOqCdiSmESU78UfktesNexCQHVl9sdUTcSYrn0oj4mqJGJIbZ0qgVisrmeV\nqi05xO1HzOeHq7UJIhRBY8ALmMAylUBc7JPn1jUQG1UCsTEs59ys0oWtKnXoN+QiZMhUWyGfj8ZC\niImQmptKXOpaiCmSh+214xITh6mShilSaKk6MudVZwT5ntL+bPfAYA5G5ddnnKn+l6YkouS4mJjE\nLbfcgoaGhoz3T5X2e/DBB9Ha2gpXBreRHHvssTj22GNRV1eHlStXwuPxYPz48d1KEmZjDCIiIiLK\ngsIyOEcWy8VPX64nM7hE24IIN/sRtOTf551qkZaIqN8RyN812P64nk1Zw8XEJHbdddesjLPnnnt2\n+5ixY8di7Nixm3XebIxBRERE1BfyqTYiABSZ8h9hKyy5rTZKMbxIHjO2RJ5ruzKZOpu14woAQMEU\nmXQLfBEBPF4saZoIw+20E3aanUR0yFRagUslEj0yzWZ3aS5RXZpLknRpBoAStS1tTyICgNDbwkKg\nUL3W0yYvuiaiX6bvoBKIRquK8SXWRGyVdQStJrX1qSRis/xtM6w6NAf8cu46idgW1nURVcfmmMOu\nfRjSiUTVlTmmth51e3K16sY81BOCiMZQFduAmK8NgYABGMAG1YV58UaZMl3aKo/fFJTH14fkdW+O\nqVqIKnnYasou3IkJxEgsvg6iTiFaIoKY3YU5IXmoHutkYntX28TVAfW4UzfmdL+t5+sqQ2eZrzvk\ndx3E7Os/n+FmU1HS/pmqJKKOuJhIRERERLS5DBNGVTWMomIYfv4Vu69EWkMIbqpHrC2I8lK18Mlf\ncYiIiHoV/5+WiIiIiPpcvtVGLMUQAEBVrBIAMMwt9xtV7MA2qjbirkM3AQC23Fem8yyVtlv+dhGc\nQyqwqk023YupOeraiAUOmUbzOuW20C1TbIUqiegtTujSXKbmXq5qI+ouzaWqRmKx6tRcpJ4vTHjs\n8aDbdLdQv3xvhqohbvjUAp3etqhkYrNM6VmqO7PVrGsiynFCrTIZGvCrBGJIfi5tYbn16ySiJffz\nx9o7M4dVLUT9LeBVNRGHqOs21NuKWHMbXC2NQDSGhmABnA7gfxtkmvTrZnmODTIkiU1Bed0bVDfm\nZsj30mrIBGLAlLUu7QRiVL73qKqZ2KkOou7IbOm0oZWkG3NiArG7ycM0abWBWCzQ/pkfREk9YGB+\nlpRd+fotkq/zoj7BxUQiIiIiop5wOIGyKjhrKnI9k0EhFowg3NiKkNgIAHC5YmmOICIiot7AxUQi\nIiIi6nX5XhtxqCFThcOLZaptnKpXOKUsiBnTVsoxxsmxWhbGYJSW4jv/RBiGYXcXNlUtP52k07UR\nC1V3YV0bsaBQPnarxKOjQndplulJu0uzTiQW661KIBbK10WB2k9vHQ50W0h1aW7TSUTVLUYnEZv1\nVqUxG1VNxIQkYtCnOi8HVFfmoNz6I/E1Ee0OzVZ7ElE+ltfAbQiUOOV1GeqNAEKgxtiAWJMP0UAE\ncADrWmVtyHfXyS7by9rkGBsCci6bQvHdmFtU8rDNbJJvWSUQQ5bqxmzpJGJ8AlHXQeyYQAQ6d2YG\nYj1IIKbqztyzqE8uatBlvTszE3oDHmslEg0cXEwkIiIiIsqU0w1zRCUMtwdGIMuLKWQTMYFwvQ+R\nxlZESxrVs7zeRDTYCAgrPxdhuf4/uHExkYiIiIh6VZcJphzXRqzxyP1GF8vE3KRSeevsHjXyVtox\nM4OIbpTz/H7+cDgrSrApIM8RTaiN6FbbQqdM7RWoRGKRSiJ6ilUisVwlGctVl+YK1Z25THVgLpPJ\nO51IFIVq61UJRK/a3+1Ct+kuzQHdhVnVSGxVCcQmlUxska+LRrmNNcqUXrRJvseQL74mok4itkV0\nTURdC1HtF5Ofm04ihlRnZrdKc1aoztY1Tj+qYutgtbaiLeACPMDievnZfdEk3/dqNfUNKWoh+gy5\n+BjQXZkTujFH9TYxgWgnDqMJjzOvg5g2gdjN377zOcnV23PLevIxB/L58yMi2hxcTCQiIiIi6orH\nC6OiGs7WklzPZMCKBSMIbGqF32pERbkv19MhIiKiLnAxkYiIiIh6RdJkUTdrI7YnEpMnEXtaG3FE\niTxuvFof3LFcptX23GOVHLfCCRgGNi0Zgh/Co4HVQMxO01nqHGrrlAm2IpWuK/LKxJtdG7FM7udU\ntRF1EtEoV0nECpVELJFboROJBer1ApVEdHUjiahvi4vI1J0RVK2NdZdmlUiET3dnllvRFF8bMdqg\nkojNcu4Bv5yLnURM6M7cllATMawSiYlJxGq3vFbDRBMi9T5UuxqAUqCpzYv/rh0KoL0r8/qAnMOG\ngLyeDTH5HlpMuejoMxvUOZLXQozE5HtJ1Y05MYnYs07MPUsgMrmWGq8NEWTH5HxtcJ6v86I+wcVE\nIiIiIqJEngKgtArmJjewKdeTGXgiviCC9a0odzTJJ3pwxzYRERHlBhcTiYiIiCirupNI7LSvSh6m\nq42oE4k6iehyyFRfoVkOACiBTLcNiclE4jC3TC6OKYmvjThjxAYAwMi9ZGotuMyEUVmFRd+OAQD4\nok47kejRiUSnPLZQJRILXapbc6FMAXpL0tRGrFRxSFUbUZToZKJ8XhSo7s3drYnYIY1o10QMyFSe\n4ZfJS7tLc4tKIqqaiFaTSu81yfcUblJJxFaVQAx6AACtITmnVlUbMWDp2ojx3ZnDautSH+8wjxx3\nSKQekYYWVLp8QBlQ3yY/l3krawAA3/kcWOtXScSgnHOjkHP1qRqIbaasiRgU8nH2aiHqZKKiXs+o\nE3OaJCJTdkRENJBwMZGIiIiICAC8RTBHlAKmmX5fypiAQLjZj2B9K8pccgGQSUQiogyIThUW8gf/\njWRQ42IiEREREWVFyu6rcanEdN2a45OIhtqmqo2ouzUXmVUAgApLJhJ1bcTaYnncuBJ5vp0rZHpt\nj91UbcQiE3A4sPaHkTCLCvBdYzkAIKZ+SfKYAoUOmVwrcKiaiKreX6FHJt8KVCLRo2ojOlRtREe5\nro2oujBX6ESiSiDqGokqkYiCAvRIYofmYABGa3y3ZvjU4+b4JKLVJI8NN8k3HGhRSUTdpTmsHuua\niNH47syhFEnEIZ4YIASGROsRqW9BhasVKAM2tMnP7XWVRFzSKg9Y2yav3cawH41QtRBVV+Y2tQ2r\nWohhuyai6jCtEonZr4WY4jf4LlKITCASEdFgwMVEIiIiIhq8CouB0kqY9T1cyKOkws1+BDf5UOZu\nkU8wiUhERDRgcDGRiIiIiHokZRLR3kG/3n7bcGIi0bATiF3XRnSZKpHolEnExNqIVTGZTBzmlvUG\nRxfLBN3kUpkumzFiHQBg5K4ymde2wg2zugrffTcKANCqEnea1yGPK3BYKHLK9F6xWybh7NqIxTLx\n5iqXaTRHuXoPFWphskzVPixXycOEJKIoVrUSu1sbUQvIRJ6hOzT7ZKIPrX6gWdVGVF2arWaVRGyW\n7yWqkojBFnm9W9t0ElHVRlRdmnV3ZjuJqGojhhKSiFVumfobEmlApKEFVa4WoBxY3yqTiK+qJGKd\nSiKuaZP7b4rI+oaNkIuOPrMRftGoziHfQySmt/L9dr8WYvIkYtpaiOzITET5gLc5Ux7iYiIRERER\nDS5FJXDUVnS4xZo2V8QXQGCTD2VOJhGJiIgGOi4mEhEREVG3ZFYbEYirj9ipNmJijcT4JGKmtRGr\nIZ+vLZKJuvGqNuIO5TKJN2OXlXJ8LwCHEytWTIBZ4MGPzTId2LE2IgAUmKo+ourYXOwOo9ibrjai\nnKuhaiSitDhuK4rle4DaikKVWHR1c8UtJueENpVEbJOJPaNFJRKb1LbFb3dpjjWruoJNcs6hZpU0\nVDURfQFdE1F1abZrI8r3FlSJxLDqaK1/eSh1yfGGiWaENzZjmKsRKOucRFzqUzUR/XLuG8Pyc2k0\nZCOWFrNBzsdSacRoKyIxmaZM7MqstzqJKFRcR4iI2vZeLcT444mIiAY3LiYSERER0cBXXAqjtAKm\nz5PrmQwIsWAE/g0+lEEuAjKJSESUfSKPuzlnWAmCBiguJhIRERFRRnqUSARUKjF5t2bTTiLKVJ+u\njehSicQCRwUAoDSxNqJLvj6qWI4zSdVG3HvEevn8jjK117yiAI4h1fi+bjiA9uSdnnGhQyUSVcfm\nQqdMven6iMVFIXhLZPLNXS6PMsvlnM0KlUQsU8nDEpU41IlElUC0k4i6W7Mjvj5jWiGZ0DNaVRJR\n10Zs1olE+bxoUvURG4OINcr3E26W193fKhdR24Jy6wupmog6iahqI+okYlDVRNR50gKV3BzhbEV4\nUwuGYiNQDGzyy/f2n1UyifiDL74m4vqwrHPYaDbJ8zpkEtGvkojhqOrMrOohRqxAe03EHnZlTlsL\nUWMSkYiIqEe4mEhEREREA1NxKZwjq4F0jWIoLRGzENjYCn9ko3yiJLfzISIiotzhYiIRERERdWnz\nEomyY3NiItFhetVWpvzczsTaiNUAgHJVG3EIygAAtUXyuHGqNuKUMpnam7nzCjm+C4DThWVrtobh\ndWOJT9UpVHPStRG9CbURdcfmkgLVsblIjuspt+BUtRF1EtEoV0lEO5GYUAuxSCYQhUclF/XWzHBR\nMyLnors0o1UmDnUy0a6N2Ci3VqNM9VkN8rhwIxDwqVqIft2lWdVIjMjr74/prZxTWCUSVVVGOA2V\nRPSGEGlow9DIGsAt4Ff3M89bIz+X73zyuFWt8TUR602ZPOyURFSdmcOqLmIsoS6iZYU7dWXWNRHT\n10JE/H72DuzKTET9WJ7e5py386I+wcVEIiIiIkoq/SJi8sXDZLc069uZExuseBwy4uZ1yMXCEgwB\nAFTF5Ha4S94yPLJI3c6smp/MqJEJuTHby0YejUvl7btf+8fDO6QUAcsEWgGHmprLlMcVOvQiolyw\nKnHLhariQrmoVaBvaa5Q76TSDbNC3Z5crhqrlKltkVpELFK3M3vVfm5VQDDTBithtVgWlAtxRlAu\nDho+tXiot43xtzPHGuT+0Qb53oJN8lZlf5sHrSG9eCi3/qh8rU0tGob0IqJqrKI/0WEeeV1GFrch\n5mtDaK0PIhrDJ42VAICvVAOXVa3ynOvDcsGzAU0AgGbHJnk+u6GK7O4cseR70rczJzZTaV9AjPD2\nZSIiojzHxUQiIiIi6v/cLjiGVMHbXJrrmfR7UX8Yocb1EMEwhGWmP4CIiHpNvjY6ydNpUR/hYiIR\nERERxemUSOzG7cxy27nJilPd1uyyE4ly0a/QlA1VKsQwAEA1ZCSwtjDhduZymRzce4q8nVkNjW8+\nGQJnRQlWe4cD9UBM/Xbj1bczO2R6TTdYKXapJKIn/nZmb7l83Vkp34ujUt3SXFncnkgsUY1VihNu\na/aoDtHpkoiWmlxUNRJJTCLatzWrbbO+rVk3VlGpPnU7c6hRzjXQqm5lDsg5+0KujBurVLvl+64t\n9EOEo0BTA6zWABY3lQLw4n+N8r2tVA1V1qm51kMmQptNmURss+oBtDdU0bcxZ5JElNsOacRMk4i8\nfZmIiCgnuJhIRERERP2TywXv6KEwvG6gNdeT6b+EJRDZ2IxoUyvcjmj6A4iIiGhQ42IiERER0SDX\n8wYrOomYWCMxvj6iy1kEt0Om+QocMnlYDJlIrFQNVoY5ZepvdLEcY1KpTJPN1LURJzcBAOrrZPru\nm9B4eKtL4Fe1EQ2VPiuUQTwUqkWxIlUbsdgjE3BFXlUbsVg+76mQaTdHpZp7pap7WKHSiOUlEKXq\n1mmdSCxQiUR3hjURVUMVhOS5jYBOIsqtbrACn9o2y2SiaNZJRHlctEm+x0CzPG9bm7y+LSGZHrTT\niFEHAjqJGNOfjTy2SCU1RxTIMcsjmxBtasKqpiIAbiyoLwcA1Pnk/msDMj25QegkokwgtlkykRhU\nNRE3K4kIJKQRWQuRiAgAIDr1nMofvfif3GeeeQbLly9Pu9+YMWNw9NFHp90vGAxi3rx5WL58OUzT\nxLhx4zBz5kw4nZkviWVjjIFkcL5rIiIiIuqf3C44h1bB28LaiJvDCoYRWt+EYmdTrqdCREQU5777\n7sNbb72Vdr999tmny8VEy7Jw55134rrrrkNLS0vca0OGDMGtt96KU089tctzZGOMgYiLiURERESD\nVDa7NQOwOzY7HTI9qNOIHkcpCkyZSCyHqo0oZHfgEQW6NqIcc0q5TLT9ZDtVG9Ehow9f/W8onJUl\nWO2tARoAoboQ65qI7bUR5bZI10Z0ywRecZEc11sqE3LOClUbsULVRqxSaUNdH7FcdpcWJcUQReq5\nApVaNFNct0R2ElHVQGxTiUSdRGyNr4koWuTrVqPq0two30uoSc7V36ZqIgbiOzW310eU+0UsA1Er\nfo61BfL913hbEN3UjMaNUbgBvL5eJkO/b5H7r2yV121DTM6twdwgp2rIRGIwKhOKEZVEbO/SLOfc\nsyRih+flzsgEk4hERAPbFltsgZ/+9KcpX99yyy27PP4Xv/gF7r77bgDA2LFjse+++yISiWDu3LlY\nv349TjvtNKxduxZXXnllr44xEHExkYiIiIjym8sF75ihMD1uoC3Xk+mnBBBubEU4slY1gvHkekZE\nRJROF5Ufcq4P5rXVVlvhtttu69Gxzz//vL0IeOqpp+LBBx+0b0n2+/346U9/itdeew2//vWv8ZOf\n/AS77rprr4wxUHExkYiIiGiQyV635vjaiE7Vqbm9PmI5AKDIrEZlTCbgalQX55G6NmKZTJftNUwm\n38ZPagAAbFouk4JfhcfCW12CkHAAfsChpuQyVQIxoUtzkVvVSCyQSTldG9FdrmojlqsUZblKJFbo\nRGKJ3Jaqjs0l8rEoLm7v1pwpXRNRJQ+NNrUC2qKSiC3qcaPq0tws5xprku8h0iTfpL9ZdWkOyvO3\nhuQ1a42obRedmod45PseWdwGyx9EdGMDRCSK/26StSq/aJLHrmyV129tWKYn61VNRJ9KJPqtJjmn\nWHyXZl0T0dLbVElEEd/QJWkSUR6AVJhAJCKi7vrNb34DQKYJ77333rjahoWFhfjnP/+JCRMmoKmp\nCddccw1ee+21XhljoDLT70JERERE1LcMtwvOkTUoGFIKo9NiJ2XCisQQXrsJ4dUbISLs0kxERIPD\nF198gW+//RYAcO6558Lr9Xbap6qqCieccAIA4M0338TGjRuzPsZAxmQiERER0QCXaW3E9qcdcc+n\nq43oMlUi0SlTh4VmOQCgDDUAgCGxKgz3yn3GlsixdW3EWVvJ2ogOl0yfffnlMLgqS7DGOwxoAixd\nG9HUtRFV12anTNTp2oilHlmfsLBIbr2l8nVneUJtRJ1ELNVb1aG5RNVF1B2bC9XjTDo2x+S57ERi\nm0okNvnk8zqR2CS3olGmAGON8hpEm+R7C7bIa+Nvk0lEu0uzTiJG5evtnZrjP1fdqXlMoR+l4Q2I\n+VqwpKEMQBE+3CSv//JWef1Wq7luQiMAoNFcL8cW8nEopro0R1WXZkumJ9MmETOpiSgPQCpMIhIR\ntcvXbs4ZlrfNif/85z/21/vtt1/K/fbff3/cc889iMViePPNN3H88cdndYyBjIuJRERERJQf7NqI\nLtZG7KFIWxiBjetR5G5JvzMREVEeCwQCeOONN7BkyRLEYjEMHToUU6dOxdixY7s87uuvvwYAmKaJ\nbbfdNuV+U6ZMsb/+5ptvsj7GQMbFRCIiIqIBKnu1EeVjhynTfYm1ET2qDmKJKesiVljVAIBhpuyI\nPLLUja1LVW3Eoao24gS53bRaJgC/jY1JWhvRm1AbsdApE3ElHtWtWdVGLCyRj93l8jyOCl0bUXVg\nLtPbhARisUwoioIidUJ1G1MmicSEbs2GXRNRJRJ1MlHVRow1yDRgrEm+h3Cj6tLcqhKIqjaiT9VG\nbO/SHF8bMZLQqXl0UQgiZmFEZBks4UeTVYBXVshU6HfNct9VbfL6rFOJwwaHrInYZm1SY8vnQ1E5\n52hMzVUlEVN1ad7cJCJTiERE/Vs0GkVjY2OX+1RUVPRo7Pfeew/7779/p+enTZuGm266CbNmzUp6\nXF1dHQBg2LBhcLvdKcevra2FYRgQQtjHZHOMgYyLiURERESUM4bbBeewKhS0yQVJri11X6SpFaGN\nLbAq/LmeChERZZFAft9ODADz589HZWVll/uIHrwJ0zQxZcoUjBs3DiNGjEBLSws+/vhjLF68GB9/\n/DH23Xdf/OUvf8H//d//dTq2pUX+A1lpaWmX53C5XPB6vQgEAvYx2RxjIONiIhEREdEA0e3aiHYi\nMXkSUddG1N2adRIxsTZiCWQicVhsCACgxiNTgONKZaJuu7IwZk1cJcdUdf2+XjwMrqoSrPcOA1qA\nmK6N6LDitgVqm7I2YrmqjVipaiOqJKKhE4nlKnFYrLo0F6nnC1UyUXdq1klEVxeJxLBK4wVlGtII\nyMSh0aoW8Zp1ElFu7dqI9XKu0Ub5XgLNKnnYpro1h9VW1UZsTyLKa6JrJOpPsVp1ah7trkdkQyPc\nwg+UAwvXys/hs0Y3lvnkdVkTknPcaMoEot2lOdYk31JU/uITsWQSMRrrujYik4hERNTbLrnkEjz5\n5JOorq7u9NpLL72E008/HfX19bjwwgux4447YrfddovbJ6DqAnv0/8d3QS8E6mOyOcZAxm7ORERE\nRNS3VG1Ed1UpUq1/UmrCEgisb0Z45XqIUDjX0yEiokFs9913R0NDQ5d/uuvggw9OupAIALNnz8YL\nL7wAwzBgWRb+8Ic/dNpHd14Oh9P/f2RIlSxJ7NacjTEGMiYTiYiIiPqp9ElErXu1Ee1uzapGokcl\nEXVtxCKzCgBQZQ0DEF8bEUCn+ojjxtdjw6oSAMD3GANPVTHCwgQCgGnIfXWX5gJTJuqKVRKxWNVG\nLPKqRKKujVipayPKc5pVuiaiSiKWJnZnlq+LArWfWyUNnOqvw2bCNbNUgi4aBcK6NqLq1uxXyQOf\nqpHYrLs1yxSg1SATidGG5LURfQG51YlEv+rSrBOJuiZie1pTzmWLYj9irQEMjS0HSmNYuVFe95dW\nykTijyoYuaotiA2iGQDQ4Fgnx1a1EbvfpVk+ZhKRiCgHhAFYefqvbur/BpxOZ49rIvbUnnvuiZ/8\n5Cd466238PbbbyMUCsUlCEtK5N85fD5fl+PEYjEE1d0G+phsjjGQMZlIRERERL3O8LjgGl0Db3UJ\njE6LnZSOFYkhvHoTwmvqgWgs19MhIiLKqalTpwKQqcB169bFvTZmzBgAwLp16xCNRlOOsXr1aliW\nFXdMNscYyJhMJCIiIupnMq2NmJhEtJ/vYW3EMsgOwUNiMpmYWBtxSrlMt82csEqdVqbSvtu0JTYU\nDAF87Xk2j6kTifKZQkdiIlGOlbI2YqWujaiSiBUqiVgqUwHCTiTKbs3dSiIC7WnEUAjQicQ2VRux\npU1tZSJRNKrOx5tZG1F3a9ZTG1Egr8FIcyWsYBMsB4BS4M0lIwEAXzXL4+tUfcR1YTmferMeLYas\njRiIyg6boVh8l2ZdG7G9S3NIPdY1EVVCsVOyUL/OJCIREeWOabZn4/RinjZ58mQAMjW4ePFibLvt\ntknH+Oabbzodk80xBjImE4mIiIiod3jccNSOgKuyhLURe8AKReBfvgFWfWP7YicREQ0eQlaZyMs/\nOf6/pS+//BKAvM26pqYm7rV99tnH/vqtt95KOcabb74JADAMAz/5yU+yPsZAxmQiERERUT+QNI24\nmbURHaomotMhU346kahrI5aYshZfhSWLoKeqjbh3jazHN2aMTMGtX1MKR1UZ6iIjgbVAyDLs2ogF\nKpFYoJKIRS6ZgCt2q0Siqo1YoGsjlqvaiJVq7iqRiEpVl6hMdWkulXO2ayQW6ERiF92ZgfZFuohM\nARoBlT4MqI7NrW2ATiQ2y0SiaJKPrSa5T6xp82ojaqUumayYWNaMaH0zyrAWqAEWr5Bdsj/YJN/z\njy1yzqv98vwb0AQAaHLINGKbtQnBqHwuopOICbURY2q7uV2amUQkIqK+9vXXX+O1114DAOyxxx4o\n0PWQlV133RVjx45FXV0d7r//flxwwQVwOBxx+7S2tuKf//ynPUZtbW3WxxjImEwkIiIiouzxeOAe\nXQNn+eApQp5NUX8I4eVrEWtsyfVUiIiI+twnn3yCZcuWpXz9008/xaGHHopYTP6j5K9//etO+xiG\ngWuvvRYA8O233+KKK66Iez0ajeKcc87Bhg3yH+Guu+66XhljIGMykYiIiCiPpKyHaO/Q8fWe1UZ0\nOmQi0WWqRKIzvktzKWQSTtdGHO6V+41XtRG3L5PpwRkTVgNQYTXDwLeNW8FdUYy1KpGnM21eU9i1\nEYucUbXVSUS51bURPWWqNmJ5Ym1ElTqo0IlEVRtR10gsVLUTiwoTr1hyOpEYUglEv0wbGq2qHmJr\nh7qIKpFoNat0X6NM9UUa5BhBX/dqI0as+H/Pn1DSBmFZqHXXQRit8FtuwAm8/K0s5P6Nqo24vDW+\nNuJGU/4C48NGAO31EcOxNkRUt+aY3aU5PomYujYiuzQTEeUTIfKzTkhvzeuf//wn/vznP2O77bbD\ndttth5qaGlRXV8Pn8+Gjjz7CvHnzINT/N1177bXYf//9k45z8skn49VXX8VTTz2F22+/He+99x4O\nPPBARKNRvPDCC1i0aBEA4JJLLkl5e3I2xhiouJhIRERERJvFKPDCrK6Gu6E411Ppl6KtAYTXN0LU\ntuZ6KkRERDm16667Ysstt8RXX32Fr776Kuk+22yzDW644QYcccQRKccxDAOPPvooRo0ahTvvvBML\nFy7EwoUL7de9Xi9+97vf4fLLL+/VMQYqLiYSERER5VD3kogAYHaRRNTdgD3qsa6NKJNyLqfq0qxq\nI3pNVRvRkLURKy25HeaUi4IjS2SybktVG/Enw2USbuSoZgDAmtUVcA4pR12kBlgLRFRKwanqI7od\nclvoiNmJxGK3TMgVFcqtro3o0rURy1WaskIlEXW35rLEbs3Fcdu0tRE1nUi0OzTLBTzDpxby9LZR\ndWpuaoPVKBOJuktzuFnVRmxTNRGD8YlEXRvRrxKJYVUbUX+UVao+5FbVGxHb1IACdxMwDvhy8XAA\nwIJ6+Z6+b05fGxFA0vqIPa6NyCQiERHl0AknnIATTjgBdXV1WLhwIdasWYMNGzbA4/FgxIgR2HXX\nXTFlypSMxnK73bj11ltx2WWX4dVXX8WyZctgmibGjx+PQw45BOXl5X0yxkDExUQiIiIi6r7CQnjG\n1ABOB9Cc68n0P5EWP6KBNYBlAe5cz4aIiPKVsNLvkxO9/G9MY8eOxdixY7My1tChQ3HaaaflfIyB\nhIuJRERERH2oJ0nETsen6dJs10ZU3ZrdzvguzQVGBQCgQgwDAFQL+Xh4gdx/Qqmcw47lso7hdFUb\nMRJyAA4Hvm2ZDKcowMaQPK+lEolelUT0mrK2X6FTbovdYRR7ZUKusEhu3WXytyNnhUpTVujaiCqJ\nWKqSiXYSUXVpLlJJRDVXuDJMJKpC7XYi0eeT2xa5RZPeqvqIDWrbGEGkKbPaiAGVRAxZujZifCJx\nUnkLRCSKmvI6oDyItvVOwARe+nQcAOArXRvRJ+e6NizrOG4yZU3EFshEoq6NGIrJOUd1IjGmajqK\nMGsjEhERUa/hYiIRERERZcQoKYajqgLO/2fvzeMsO+s6/89zlrvWra33dHc6O0vYAwSQIeCMjom4\nAALRIIgDAcVBGMUfYoBxAUQGBx1QQJyJSoIKIrIpGdEEBJOYRJhAErKR7nR6r/3uZ3l+f3y/33Pr\nPFW3lu6qW9Xd3/frlTx1zj3nOc85d+mq577P5ztZ3uihnJYk03OIT8wA5fZGD0VRFEVRFOWk0clE\nRVEURVGUdWBZAzHbcAUmIuDYiGSwuSaiZCNKJmLIbdkn83AYlIm4JaUqzTtCenzvEPXz+GGy1f7T\nniO03U6y8x47sgXh9jE8HG0DjgAJy2oBD63AlZrFRKyGXKm5QGbjULWL0jCZcWIi+mNkFpoRnpgc\n4SrMNTYTa5yJyNWZLVeURplbb4XXt0tjMU3ORpzlTETHSLQTtD6dpom+ZJLG25ny0GrSdZ3jKtWN\nLpmIzZiuW5urM4uRKM/grjKd/0U7jiKdOIFipQVsB759Fxmht54gU/S+Wdr+YIO2P2bpvvEpzkas\np9R2Etqwy5Wa45RNxKxiM9uHNoLln/tmIwqOkagmoqIoyubB2s17m3MfoV05S9DJREVRFEVRFKUv\nZngEhcJOGM9oNuJJ0J2YQ5oeor+6Khs9GkVRFEVRlFNHJxMVRVEURVHWgPUyEXuVmnu5iG6V5sAn\nW0+MRMlGrHpkII5xlebtZgQAsHuI9pNsxGeNken2lPPJSGw1CjCFAu5tPRWeLWC6S8cTI9HNRswq\nNRfJAqyWyLArD9H64lgKf7xPleYaz7ANs4lYcUzEEmcjrrRas5uN2KTcwaxa8wwbiVNsJE7R48kE\njVkqNrdn6ZzrjQLqXTISmxGtkyrNERuJMVsjPj91T942CduNsHXnY8DuLuoP0/q/+xoFyX9nhs7l\nEc5GPNShMRz3qDrznDnGx5kGAHRjNhITep6kQvNiRiKRwtrEuTBarVlRFEVRlLVBJxMVRVEURVGU\nHsbA2zIGf6QGb0rLDK8aCyRTM0gnZ4At3Y0ejaIoinKaY+0Kv6wcMHqb89mNTiYqiqIoiqKcBIMy\nEQ2v9wxN7HlekFVpDtlIzKo0e5KNuA0AsC0hM3FnkbY7r0ZG3ROHyWB70fmHAAClKi0fmDwHhe1j\neKRVA1q9MRZ4aEOcjSgmYjXgbMQSteUK2X2lUbLignHORxyv9Ko0j3E1ZslEHGITscyPn6yJ2KWJ\nO9OiHEFTZxNRjETJSpzibMRJOsFkksbemaKxtupcoblF42h0A7QS10ik50xMxN0VOub5e4/ATpxA\nWIuAPcD/+9pWAMCtE2SE3jtDO2TZiFyNeZKzERspmYktMRLZRJRqzZmRaKntVWxeLB9RqzUriqIo\nirI+6GSioiiKoijK2Y7vwxsfR7FMk1/obOxwTjusRTQxCxse4hWb0yJRFEVRFEVZC3QyUVEURVEU\nZQWsv4m4MBsRWFipOfTKC6o0D4EMxC0pG4kBPb6nRn1dXCP77IodEwCAfedOAgAmj1XhDQ/hgWgf\nzAmDdsImIQ+x6Fk+Dtl/FTESC/lsxMoILYejtL0/xhblFslHHALGyJ60Q2wmDtEYJSMR4QpNRKFD\nxzZtsgIh2Yh1svkwx+0MtXaS8wa5WnM8ydmI02QbNhuUi1jv0NilUnMr9tG1ko1IFybk6/LknSdg\nO12M7j0OxBGa36PHv/KVfQCAu2eor+9LNmKbDMMThq7/tHeUjpFM0Skl+WzEOKGxinkoZuL8bERa\nsUTFZq3WrCiKcvpiAZtu0i+oNunt18pg0MlERVEURVGUs5EwRLhnB0ypCDOtfxCsGmuRTk4hnZ4F\ndukEnaIoiqIoZw86magoiqIoirIIG2UiLlepueQNZ9mIUqV5K8j6O6dKdt0FNTrmM0bJbHvWhXT7\nrU0NYAwemLkI4XgNh1oloA5YHmPJS3NtOeBqzaFkI5IZV6mSFVgcoe38MclGpLGacbYPR7kdqcHW\najQGMROLRawKyUZss5HYJHvPNNhAlEzEOTIUMU3rUzYRkykae8QTp022BhttGsdcl56HthiJnI/Y\nTQ0CNhHPrVKf+y44Bjt5AkGNzv+7N40CAG49Qc/LPVk2Ih3zaExjm/QoG7GOCT4GGYluNqJrJC5W\nrZmWV16xWY1ERVGU0w+LzVvoZLOOSxkMOpmoKIqiKIpylmAqZXhbtqAwWdvooZy2RCdmYIcO85K/\noWNRFEVRFEXZCHQyUVEURVEUBYMzEWUCql8mou+RKScmorRlQ9bbCLZhS0I/S5XmvUPU5+OHyWD7\nwXNpsmtkO5luhw+NI9w+hu93dwCHgZgFtoCHWmATseKaiCGbiBVqSzVaXxilHb3RRbIRAWCEKzUP\nc05irQaIkeit8pZqMRIbnInYIMvPzDpVmrm1U1z5eIoMxniazq0zk89GbHTIRGxE1DZjNhE5HzHl\nLKiin+LJu4/DdjoYPn8GSGK076VD/t+v7wEAfDvLRiRN43CLxnAMZB5OSTaidbIRYzYSUzIR06xa\nc95EXLxaM7CSis1qJCqKoiiKstboZKKiKIqiKMoZjBkeQTHcRZOgsxs9mtMPa4F0cgp2ZhY4d5VF\nYhRFURTllDCwm7bQyWYdlzIIdDJRURTlDEXsKLVSFKU/K7IRF5iIgrd4H1kWovyalTcR3UxEaQtc\ngbngkcFX8sjqq4FyEcdTshG3BVXsGaY+Lhyi9/fztpHxduE+yuKrTxdgyiV8t/kEeHGAmYi2T/jj\nwK3SXM2qNFM7VCZTrlyl5UKWjcjnMlaicxmlMWOE22HORayxmSg2YrmMVRORlbfQRJyjdoaXJRtR\njMRp2k+yEVtznI3Y5CrNbCI2YmpbXME6Sqn1DOcjDtWRtrs4b+/D8EcMAB/f+yKd9zePnQMAuHeG\n9jnYoOt0NKKxTHj0PMziGB+Dnp+IsxElI1GqM/czEldUrZl2gIt+9iuKoiiKsl7oZKKiKIqiKMqZ\nhO/D37EV3lAF3qT+qndSWIvo+AyS6TlgVwSgsNEjUhRFURRF2TTob5iKoiinKSvNd+u3nVorytnM\nku+fkzQRe9mIi1dn9jxqA4/stpCrNId+3kSsggzE0XQrAGC7T7bfrhpNaJ0/BFw2Rhbe08+XQiDE\nwzPno7BlGI/Wy0C9l/tXYBOxGnA2IhuJQ5KNWMxXaS4N0+PBKFdpFhNxrEIHGpaWjEQxETHEyxVe\nLpzEbcEdrtZcZyNxhu/NnuZ2isxEO80m4iRZlG42YotNxCwbsSsmIj0ecTZinEolazrnJ+08iPTE\nCQw/n/bv3GPxj1/dAQD49gzlLUo24mNNOvYxTAMAZvwTdKyUzMQsG9Gp1pwZiZbaXjaiGIcnbyTm\nt1cURVFOayxg0815O7FWcz670clERVEURVGU0xxTLsPbMo7C5MhGD+W0pn18FqmVCVq1ERVFURRF\nURZDJxMVRVFOM1ZccXaF/WykwbJW56IWjrJSVmckrrRKc8itZO4tXqU59MhEDNhIzExEjwzEkXQL\nAGALyETcUSYb8Lwa9fvEYbLXfmDvYdTGyd47cnQcwbZRPNLZDhwCIrYXfB5qyc+biGXORqwV2Eis\nUD9lqdI8xmcmVZrZSMwyEUfEQCQz0VbFROTHJRtxtRWbgV615jrZfGaasxEdIzGdILsv5WzELsUR\nZtmIrRY9H/VIMhLp+ehwNqJUa/Y5G/G8YcpGPHffI8C+CP42OucH/oYMxluPXYjvztI+B+p0HY92\naaySjTjH2YjNZBoAECVkVa40G7F/tWZBTMWlP+v0s1BRFEVRlEGgk4mKoiiKoiinG8bAjIyiUNoJ\nQKs0nyw2tYiOT1M24u5o+R0URVEUZcBs1tuJN+u4lMGgk4nLsH//ftx+++24/fbbcfgw3fbyohe9\nCP/lv/yXNT/WXXfdhS996Us4ePAgisUiLr74YrzsZS/DOeecM9A+FEXZXKyVvbdc/+tptAzqHOaj\nho4CrOC1l7MRV2oiLp2NKCaiGIhiJBZ9MhErHul/NTsOANiS0PL2Ihlxe6pkxF1cIxvthbuOAAB2\n7CYzb6qxHd9unwsv8lCPaduExypVmiX/rxJwtWbJRizlsxGLo3SMYIytyi001r5VmiUTscQGItuT\nCE8iG7HL1ZolG7HuVGuenKFjTZEFKNWa4wk6p840jVmyEettNhPZRGwl1Ha5SrPkR8q1uXTXY0hP\nnECNsxG799G1+KebyBD99gyd28NzFo816XqdsDRrO5VlI57gY00DAKKUx5hIS5mKYh6uZbXm/PaK\noiiKoiiDQycT+/Arv/IruOGGG3D06NEFj5VKpTWdTDxx4gR+/ud/Hl/4whcWPPbf/tt/wzvf+U68\n853vhOkbCL82fSiKsrlY7wm4fsdbiz9OBz32pcagf2yfnZzMJOKpTh76HhXnCH2ahJLJw5KhHMNh\nbAMAjMnkYUgTcucMUz/n8x3Ez91Ck2gX76OJqm6LHv9/B85FcdsIjnk1xIkBEqDk0aRTlW9nLjuF\nVSoyiSiFVbLbmelcvS00Zm+cJw1HeRDD1Fppq7x+LSYPWzQ5iCbf1jwrk4jcTlMrk4jJJI09mqT3\ncnuOC6o0eBKRb2du88SqtFJgJTB0bfbU6Jbj3ZfOAXPT8EodYKeXu50ZAO7hW5ofzW5pbmHCm6Qh\nmuMAgFZC91ZLgZVIJg9TmjzsfzuzFlhRFEVRFOX0RycT+3Dbbbfh6NGj2Lt3L5797GfjGc94Bn7j\nN35jzY/TbDZx5ZVX4o477oAxBq985StxxRVXoNFo4FOf+hTuvPNOvPvd70aj0cD73//+detDURRF\nUZRNSuDDGx9DZZgmI9Ha2OGczth2FzhxGIgjoFTc6OEoiqIoypJYC1i78V/SL8pmHZcyEHQysQ+/\n//u/j3379mHHjh0AgHa7vS6Tie9973txxx13AAD+z//5P3jNa16TPfaWt7wFP/MzP4O//uu/xu/9\n3u/hpS99KS6//PJ16UNRlM3Diq2+kzWNlwk4ORmjbzOYiP1QQ/Hsou9rsU9xldz2mYEovx6Rsbbs\nbcxsIhY8sveksMoQqLDKWEq3M2/1yf7bVSOzbh/Lfs8cIwPvKecezQ31e49uRzhew6HCdmCqd5tu\nYCwqTmGVKhdWqRbc25mpLYzw7cxb+JzG+HbmMSqkgtEat2RR2qGhXIvCSZiIABDx7bydDoyYiHUy\nBDHHJuJM3kRMp8nuSybpnLoz9Lw063TdG22aBGzw7cztmFopPiO3fJf42jxh7wnApqie3wZac+jc\nR6bjLTfR8/PtaXr+vl+nz4jDTbpmxywZolP+iex25nZKJqIUVpHbmfsVWAFoDCsusCKokagoiqIo\nyibGW36Ts5NnP/vZ2UTielGv1/GhD30IAHDVVVflJgEBwPd9fOxjH0OtRr/g//Zv//a69KEoiqIo\nyubC1GqonL8T4XgNm3iu/rTAtttIDh0GWnMbPRRFURRFUZQzAjUTN5Avf/nLaDTom+1rr7120W1G\nR0fx8pe/HP/7f/9v3HTTTZiZmcHIyMia9qEoysay7iZiv35OwVAc+JjXoFzc6WQoLnd9T4dzGBSr\nNREX7Gc89LIRl8tEpDbMjMS8iVgFZSGOpmS8bfPoi7wdVer33CE65lNHyF67bDeZiGW2B/c/Ng6v\nWsKjpXPgNwK0Ey6KwkMteGIj2oWFVQpk21UrtCzZiOGok404KiaiZCTS2O2oFFihZVQrOCVaZOzl\nbEQxEiUbcYaW0xnOGZyi67AgG7FJbbNL178Zi5G4eDbi3iHORnz8HOzsJPxCG9gG3Pc52v+24zsB\nAPdyNuLBedmIABbNR+yfjSiFVWjsa52NmD2s73lFUZSzFIM03ZzfKm7a26+VgaBm4gZy8803AwCM\nMXjBC17Qd7sXvehFAIAoivCNb3xjzftQFEVRFGWDKRZQ3LsNxd1b4Rf1u95TJW11YI89BjTURlQU\nRVEURVlr9LfVDeS73/0uAGDXrl0YGxvru90TnvCE7Od77rkHV1111Zr2oSjKxrC6arOLsdrvgxwT\nxu2/jxmzqjzE9aoYv8Kxng6sRb7kavs4E62mk85GXFCpOcyqNXuGzDXPyxuJoScmItl8JZ/s/oqh\nf3dH0i0AgK0gq297mSzAc4eo3ycOk632zO2Uuzc6SnbboWMjMGGAA+luhCihEXtAt5eNWOJcxJJP\nz1/F51zEMMZQlo3IVZqH6LHCKO3jj9I5eKOUB2gyI5EzEEfYRKyJkcjriydZlCQhuw8NNhEbZB9m\nlZrrdWCajEE7S+efTpHdF0/RmCUbsdVcOhuxk9J28qqWbMTH7+ZsxL0NoDWH9vfoGt30VTIR756h\n/r4/x9mILbp2JyxZh1M+PT+L5SMOOhsxe/gMfO8qiqIoinL6o5OJG8jBgwcBALt3715yuz179mQ/\nP/roo2vex3JMTU31fWx6enpVfSmKoiiKAsD3EW4fhT8yhLBe2ujRnBHYdgvpiQlgq954oyiKopwZ\nWGze79A367iUwaCTiRvI3BzdelOtVpfcbv7j9Xp9zftYjvHx8VVtryhKf5Y0yk7RRHT7Xmi0uPv3\nMRVX85tB3zGf6h/z6dIPn8xYN4jNUOm63xhOJ+tp7UzEhfmInpuJ6JiIRa7SXPbERKRMxC2WDMXt\nbPPtqVKG3+OHyU571jYy3LaMkK13eKIGeB7unbkAxbEK6tYHpoGYTcSiJwYiWW7lgN4HQ1ypeSjs\nVWquVMmqK3KVZn+EzsuXKs0j1GI4X63ZShaiGIkVXg5Pslpzl8Zk+HcLw7+XYIasPsxyTuJME+kU\n233TtE9ExZLRqa8sG7HD2Yi+oesk2YjnXDILOzMJ32sCo8B3vkDn9m8T9EXrfZKN2JBsRHo+JrwJ\nAMCcOUbH4VzExfIRF2YjsnnIGYkrzkYUtFqzoiiKoiinMTqZuIF0OvSHQLjML/CFQmHBPmvZh6Io\niqIo64xnEIwPIxirodha+gtAZeWkjTbs0YN0m3XN3+jhKIqiKIqinBXoZOIGUq1WMTs7u+zkXrvd\nzu2z1n0sx+TkZN/HpqenccEFF6yqP0U5G1iRjbZKE3H5jEVne7u03Wf7mYpLWX8rNBFP1sbrWTnL\nWJSn1Pf6shlMxJWyGatGrz5LdPUmIoCcjeh7ZBYWJBORqzOLiTickqE/no4CAHYU6Lbkc6rU9+OH\n6fX5jHEy3baNkDF3bHoIMAa3zlyC0pYh1BECs0DEl7XAJmKVsxHLvpiIZNBlJmKBbLgqV30uj0QI\npErzCOc8spGIWiXfDpNVKQZiZiKWeXv/JCfgJBuxThZflo04zWYiL1uxEac6iKc5G3E2n43Y7HBG\nImcjtthI7DrZiFU2Ni855wRgLYZ2TQOtOur3WAAe/umfKNLlu7PUn2QjHmnR70AnINmIZCK2Uopx\nmZ+NCGDRfETNRlQURVE2BLt5qybrv1xnNzqZuIEMDw/j8OHDmJmZWXK7+Y8PDw+veR/LsVRhF0VR\nFEVRFsEYBGNDCMaHUW5zcZNTnxNXANgWZyOO6p8xiqIoiqIoG4FOJm4gF154Ib73ve/hwIEDS243\n//ELL7xwzftQFOXUWbGNtqjZt3i+4II+zeLb9c0nzLZffAbDLDBqnO2XNCf7mIjLjnHp2RTXpuw7\ntmyHzZOdeDoZiStlPczFU3uvAGthIlLbsxHFRCx5lIEomYhjiWQikom4mzMRH1ej1+HTxqYBAFtr\nZOkdm62SiTh7EcpbhzCHEJgDkpTGWsgyEakVE1GMu8xELFJb5UrNlRotF3jyzB8L4I2yWZhlI/Jd\nBzUxEXl9mcZuy5KNyLEn3ipfr3wHhMmMRDYRZ9lEFCNxiuy+dFqMRDL4utMG7QYdu9Wi56Ye0bJk\nIi6Xjbjr/BnY6Ql4aRPYA9z1pW0AgDsm6fm7b5bO6VCDjnk0pjFOeXSHRR2UYdlMpgEAUUKPi5HY\nr1KztVGWkbhW2YjZw+p1KIqiKIpyGqLl7jaQJz/5yQCoWvIjjzzSd7u77rprwT5r2YeiKIqiKKcI\nm4jl83eisnMYJtBfsdaSdK5J2YitxkYPRVEURVEGirVmU/6HTXr7tTIY1EzcQK688kq8//3vBwD8\nwz/8A974xjcuut0//MM/AKDbjS+//PI170NRlNWz+ly3+ZysiZg3rxbiZp8lix4vM2xE7FpgAy4/\nCdLPCsseX6FFad1cR7kEvF6Os6yhuATrbf6ciUbiSlmTc19tDucCEzHklpb7mYhSoTnwqZ2fiyiZ\niK6JuKtC76lLuWLyE4YpW29rhay7w/Uq4Bnc2rwI5S1VNBAADSDml2fRMRArko3oVGmuBmQe1spk\nxC2o1DzGpt44V2oerQIjYiJSm2UhVmkby+cAKcC20mzElN8vEY3FSOZynSbxDLfIMhIlG5GMxWSK\n9pN8xPYsHbfVLKLRoedKDMRWks9GFGp8XS7YMQkkCYZGTwDtJqbupe3/+WGq0nzPLPW3v06fdUfa\nko1IWYgzPpuInI0oVZr7ZSNKDuJ8I5EvimYjKoqiKIqizEO/Nt9Anv/85+O8884DAPzhH/4hoiha\nsM13v/vdbCLw6quvRhDk53/Xog9FURRFUVaJ56GwZRjVC3aisr0G4+uvVGuNnasjefQxoN3c6KEo\niqIoiqIo89BZpXXk137t13Do0CFUq1V87GMfW/C47/v4nd/5HbzqVa/Cvffei2uvvRYf/ehHUSxS\njtOBAwfwile8Ammaolqt4rrrrluXPhRFWZ6TNxFXUOV4xQaiv+h60/d7IfqItwssPllOnMfZBlyq\nCrR7bCP/jOTH1n9MWPSYvQqp0rFs2M9QPINZtsr3mcIyhm6fLER5rYmBKK9B3zUR2UAM2Egs+WQd\nVgwVFaul1I6lw1l15l1cnflSrs58UY2su5EimYKP1aswgY8Hk90oDVfQSj2gDiT8siyy/FcVIzFY\nvEpzNavSTP1WK7Rc4mzEUCo1jzmVmke5kMvIECxnI6LCZmL5JE3EhA3mLpuIHTYRuQJyZiJyViJm\naNlO03I6Tdsl05KNyJWa61ypuU2/j7SiAE2p0pzQNu1UshGp63P5eu/cOwU7eYLGsBX4xlfJRPz3\nabIvHyTBEIebNOZjnH046VGV5jqoqnY7puJzEZuIUbqwSjOw0ERcLB+xbzaimoiKoijKOmIBpJv0\ndmKtK3d2o5OJfbj55pvxiU98IltO0zT32Kte9apsuVQq5bYVPv/5z+N73/seRkZGFp1MBIBrrrkG\n//Iv/4KPfvSjuP766/H3f//3eO5zn4tGo4FbbrkF3W4Xvu/jxhtvxDnnnLNufSiKoiiK0h9TCFHa\nNYpwuIK5dmGjh3PGkkzNwYaPbYqiSoqiKIqiKMri6GRiHx588EHccMMNiz720EMP4aGHHsqWq9Xq\nopOJK+WP/uiP8KQnPQm/8zu/gyNHjuBzn/tc9tjTn/50fPjDH8bznve8de9DUc5mVp39tsAeW8ZA\nzBl9y+ULLm35mb7G1uIszCWkZbFx+puKmPe4O4bFc+pM1ubHLn3KWLIWbAQ5WYhZVpljKPboU3l6\ngBMQJ50XeEaYh6u/pXf1maCugcivNTYPvcxIJPMt9MnMK3hk7EkmYgVkIA7bUQDAuK0BALaVqJ9z\nKj6eNEKvw31VMtxKPi3vb1QRVIt4uDCCwlAR3dRQJiK/zEo81NKC6sx5E1GqNFe4rVbIiCtV81Wa\nvRHOexylczFjnIM4whaiVGquDcGykYhSkS/XSZqILTERydpzMxIxJ0Zi3kRMZ2js0TSNvT1HnwmN\nJrWtLrXNmHMSEw+Rlc8FYrRAfZy3bRq220W1dASodHHwO/Qcfe3wdgDAfbO038EGZyN2aKwTXKV5\n1jtOx+RsxG7KVZrjfJXmFWUjAn3yEdVIVBRFURRFEXQysQ8vetGL8Bd/8Rcr2jYMw0XXf+ADH8DM\nzAwKhaUNBmMM3vSmN+GNb3wj7rrrLjz66KMoFou45JJLcPHFF69oDGvRh6IoiqIoAIyBP1xBbfs2\n+MUQrc4KJ+qUVWNTi2RyCnZmFjh3Ye6zoiiKopzVWAObbtIvojfruJSBoJOJfbjwwgtx4YUXnlIf\nP/ZjP7aq7X3fx7Oe9Sw861nPOuljrkUfinI20ctnW+0/hmLY9LOtls49XOwxs2Cf/LJr+7nr3X5c\nw9C1Ak1m5cT5x01+u1zfy1TMFUvMc8y1lMeSpjRZkGWWsamUmUIQWzLk9XlD0fStqLqQtTaDBmci\nDq6Qx5pWol5hBW/XPFz4us6biL5jIrpZiEU2EQuG2hq2AACGUzITt/pk8G0p0/4X1Oicz6vQa21X\nuZ0VMH64XUVprIpWlQqqJAmAJrJX0nImYlUyEbka8VCBqzOX2EQcovUFzmT0R/j9NCJVmjn3cIiN\nxFEy9OwQm4lSqbkyBBQW/yKzL116L5kWm4ZNak2TLL/MRKzzMhuJ6QybiNO0fzxLY++yLdhq0nu+\n2aHnqdGl6+xWao5Sk70Vzq/NAQB27DgBO3UC3akUKAM33bIPAPCdWerr4Tm6zkdalC15zFIG4oxP\nmYiNlKo1t9OlqzS72Yi9jFZeXkmlZjURFUVRFEVRMnQyUVEURVGUsxq/XEA4WsVYYQQwQLurlZnX\nE5umiI9Pw3pHeI1eb0VRFEVRlNMJnUxUFOWsYqFJuLIqzAv2X6bSbD+LcLG+V2oeynrPC7mXftvl\nb8m0mYVDlk0iFiBbgqmTH5ambOuYxczEfOXcgC2xAttfklMXGLKVxEiMLFlO3QVVVTvckjkkIlDP\nUBRzUUawTHbi/LGuUQXoFdt7KzYQl584OXkLcq0mZVbWz+J5nUtXHXfNw57lyi3ncQaO7RpKVWZD\nVZnLoNfakKXlMc5CHA9pvx0VGsdFNXr+d5XoNTXKuYWTUQGFkTIOVXcgCmifuMNnwJe/yKdQ8PIm\nYnkZE1EyEYuVPibiMJuINTYReVnMQwxxhWZpK2wqlvnxleQjim4pWYjNBrdiHta5ZSNxltbbOa7O\nPEMXI3VMxHZWpZnbSLIR6fnrcKXmjlOpeWupg307pmEbdYTJCWBninu/sw0AcNsJeg7vn6OND9bp\nuh3nzMMJj0zEuiETsZlMAwAiruIsnysnU6UZwElValYTUVEURRkUm7Um2SYdljIgdDJRUZSzHmMM\n9uzZg5GRYSRJgiNHjmJqamajh6UoyjrgFQKEY1WMlIdhPA/tyEcUL7+fcmrYKEFy9AjQagOjCyf/\nFUVRFEVRlNMHnUxUFOXshC0yzxhcdNFFGGIDCGGI88/bh23bmnjkkUcQdbny6QITke3APlWMXduq\nX47h/MeMyRtdXmYi5o0taX2xBBHm90d+/yyvkO2cBFyF1bEDY7F5vLzFY2264Lwkt64UjAIARs1u\nAMC59lwAQJXH3EholuaQOQoAmAnotsZWQlVXo34GXJprAB6zVH3um524BlWdT91EXNrqW7b/Je3C\nUzEGF2Npw61fP4tXGF/OqM2/jn3nPRJ4ZN+J1VrkDMQSqK2ygTic0vJoQNttKdL++4bouu4s0Wtj\nR6nTG6BnMF0YRmm0gkdD2i/hh2PbMxBZZkTR0OunxAZiJZBsxJVVZw5HaH9/hCtS1+jczLBkInJb\nkVayEPMGoi3z41LIbSVGopuNyFmIJstEZCNxmpYtG4npLL//Z+g9G83S9ew0lq7S3IppTFKpOeG3\nnhiJF49PIZmaw9athzD3aADAx03/StmI98xSH/vrXKWZK0mfAH0+zPhkIko2YiehbMQooe3cbMTV\nV2nuM6mpRqKiKIqySbAAUrs5C53YTTouZTDoZKKiKGc15513Xm8icR5DQ1VceukTcfzYJI4cOYIk\n1T8gFeV0wy8XUBipIBwuI+bbcaEW4sCIm11EjSOw3QjYqp+hiqIoiqIoZwo6magoylnBYlWbh4eH\nMTo6svgO1sIA2L5jG7Zt34rp6VlMTU2j0WgjSZKckeh5HsrlEsrlEorFEjzPIE0tOp0uWs0Y3W43\nM/kk/40OwVWVF2QlkunjmodibIWG7KWirfDjbHbZILfsW+pHqjNHbPd1DFk9TZ9u5e56nDtm86Zi\nOi9nrFdZl8Yg2YhjZi8A4An+Hh4zXd9WmsJ4BrsLZFZtSWi7+7u0POGz7WQoC63vBA+LQ4mbdbag\nurOTnWhMZhedcsXik83VXPDAyqod0yH7bbu4mbZSg7D/9v0qLueXFzcSqRXjMHutGMdE5NeOvI5d\nA1FezxVLy8MpTfLXfLLyRthA3FOl/neW6bneXiSrrca5hZNJCcXRMqarQ/ALATpckTm2ck2IEl/K\nimdR4kzEkseZiIGTjcgG4lDI1Zkr+erM4TDt743wOQ/TmDMTMctCFPOQ1tsSP16ia2E57xFFbgP+\nNW0pI5FNRLDVZ9qckSgmYpaNyKbiDFdpnnaqNHPl5PYcHavd4urMHTEQaSxtyUbkTMR2Is837b+z\n0gaSFHur34cN5+CFtP7fbtuFu6boub2fBEM81uQqzQlVd572yECsY4KPSYbiulZpph2wFGojKoqi\nKIqi5NHJREVRzkqMMdi7d8+Ktx0fH8P4+BiM8RFFMZKYpiSCsIhCGM7b1sv2AahYSrfTxcxkGxMT\n02t7EpsQP/Cwde8ovCpdk7AVY+rILNDUjDRlHfEMQjYQ05AmIbvxCm4JVtacZKaB7okZ2H1zGz0U\nRVEURTn9sZv3dmL9qu3sRicTFUU5o+lniW0ZH0dRcsgW3VH2k9zA3kNBYBCGMlFhkdruwuNxflia\nJvB8YOvOGsa2ljF1oo32NFlIBTYMEzYAxfry2eQKLZuIYIPLksU0lNJ+khlX9GQ/OrbfR2qTsXVT\nOqdDHTKUZjwyFJse/fHf4Ta2bCgizfIXxYocBlVhvcDsAgC0kxRBwcfchZOYCWYQp2Q91Uo7kOxJ\nMXYgRNxNcK6/hc6ZK0ZbjzMp5V8jx1AUe9NjuzLN1kdyUtQsMBSxaDXq1bFC83CFxuFCG9Dv+3g/\nA3C1x+hfUXlp87BffmcvOzNcsCymobx+A+Rf5wVLdp68jqspLdc8XmYLb7RAfZ5Toeu8pUhvvrGQ\nns9htgNbaYBwqIhGaQyFWhGHUg+wvSzEiJ9+MRCL3BZMvjJzybco8+uwzNWZKwFnIRapLZepLVb7\nmIijdK5GshCHOTphiI1EMRElA7HIJmLAX0TIFxL8Xs5aeZ4Sfp+kaa/t0HXITMQWveeQGYncsolo\nZ9jqm+lym6/S3GrS52Grw21E5+ZWae5aeU3wqYZ0TS4YOY7k+ARGds8Cu4Gj99M1uPnRnQCA++Y8\nHJBsxA6NZcKbBADMesfpmOkUH0uyEWk7+TxZtkqzzX+AnEyV5vx+iqIoiqIoymLoZKKiKGcl27dv\nH/gx/SDA7j07YbdW8Oj+o+ue3eYHXna7bxKvrxlojMGu88dxIFhoI/mBhx3nj+PwgyekjoqinBzG\nIKyVEAyVUaxWAGMQRWogbiTWWnRPzCGePbTRQ1EURVEURVEGhE4mKopyRrLQHpNlD0NDQyiVigv2\nWRwxtvL9WcnjM2wMuZaZa4CxORMnHRTKIc69eBSNwwXMTDew1ZLdV2Jjy+NjFdkOq3K+YImVwwKX\nSZVqqbHvo1gJYYo+CqUQCH0EBT9n16RJirQdoz7VQrXZhbXAU0o1AMDRFpljx7ujAIBZQxlrkq2Y\nIILH51fiXLvtoG0LAY3pyK7jMOUuWslM7jrM4DAA4CDKmBlPsGOS/tmZa4zT2A1XX+WsOusnWIxs\n3nVBlWfOSOM1Jle19RQnmU7ROOxnBS7cb+H6UzUK3cdds3CBYbhMXme2XvI5Db1WxZ4NUETBktFW\nSGldFbRNxaM+qgH1UebXzJYSvYB3S5wg5xSOsOk2xJZgPQ0QVotol4dQqBUxyVmgMRuIXX7KA34/\niIFYDSQHMV+ZWdosDzGIUOZjlotkvhVLtFyo0BgCigiFzyaiGe5jIkrrVGO2RTES2YYW89B3fg2z\n88xDAGCDFxHbyzHPxnc6PROxQfYe6lK9mZazKs0zdKGSGTqXaIYuVLvBBmKLqzNHTjYim4hSpTlK\n5bVG1/Pc4TrSegu7xr8PjCSI69Tvzd+gfNRvT9M5P8RZjIdbHRyz9Pkw5R+jY7GJ2EqmAfTPRjzl\nKs1qIiqKoiinKRZGqzkrmxKdTFQU5axj+/ZtGz0EeL6HPfu2Y2i4js5Bsypz0BigPFzC0HARpVoR\nsZE/9nnCcpG/iz3fQ3GoiPJQESUA00fnYOfaa3EqKJRDjG2pLbvdyHgVYTNC1NZyusrSmMBHOFoi\nC7FQBoxBGsvE/MaOTQGSKEb02HGkzTYwuvgXAIqiKIqiKMqZi04mKopyRtC/gm7PSASAIPAwOjrc\ne1yMlb4Ve1d4fOc4C9bPM8KspT++G+kkwhFgfHg7pibmEBwPEXXiLAux4ovhRl2kpQC18TK8oRDW\nM5i0CRB10OV7hzPLj23JlGddUrZ0wpQNR1sCtgQYrY7gxMEZPI5tq60dqrR6tElGUSPh6s82yWxJ\nscy2l6nd32zhnL1D6FoyjTwx4yTrkK+HVGndtm0vph+ewL4KmVtRgzIULZuJIhNmhp0z75jIdZTM\nRTGVjGRbzq/2fLKTHEtXTF4+z3ClFqHXZ72/rFFoHPuxn1ko20mOoZiFC9dzyxXBJaczZNtQXjsF\n3r/A+1U8Wq74PopszpZYERwrUHsOV10eL4gZSM9ZlS1UjzMMm0EZ4VAJ7VIFQSnEZEr7x1026RYY\niJaPTcuhU5G55IuRKHmIca6tFKgtlSIUyvRzUKV9/CG+/kNclVnaYdEonbZKRqKtkrlrS7y+4GQi\n9iPlz6GET7JLNqGRSs0drtQsNmKrvdBErNNjdlZMRDqniGXhboOeq5ZTpbnNhWraSb5Kc5TmDehd\n5RZsanHu8COws7MIdtBY77+b3sO3nhij5Vna4WCDjn80IttwwptA3dDnQFNMxJizEVPORkz4HPh9\nLWbi8lWa1URUFEVRFEUZJDqZqCjKWcXo6OhGD2EBnmewZdswxraMo9uOUOgAUZSgZDz4gY9C0Udp\nqIgO/wHcStbG7KuMlHBOOURwfBpx9+Qm3iq1Iqq1Eo6vcPvSUAGloSLQ7i6/sXJGY3wPQbWIcKiI\noFqED5rkimN3clbZDMT1FqLj07Dnziy/saIoiqIoa8ZmvZ14me/ulDMcnUxUFOW0pK+JmG2QNwVl\n+9HRMV6XOtu5OFmJbn/Zcr9cvny/ZpHjSBXnOZ6K6/gtoApUK8P8eAGk5sVA0kGS5QPaRfvsZ9sE\nYrHxmMRgPMoVVXfsHMfR70/gycO0fluJjKVjLclOs/D4UCNsm7ViuiTVPUU00ELAZltWQZm3l6xF\nMeImoy6i0QDbp+jcd0VscnXJbjJSmZoNSI/z9XzO4RODKU1p/16GmphKjqHo/LwSljMKF6zvm2+4\ndEXkfrmFvgmzdbLPgmW+3mKCyr6BzZuGIU/QZYahlfWcfZhV6eY8To/6EytW8g1LrKcV+OVe9vOV\nls8pxahxleXQ5I3AIrfNNIRfLqBVHEZQKaAbFBEDmEo8oAs0E+oz4ZdxyJe1wMbhiCxz1mGR1xc9\nx0Bk87HEmYulkCsycw5iMbMQU24NTJVOLDMRq/x6rnC26hC9TlGV6sxOJiIvS5XmZU1EQao0d7ky\nc2eeeYh5JmKTcxGzfMQWbJ0MRDvHVddn6bziWboenTobh5KJ6FRpbrGJ2OXrLlWafX7+aiGN7YKx\no0gnJjF8QR24AJh9kPr9h29TNuI9s9T/I3N0PY9ydenjoEnHac5HbKZTWZXmftmIqZWMxMWrNC/8\njFuZkagmoqIoiqIoytqik4mKopw1+L6PWm35bL+zDS/wsOP8LfCnTiBZhaE4vH0I9cLqLclqrYSg\nGSHuaHbiGY1n4JeLCId8+JUivKAMYwxSvq32pO9CVwZDatE5MYukoVWaFUVRFEVRlDw6magoyqZl\nWfswt7G77cLqtiOjY32zDfseexkj0e1vQR6eux4my0/0nPNLLBlGdVCF08DjSrlZxdy8BdnPtZGs\nROnf4+q3gZUKvWIL0jimOmQD1bZtxbGHTuB5Y2RDTZRp++nIy3LTpL2n6aO4pYpCm3LbSoZsyq5p\n8tgsH5OspaIt8zmSSTS2vYapg9O4WKrjzlJeY9iRTD6yqGZ9OveORxWmI0v9xylbWY6JaLNstf5m\nYurYTF6f10J/83DpvMIFBqKTT5gZiJJT6OQWhraYmZxuZmGQPXf8nGY2o/TJlcDZMJQK4GIYlgN5\nnM6FVyNk9bTGFZC3Fuka7S7TdR5mu0/yDcVeK2S5h0DHFOCVCmgENfilAtphGQmA6cQAKdBs0cE6\nSf51L9mHVW7lGIXMOJRlMRLz2YuZgchZiKUijVUMxFAMxCE6rqlKHiK9Jk0lBKpcbbnE1ZbLbBhm\n5qFUY6b1WXXmEreFFZqIQtSrygwAptnMtX0rNM+zETMTsUHXJ5qj88pMxDaNVUzEdsKmIk/mxhAT\nVLIRqZ8LR2eRztaxY99h4NwECRuH/3YLVZ2/c4reqw+SZIjHmvT5cTyWbEQyreuYoOPH9HkWJY0F\n2YhpZiSubZVmNREVRVGUM4nV3WMzOPRf27MbnUxUFOWsYWxsdKOHsKkJQh/bzhsH5g73CkIsghd4\n2H7e+Oomex3KIyXMHvOBWPW00xLfg1cMEVR8eOUCgnIBHt9S3eXJKzUPTz/iVhfR3BHYThfYq0+g\noiiKoiiKsjg6magoyqZhoQ3ILGaf9DERew+LPRZwFylqtWEY/g7NNVdO1kTs7e9lP+W2c+w1zwuy\nY8nYpPUkx86QUSRGYT9zTkj7fC9oJAPNsdkK2XHESKLtDrViAAa10R2YOjCFK7bO0XZeimnOW7u7\nUcHo7nF4sY8UQInH3mXzUMaaVZC28ypIAyh5ktcG+ONDuKA5waOl9aUGZdNV2rTfUEJVchuGjKY2\nm4+JLwYTXwPOakyzXMkUqc1PhmQW4zKzXJl56Fx3N58wy4O0QW6597jkGAa57XwxRXl7yS8scv8l\nL0BBKiOzCiptwAah5AmKKSrL8pwOsWE4WqB2Z4mu17YimW1VNg1Dzh80jnEYsnFoDADfQ+yXYYoh\nmqYKr1RAy5RhAUwmdOC5ho8G/9zuYx5KlmIhkGPx42waFni7opFlMRA5e5HNQ2lLfIt9gbMQw7IY\niNSvl1VkpteeVGTOLMQKt+ViZiBK1qEtsZnIxqEN2Dws8PqAf33yVjihLpPzblXmJtl8piEGYoNb\nfnyWM0K5QnM6x5Xb5yziBl/vBldl5urMzShfpbnFRmLMnwddrtYc8nXfV6vDRjF2n3MAaDXg12j7\n/d+g9943jmwDANw3S+d8sEHPxxE+lxNmEgAww0Ziy5KJmOUjsrGYpN1Tz0ZUE1FRFEVRFGVToJOJ\ninKacbI22Gb8Y2vBufQrhpLdqryY5O8WSpFJQJmooz+sq9UCfN+HTWNn+363R8v+i9/G3Js0dCcx\nTW67bHnepKLPt/BKGxqayAjldma+tVUmndzrJM+lNYvfvutOPhq+jVEmr2TCKeRbYT1elr0OJgHi\n7eO4ZbaATr2LvZUEiTUo1Ioo7RtH1zfwYpvro5gUcseyMnEkk4l8Pctc2CMFUB4towofaSfGC7bR\nZMq3QpqUPNSk/iZ5gqQe0cRGN82fs0yuyS3dMrGa2N4rPnZulZQe+k3CCtlt4rws5+Zlk7CLL/te\nfj9Z704EFrKJQlqWoiYjBYvRkEY5ykUwRkJ63VZlQo0n2GTCzeeJIZkMlOWQ9w/8/ARqzBNMnS5P\n7kYB4Bk0vSq8QoCGqcAvBmj4JZjAQz32AAu0YwNEvbmxwrwiKTJZGPaZLAw9m2uzSURPblOWSUNu\n+ZwLPGkYFmh9WOFz5Zoo2W3LFX4NSjskxVO4OEpWPIVvXZZblEtF2JAnGkP+tciXVmZt+xVa6sNy\nhVWkoIpMHs7xZOIstakUVZmmybakTucc1XlCsBWgzRPuzW5+8rDNhVXilJ6ADk8iyiTtrjId+/yL\npgBrUdgeAc1ZNL5FY7nl5t0AgHtm6fp9nxIGcLhJYzqW0JcM094JOgW+nbmdUMGVDj++oLhK2tXb\nmRVFURRllVhs3mrObsFJ5exCJxMVRdl0lMtl7NixA57nYf/+/UiSU/+DcXhYC6+shiD0Mb5vHDa1\nGDFdmGIIYwxa7bX9paG8tYbGY1Nr2qeyBMYAgQ9TKsGEAQKUYAohrCnC+D5sTBNTUcyT6fHSVqxy\nGtOow85MAWxuKoqiKIqiKMpK0clERdnknEou3VL9bCqjY54dGAQBLrrwQoRsCBUvvhj3P/AQkiTJ\nbj+1sItYjWISsqXEtwqLYVirjQDwAdPnluDMIJSPxXzxDM9zbn01i0+yLDAT5ZbX7LZrv1dcg41E\nKU6SmYnI3w4r5yrPWWLIekrkNl4j1lm+sIt7e3PAYy7wrcZFL2/WCSlbQA9ygYf9xkeJJxGlKEYi\nRV7EcpTzk29OxVzjY5Z9MRNlDPR4vVTFTKGDF+69HwAwVhwFAHxvhu5VPcYFWeZ4ckuKd8itsxdW\nYx4H+JpwkY/UoMtmVsSSU2TzyyI7ZaYiL7t3rophJ+vdYjTZrcHOcpDdgixGXups7xQWyW7rjbNt\nA7H3gqVvzU741tVuEsAEProowIQ+WqYEL/AxZ0rwQx8NLvLSSQ3QAV2jFuDO14fzjEMAWekfKZIi\nt0X3LEOb/Rw459U7v7x5GHIBlaJjHhb4dmW/xMcu83sqax0DkVtU2ESU25jL9L6ymZHIy0W5hXne\nLcsrvV3ZRQzEmCuTc2EV02XzrkV2nmmzmdhwCqzwbcx2jh5PZthInGUTcY7G1eWiKmIjtqMgK6wi\nty13+DbziN8DAb8JtxSoz4suoNuRS48vAI1pxA9NAqPAv3+aFM9/n9oJALifC7kcrNO5He3SGCc8\n2n/OIxOxmU7w8UldlNuZ47R/cRW9nVlRFEVRFOXMQCcTFUXZVJx//vnZRCIAlMsl7NmzG/v3Hzjp\nPj3PQ7VaWYvhKevA0M4RmlDuM4GgAPA9GN+HCXz+ubdsAh+pF8IEPhKZ/OaJpZRveQ2kKEq8IaNX\nNgE2imCnp4Bzihs9FEVRFEVRVooF0k16m/NmHZcyGHQyUVE2GWtlIq7mOIO2PRaeI018DA1VUKsN\nLZhUGh8fxaFDj2Xyz/xEul5mIVt4HucNer1b94aHa/C8pT/ujFtcw3OKoxjJFwz6nAMh17JXZEX6\n6+Uk+pzjGHCeYMGKoUh/5EuBFDfLUNy0mI3EGNLG/HiSO7bHhpL0V5R8wyyrTwoxyFipfzH0SvMq\nOvvO6ZrMfiQKcn2cxwu+HJNz21jXK/KG1gJe4OO4ORfRsSk87RqymS742n4AwIljZChOtegatTnr\nb0eVDK+Dc/T4NBedqHkLDb7ESp5ifjlb3+fl75qK0prMysxvL6ahmW+/eoYsP8/AeIbMVc+nnz0D\n43tIjA/jG7Tgw/ge5qyHyAvg+R66oIlDyb/jqEoqgp0AaYevozN2GZrYkUFWoEXGSMtieAryfLr5\nhnJuwRL2oZiHmU0pJqIUSpHMwyJvV3KyD8v8Xinze6wccsvv3SIXQREDschGYpmXJQtRiqmwgWjL\nfADJRTxZCxFYmIXILTr0RJh2J7cMyUZs5guq2LoUVBETkfqN5rj7Bpu4bOa2slxEthFjD92UC6uk\n8v6l56TGuZoX7SVzsPrMKtCahZ2eA84J8Njf0lhufexcAMB9c9T3/jrtf6RFYzphqYDKlE/ZiE0u\nrNJKqO0mbCIuko0IYPF8RM1GVBRFURRFOSPQyURFUTYN27dvX3S9MQbbt2/HoUPHTqrfWq16KsNS\nBkAwXEXa6gKYWd1+QyX45QLKnPUnRT3m404mpshPJtrFJoaNyaYvPM8AhiZ3jeGJOGPgGb6t3aP1\ngQFPGCJbBwD+MhMhiUz+8u2qNOfCk+WpfuOrnCTWws5MA9NzWDjdrCiKoiiKoignj04mKsomoa+R\n2K/C8UpZwa2jp2pDrtQaWap6cxiGGBmu9R3vtm1bceTICaRpCmN6t+llpiC3YiaGAU0gJmkHIyOj\nmU3mVl+ePzranyalPG7FIhRT0eNlg3x1V8tWYL9rEXBWXWAKCJysRDESS0YmxPJVlwXJtYvZOJSs\ntKzKsFPFucDHDLOsRGrFSKyIJcin4tarlUmu1M47Ns/ISa5iyH1aqSRt+Xngocsxy4EcG7yfnAtt\n+N2ZGlCs4fy7/w3odDDyjv8AABgRHXX/ITrOIbKiHv4bwKuWcV9KlWdb0q/p9S82nlyV5YQ011Ds\n96q283+wWLzI+Clg0DML+41ZjEPZ0N1OlgOztFnoXht5vGDELpRMRHoeAl+ed7YMxUIMk6xydMgm\nYlDkzEM3+5CrLxs2Ck2J31OSfciZiJl5KEaiZB2W2Dgs8PoCbbcgC1EePxXkRRGxidjizEMxEVv5\nKs1oiZHIy3U2ERtk52UmIucRxiT/odvk6tptev+0O46JmMh7nrZL5t1WVOYMygt2UaZh7Xk1oF0H\nkg5gi6h/hb6EufUeqdJMnzsPc5XmI016vo5FNNYpj95js+Y4nZKYiE42opiImZFoqRUTcel8RM1G\nVBRFUZSVYGE2bzVn/ef6rEbLNCqKsinYunVrVrxkMTzPw9jY2Kr79X0fpbJmhJ0WGMDbvhMolZfd\n1KuWEe7aOoBBKcrpg23UgenDQHM6u6VYURRFURRFUdYaNRMVZY1xK+8ut93CB/pNqK107t/5A9Lt\nbx2KXKzabMwqHveqM4+NjS6728jIKCYmptg+zGcbipFY8MlILPrD1A4lCEwR1hNzcPE/sN1sxMAr\ncr/cGuo/4NZzPL6Un2/LFk7qHMfn/gMUs4zE0FJfRTYSpeJxiW0+X6otcx8xP3cR3/rqS6ZilqeX\nH1NWvZlzCwtZViK1Fd48i6TrBR7SOcyzEbt8Ou3stOR1zkt8LHl58SlkFqQYiWUfuWUx5OQb16/f\nsxvAbjz/+u/Azkyj8oZn0eOPvxAAkNxxC1AbxeTIPqAJbCvSgOpx/v3hmfnVlyXHkh9DHvfVu9w7\nZKVTNCt9py2WIOo5Y16QWemYhdm+vN53zUQxDp2K0oGRqtFiLuarR0vuYSAVmEPen5fFPvSKFj7P\n2WfZh/wk97IPnczDspN5WMpnIVrJOHQNxICXQ26DQE4ea4ZUY+YqzKbV4FaqMjtZiNI2qLV1tvWy\nlq5bzDZg1OIKzG3JRsybiJINGrN9LFXK5f1V9mPs2zkNAKj9h1EgapGOm1YQfZ3yRu/6F4qN+O7M\nBQCAB+vUx2MNGssJtisnQEGNMz5lLDZSykhsp6RN9stGlM86WZZUV1m/ZD6iGomKoiiKoiinNTqZ\nqCjKhlMoFFAqlQC7sHjGfIaHa0vai4tRG9K8xNMRMzwCVIeAsAakEeAVAS8AduwF+t6qrihnF7bT\nBuaOAXEXGNGK9YqiKIpyJtKvWOBGo/dAnN3oZKKirBMrtvUWTI4tPlGy0v5sX4Mx7XO8NWCltqNx\n3DCeFBoZHoGBgV3GyvR9HyMjo2g1vMxEFIMw8OnW2Iq/BQBQNFTld2TEg+8XM+PFZrZM/p8/qeIs\nmYliJBYM/YEeGuo/4LxGz0r1YtpPTMSUDcgEUsmUKyyL8WhDhJyRWBQzkTMSS1m+IF2H0DGtpHBI\nxAGGQcJmIl+3KHVtSK6o7OWrN4uJWMlay8fPVyMWWzBKDTpsP0qx5F6mX28boGfjSaaf5DHKsaqZ\nmZjyOeZNOjmDf7nnHADAtt+ngizDxWkAQCsa57HRdqOc01fm/lLbq0wtOZnSt5tDaBwLarVvjeVe\n9osWdlmEXp4nLc83E33kn5PsXBZUlnZMRs8xDtlADOW6O8ahz1mIAV9PP+RlNj/57QCvwEco8euh\nwPZhMciebFPkJ1uMQ8k8zAxErrYs1ZdL+YxDGzqZh/46mIcuEZuIbB6aNll4pskZia6JWJdlyUCU\nlvMCG8uYiF1u2USM2ESUbMRekSB+z/r0eXLuOdMAgKHnVoHhESApI73rYQDA9/6Ujn335Hm0PEd9\nH2pKlWYyCCcTOqcpjzIW62ATMWETcZlsxMy+XqxaM6BGoqIoiqIoylmA6h2Komw4IyPDK952dHRk\nxdsGgY9iSfMSFUU5Q+h2kB47CswdB5Lu8tsriqIoiqIoyjqgZqKirDX9MgpXmIWYGYh9b+V017uG\nnRw3v76/sbgGmJVK7lzF1fi55drwMF0f2+9aZL4VRkZGcfxIjGpIxTekunLJ0ITkuN0FAOigjZGh\nKkJD1oxhpUtMQbFqsmPx9fa50nLIBmLBI8NRKi8XUno84KrFrpkYmzjXppD8MGoChAitVFnmLENP\nqirn24JTZVmucmSk8nTeFowTsZnEYuP+PKnmDF6mtsQ2mlh9pSw3T8xEPl5qEHB2m5iI0srYYqcU\ntFRVlozEIbYfKwEdqyqGnJPZJ0+1z+uLvjxfPPaAKwWzeSe2pVS4Tua9hnpmos0te97SeYPLCYWe\nY1Gl/XZYZWiiN88y9L28cSjXA5KV6Lw1PL4emcHo589VMg7FOPQLfE1Y/hPz0HCZbVPitsDV0ktB\nbhmLtfJz2Kf6cqmQW2+LbChyFiJ8t574OuFWaG63e9WZXROxzstsIKJOZmLPROT3eZOuP8cLZtWZ\now617a4YiGwkSiZikjcQ5SVT4df5nj3TtPy8MaA9B+ymTFF85yEc/MD3AQDfProNAHDfHF3XR3kM\nUqX5RERjnjFkHs74kok4w+0sj4XOOUrz2YhpVq05byIuXa0ZWHDz0yJWohqJiqIoirIMFpu4mvMm\nHZcyEHQyUVGUDWVoaAiet/KJziAIUKtVkbaX33ZkrIoY0yc/OEVRlI2k20U6MwXU4+W3VRRFURRF\nUZQBoZOJirKGkEnnuSuX2Hb+CtlP7D13gm05a0cqaYrBJPun+WHYlVqEi42tXx8rNIqMnFuRWw+j\no1vQO2d3B1kvlZbJeNq+bQcahyjLsGTJHBxNRwEAW7ma8wH/GGq1Kuogy0aqKcdWLBup7szGFn8c\nStVmyUgsWuqvzGZiyUqWoph6UmWVDJsYUnGZ/viPJDtRjDHrI+RjSVZige26nplIZ9+reAweKxHx\n0yFGooyhy8dIpKIyb18M8qajGIpiDxbZXpMcw8AxTQPPg8myEvPPVcA/iOwlxlyB+6ywITfEZpy0\nYl6JaRiyWSeGXeCLYZfP9BNSMbqk2q1kzc3bbDl7L6uILBGeJn+MLJfQWQ93WXC+ne33LbIrK2f9\nm55NyC8NGDE2xTSUt9ri8aO9VvaXkEXOOszMw1DyDXlDd7kolZTFPAzzy4HThkH2s5WMxEAyEMVI\n5FaW1ysDMStDzhNwCbWmy7cFSys2YqcLNMRAdKsyd7iVqsx5EzHm3aJW3kTsRnQtupKFGOerM7tB\n6kMF6n/PBWQJFn9gJ9CZA865iDa45yEAwIn3/TsA4O5Ht+Oe2TEAwIEmXccjzXyV5ilQ9uEMZyM2\nMAWgl40Yp00e28qyEZev1iyIsdjfOlQjUVEURVEU5fRGJxMVRdlQhk6i2vLISA3Nw2k2gbcYtVGt\nbKooymlGpw07Ow00w40eiaIoiqIomwCLJSJ1NpiVFhtUzkwGMpkYRRHuuOMO3Hzzzbjjjjtw7Ngx\nHD9+HO12G9u2bcO2bdtw8cUX44orrsALXvACbN26dRDDUpR1oZcHyCwwBQXXQPRzy6avobg4WZVi\ntsoyQ5Ftkmw7N8dqEZY95oJYyKX7zM6JzUKP7T9jDIaqI4CRLC7XDGObClxh2ecqyH4V59e2YXaq\niZ0+ZSVurbJZKDmCo6MIbJBVdU74GD4KueVerheNMeBjlUD7iZFYBbVlX6zCfHZgyqaNGEfdlE2l\nlLbPngUDhEYyDKWlPgIn01DawHk6xFR0qxN7WQVYfjwvpWUmYpD1m68QLJWD5fEsY9D2foXJ0iu5\n85KXNxOlT8lfrLJZOBRSztpQkdpygdpikZ8XzlLMsvzEyAvz5yJkLxW+sKlcYLEBje2Zf/m3WG99\nPrpzYbFx415oLI5s574P3LdFv7lvt3/f9MxCNgYzfTIzD2XZ1S+RW29kv1Ba0V37ZB9meYeF/LJY\nhyFvJ/mGfq8/667zHXtxrU1E10DssmnY5erCkZiHssxtm/MP22whtrpAgw1Ezka0DdpWTMSkwdZv\ni6uXtxc3EbOqzDEbs/yExFINnYc+WqLj7Hwi2YHB8/cBUQN265Npg3seAABM//a/0OL+HQCAe2fP\nBQA80vBxWEzENpmEU2waTnvTAIA6Jug0LWUjdjnQsZeJ2OF2hdmIK6nWTDugH2okKoqiKIqinBms\n62Tit771LfzxH/8xbrzxRtTr9UW32b9/PwDg7//+7/GHf/iHMMbgec97Hn7hF34BP/VTP4ViUSux\nKsqZSqVShjHmpP68HBmvYnaquehjYSlAuaLflCmKsrmxzTpQnwE6oxs9FEVRFEVRFEVZMesymXjL\nLbfgHe94B775zW/m1nueh3379mF8fBxbtmxBsVjExMQEJicn8dhjj2Fubg7WWnzjG9/AN77xDbzl\nLW/BW97yFvzKr/wKSqXSegxVUdYcyQPMlKlsTitvLHqc4WecrETP4/Vulec+tmBmJCJvJPbafHC/\nXaLy8mptSHcMC/rrc26BR+/n0dFxBEEpy7tzrcmswrJPllSB8xCL3jC21UYQjyR4HGt0uyu07zeP\nJdi5ZxQFtmo8kLmYcEZixNWdE3A+mMlfn8DysSyNUTISxUissuElVYQDZ84y5pnRDp9TlEi1YTYX\n0XslFFgJDKXly5VVXeacvNDJ6ItN3hN0BToxE0Xckn4DV2bLeslnCJp5VYWFgmQa8mNiVea9zl51\n5hJnI1YDut5VMRLLbCRW2Ugs8bH4I76X6Se6JC/LATILkCtO93k5G2/etr2AR17M9+3afNl1zZTP\nPipo7mCLDEaegAWRcs4Kt3/f772wJCQx00d9Zx+34rn0yWPy3VYyEt3MQ8k3XCQLEYDl/MPe8d2Q\nxkXWrbWJKGYhm4eGzcMs+7DtZiLmsxHRctY32T5sdmFbbOM1JAvRNRHZPOTA0sgxEeV9LlKeVGcO\n2czdOkQW5PjTACCF99yLgLgFO3we7fCd+wAAs//r2wCA+x6hCs33zp4PADjA1aHFRjze7mIy5irN\nHpmHdX+aTjOlbMROSl/ixmwiRkm+SvPy2YhrZyTm91MURVEUZTUs80/shrFJh6UMiDWdTHzwwQfx\ntre9DZ/73OeydZdddhle8pKX4LnPfS6e/exnY2hoaNF90zTFd7/7Xdx666346le/is9//vM4ceIE\nrrvuOnz84x/H7/7u7+Knf/qn13K4iqJsMEPVU8s13LZnBOboXO4f2MpICaWhQjbhoCiKshmwSQLU\nZ4HWHBDv2ujhKIqiKIqinHb8xV/8BX7zN38zW/7MZz6Dpz3taUvu0+l0cMMNN+Dzn/889u/fD8/z\ncMEFF+Cnfuqn8PKXvxyefCG+zn2caazpZOJP//RP44477sDY2Bje8IY34NWvfjWe8IQnrGhfz/Pw\n5Cc/GU9+8pPx+te/HnNzc/jMZz6Dj33sY7jtttvwMz/zM7jkkktw2WWXreWQFWVtMQa+t7RFa7Iq\nwFyh2DERPY9zBR1L0LUFXQMxZUskTTnnKluOc8uLjqnPsVw70sXt0zUUM7PQI7sv9Ch3UAzD8ZGd\n8H0PEVdcTpG3BOX4oc/7eTUAQBk1FIyPQsHHzr3DmD08g8MtD2EpALYVMdHt9Com25D75mrLjqEY\nIz/pGPD2YiRWfVqusNY3xG2JPz2LziWK+RJ02CTrSIYa64IJbFbJWwxEyTSU5SzTkPsMvfz3fv6C\nrwHzBpiMQSZZRUrLZDfHRJTwZKk+3Ot+oaEoolyB7SWxGKUCdIGzDytsJJZLZD+Vyly1ucrZiFx3\nx5T5dc9VhI2UnPbzF8ME8ww4GhQvOxZc9nwYIHD6Mo5pmOUQOk9iZkH2ySU0zv79SJ0nwv1a2S2B\nPb//LIvQzUzscy4u7udFdg3cF5mTiZiZi2H+eP465R7OJ2GbO+Hrxiaiifk9mhmIeSMxMw1b7jK3\nbf5MbLGJKBZiK+FlC5b2ELfZJJZqzF02D2MxjTkDNXXfc/z5wFWZR8epw+rTK0ASAZdfAqSd3nW8\n+14AwMwNBwAA3ztAJuL32ETc36T+jvK4TvA5TLKNOW3mMOeTgdjKMhHJROyykRglbR5rPhvRNRF7\n/5bIZ2E/ExH5xwU1EhVFURRl3bAAUruOv3+dAoM2Jg8fPow3v/nNmJ6ezta1JQO7Dw899BB+8id/\nEt/5zndy6++66y585jOfwf/6X/8Ln/3sZ7F9+/Z17eNMZE2nT0dGRvDud78b3//+9/G+971vxROJ\ni1Gr1fDa174Wt956K774xS/isssuW7Jyq6IopxelUgG+f+ofQZWxCrZfsh1je0ex7cKt8AN/+Z0U\nRVHWGdtsArPHgOkjNJGoKIqiKIqinDRvfOMbMT09jW3btq1o++npafzIj/wIvvOd76BUKuE973kP\n7rzzTtx6661429veBs/z8I1vfAM//uM/jiha/K62tejjTGVNzcQvf/nLKBQKa9klAOBHf/RH8aM/\n+qNn3ZOjnI54KIYjiz6SGYlOfqDH5WoDzlqU5Z6ZyMaWM/cvVmDCNknKbW85zq13DUZvXn/uWORY\nvpTS7UOa5TQmiz4u+4ce3c48hC0AgBKqGB+uocKKXcfMAuhZgmLOGLY35dqUPMo/LKWVzFB7eDbh\nPgJgLsYQ5yuKhSdVhxP+MiLitpPS2OI0f10CTjQs8TWpsKklWYlD/KlZkSK4WaQdm4dsLHVY3mEJ\nCm02FePUZF6PjFEK7GbZhkaqKtvcsjxjiVvZOPvJ8FhoyRXffEdmExMxtWJN8v7ZFzfyOkiz85PR\ne55kJ4qRSM9DKaQTlirNBTYSgypXjq5yHmGZX9clbgPHTHSrD4sSGTrGnizPqypMy15vH9nWtfmM\nY/kxtl8m4oIMxcX3X/A1rRi7cd746ovvzztWn4zC5ebh3UM4tqWVfpYzIP1lJuezXMi0ZxTK+YmZ\nOX+bectGlmW/hN8sXJ05q8rsZiD2MRBtU7bjzz4xENv8Hs9aPoxYiF0PMRuIMZvEsZiIXJVdTkGW\nfb7AtTIdc2w3KYTFy7YCnQbsUy8FkMLIN+dfvwsAcPRvyRp86Ng4AOD+OTIRH3VMxIkOvZ/ERJwB\n7VfnfMQmprJMRKnSLAZir1qzVGkWA/FksxGRf1xQI1FRFEVRlAFx44034vOf/zz27duH17/+9bju\nuuuW3ee9730vHnzwQQDAZz/7WVx55ZXZY5dffjkuvvhiXHvttbjtttvw0Y9+FP/1v/7XdenjTGVN\nzcT1mEicTxguPbGhKMrpQ7VW3ughKIqirA1RF3ZqApg5ArRnsXA2V1EURVEU5WQwSDfpfxKVtN4c\nO3YMb37zmwEAf/RHf4RqtbrsPq1WC3/0R38EAHjxi1+cmwQUXv/612d5ix/84AfXpY8zmXWp5qwo\nZysGBrUCBetb5G09H2wcsq0ny2LdhZzRF4LawHKWos0bjWLQSRXiOGs73HIWoOWcLOTNxWys84zH\ngI+ZmYTOWDzne4csn1HG4uQOehB7h+2+lD7wt3HmYYwU51Z3YMrOAQBaPuVMdi0bNjxWsTllfGXL\nmYm2lFVCLvKt0lUpNstGVy8fEHw9qO1wdmGWZZiK4SnXhdqSJ/1SW+NPyyE+zhBnA5bYrvSRt/va\nrA8WuZ8mvxy6CbJkSHG+ekZivpWsQjEK3erLcnJZcWCRzNiOjB0xKHCktl5mInh72s/j/QOvZyhK\nJqKsCz06oaIYiVytuVhiI7FC6yUb0avw67gkQZF5I3HeSfPgZLAS0shPQImfgCJXTS/yl1j8ZZYt\nzMsAlMrEro230tw/t6q5m7U4v5LxYsjly8zEJL/sGHp2/vH6ZkI6x86OtYwNuByyn/SbOP1lx3GO\nx+dk4qivYShZiCZ7PMm3coyIt8+MRMlK5FayDxe0bNhJBmKHK6ezgZh0+P0g9mHk2Iexh5iNQzGL\nxdqN+fNXMkC3jDQBAMNPNIC18J62G4ibsNvPpYMdPEz7f/ImAMD+m+l1+uDUKADg4cYYAOBQi/o9\n1qaxTrbp3KdisgnnQJ+Fs2YaANAC2dsd28tFjDnoMXYyEXtZiKvLRhTUSFQURVEUZTPxpje9CRMT\nE3jlK1+Jq666Cvfff/+y+9x0001oNOj3qVe96lV9t7vmmmvwrW99C/v378edd96Zq9GxFn2cyZx9\nJWcURdlwyrXCgjtDFUVRTgdsHAP1KeD4QaA7C6Tx8jspiqIoiqIoq+Zv/uZv8JnPfAajo6P40Ic+\ntOL9/vVf/zX7+Qd+4Af6bvf85z8/+/nWW29d8z7OZAZqJlpr8c1vfhM333wzHnzwQczOziJJFs9a\nEz74wQ/iwgsvHNAIFeUUMQF24WIAQMoml8dmi1h6IWf1ZdWGjbRkxgRsIIWeGIl5xBFJUskApDXd\nlLMDuY3Yf+tVLc6/12RcoQ1RZEuyxGMpcU6aWHWSOygVkW2WH8Y2Hq+Qxz1WtUpsDQ5zmeJtbKXN\n1qoYDgtIIjINiwnZO02u9hyZfFWuEGQuVtlwrPohqpyVNxTSsapOdeXAuXARXzgxBluJGIScm5bm\nbZqSn++3xsLbSMhZaWzklX028HhyNJbMRD73Ziz5h7Tc9sxCYzCr5izWXz4r0XdMxQWRfHKuIn5J\n3B2vzgogc+s7GYzSn1SKizPlsWcjWmcMIZuZxQJnJDpGYjDEx5ZqzaX8E2NkNjk7OC9yrqDJgh9T\nOTDvL080m4gVyuNEiV5DVozFQrG37UpNxLSPUbVWFYxD7l/MvazI8yIWYD8D0R1KPwnMtR/dCa9+\nmYr9yD54uB/+nJEcSBPHPRNR2shdThZ/PMr3hS6bdF02Dtnasx3JQOTPGzEQOUIx7tBJJJFUZM4b\niEkiFZklB1EyQ3tZiFLpfKRCn0Gju6gtPrEGdFuwT7sAsDGdLwB8/yAAoPWn/w4AePgByoV9cPYc\nAMCBJr1Oj/BH2gke+zTbltMxDX7W0LfedUOZiK6JGKVNvkRiI3azjMQsH3e9shEFNRIVRVEUZeBY\nDL5q8kpZ73FNTEzgTW96EwDg/e9/P3bu3Lnife+9914AQLlcxp49e/puN3+uSfZZyz7OZAY2mXjv\nvffiZ3/2Z3HnnXeuar+VBGsqinJ6Ua4VN3oIiqIoy9PtwjbmgKk5+o3ZnrvRI1IURVEURdlUxHGM\nqampJbcZGxtbdb9vfvObcfToUTz/+c/H61//+lXte/ToUQBYdgJy27ZtCIIAcRxn+6xlH2cyA5lM\nPHbsGK644gocP34cAFAsFnHxxRdjbGwMnre0DlGr1QYxREVZE4wJ8YQCfdi4lp4Yh0XWxYq+2Hvg\nlpZ7dhqtF0FLvviR2LEoywCU6sG0oi3VgzkEUIy8xPnqSGzDUmAy604qFY9yDF2N7TMx2SK27rrc\ntmVZxCKnenAtoBW7y2ThbC12YQoB7hqiAxhDbTNmIzKl5bbl7C82ZMTirHj0+FDoY7hABxl3xlrl\nVqw+QcbeYDupzrYSx62hk+aVL5YpMzNxmPsdZROvFrCRx4ain+VH0o6dmJ7YAhunIWf/NYyHiI0o\nGaEcORTrL6uUjNy59DIOZY98dqLIfvJcS+6bYBwj0XOukYh5sesypik8NuRStplkC5+rOfts3flc\nV8cUFzcS5cVhe3orD5qrPPv5HEcj1YRlcHKSsp5zETMjsVzOP74a1spAXC2L5RyKte+aiS7yfGQ5\nhE5uoWMQZttnx+pTedp9PHXsycWswyzrUB5Lcttaea7ZRLSOkZh2+XExDvlDjqMAkXL2YdLlNpYM\nRDYRpRIzv5YStg3FFpanN0okf5ZWDBU62DFGBmDlIh9IE3iP2wJELWA7f9N87AS1/3AbAOCxm2is\n358YpbZxAa1v0TFPsIk4wWXdp/mazCRkE9bZRGx6nBu7AhORzqlnI7omokU+E3GBiSiokagoiqIo\nyhryzW9+E+Pj40tuY1epMX7hC1/AjTfeiEKhgI9//OO9u5pWyNwc/Y5VLi9f9LNcLmNubi7bZy37\nOJMZyGTiH/7hH+L48eMoFov44Ac/iNe+9rWoyK1piqKcVYS1Sv9bMxVFUTYCa2GbTWCqDXRbwHmF\njR6RoiiKoigKYHvRLJuN9fqTbnp6Gm984xsBAG9/+9vxhCc8YdV9pPwF+HLyGgD4LCG4EXxr0ceZ\nzEAmEyW48h3veEd2z7uinIn4Xgn/YUev5i7Qs78KbHCV2byqctZelQ23oZCr4bLpVpAsPk9sE+ov\nStl8i+jt24hJK6zLMts5TTbvOlllXzNvVECR7bfRQoRzymS/bBsiG2b7+WTHlB6Xn/SPDrItw1+4\n1I/RH9xzdco0bLNhKOc8OkT9br+Mzm3muwb+ngL2H5BMQBrjHI+13mVbMKV+JBdSKjRX2G4bLXiZ\nkbilQNdpvEDHqIZsDHr5D3Kp3tzgMc7Fcv3y10lspQKfQ4WNxBF+nmp8nKFC/vny+HlK07yZGHa5\nancs1qGPbuI8J1kV53xWolROlkrRmY+YmYq8P/9L7mfZh7yds5/gCnhuVOBihmJguVKu01dWbJiv\nExcEz1q4//hK52zO2sQx4ETFZcPOyGClmrNbXdg1FU/GSHTHtliG4WL0yzUUpB/XFsyqG/exCed/\nc9uvGnOaf7KzvtK8DbjAJJTt5BjZfshvnz3uVmCWF4drHSZAR4zDtLcOgBWNusumIb8XpKiwRPtl\nxmHE78Ukbx4maT77MOY2sfJ4/oUtlZhTXj9cJKtvbJg+x4Z2x7CdNsKLysC5WwFYoE7GIA6SiTj3\nZ98DADz4yFYAwKMNbpv0Aj/apmOcYJtyhq/BTEQnN2dJUax79Jna9OnDs2OlpfVSkdk1EVO+SFml\n5iz7Ml1QpXlQ2YjZZvqtkKIoiqIoAJ73vOfhi1/84pr199a3vhWHDh3CJZdcgne84x0n1Ue1Sln7\n7XZ7mS2BVot+/xoaGlrzPs5kBjKZKErrD/7gDw7icIoyMIwzTVMIhnDN8x7MrfN4UsXjeTl/hCfc\ntpAubcbpQwpjo9TWeLlME3QIeHJE/nhv8YfZDM/oTVJrJ+g2uWSCHk9maPuE/zZOI6mgwZNa/DkX\nnleGedkLqY9zzsmN3f1TMXBaHiG233MP/XDoGB+M99zzeFp84hMBAIVfugFezcNWngAMuERIJc5P\nFrZ5gk8KvHhGHqduxwrA1iL1sb1If2SPl+m8h0r0R3lQyE8mRl06VqtNs5DVLrUNnoRtp/miDGE2\n+csFV0I6zhAfr1SiP/JDPo7MJyV8LmGHC+pIIR657nGKDk9CxTY/CSWThr4n503rjRS+gdwiTOs9\n/iGQ9X2+tMymt7MCLvnbrGUc2TwRP57YXg9yq6hM6Cz7DemCGUtpnVtaZaJJ5j14MtHIMk/WZgUv\nskktZ/JMlnkSB6HMZi6CO4kn+8gknzsJ2I+VFi2xzoQcT8BlE4DuuaUW2bPj3gItuJOBiTPmfkVO\nUne//KSkleNJZgH3Z7N+ZaJQWmTL8rPMd8myFERJ4/wkoUwKJnJbcrZeXn9SHMl57ckXNXyNurxe\nLlmtSAcer9KkYXUbLRcuog89s3cXELeAmWnAVoBj0+j8xfcBAA/eTbfoHKzT5/DBFt2+fIQnDSe4\n2MsU374826XPg7mE2rppckufy22eRMxuX074Cxnb4XOSScSFtzED+cnD+etpwnDpyUNBJxEVRVEU\nRVlPgiA4qUzExbj11ltx/fXXAwA+9rGPoVg8uax9ue16YmJiye0ajQY6Hf7C2TmHtejjTGYgk4kX\nX3wx/vmf/xmNRmMQh1MUZZNiqmfPNzWKomwium2gEdEtzFsdA1VRFEVRFGWTQl8xb9bbnNd+XAcP\nHgRAE5Sve93rFt1mZmYm+/nlL395NuH4ve99L7vd+JJLLsH//b//F1NTU5iZmcHIyMiifT3yyCPZ\nz4973ONyj61FH2cyA5lM/Pmf/3l8/OMfxxe+8AX85//8nwdxSEVZU1wDsR9DhR0o/dF/WdG21mnX\nCs9pl/CzFowFPOHv3XIHEBSAekTK3bYakMZIX/AcADHcUYt5CGn74I9VAGOxp0LHGQ7pg3+W7cAZ\nNpiabCxJoRe5+nKL+FiYYFuJTJ6tFTJ9RkbITCyMcEGQcr5yTcKVVsp1Mnsqddq/0aYxyG3JYtz5\nrMbJbcwVPl6pSvsHZTYOnWg1EYn8JtuFPGavLbc5Byg5hSGEfoVRBDEGU+eWbNcS9LJCKza37NZt\nEfOry1Zahw0vK7fGi+yGnr0Ys70kt5RmdwLLGJzbb2UDmbex0mlbimvY3OOGi/aIdWn59nLT4Qsr\nxT242IfpsP7mtXiwbGvNu/U4Mw2lzW4Bdk1BxyB0i4/0Y8Gt3K5N6NwanJmI7njm2YWJ00fvQvMy\n8o/LeUeOQZjdjizLYhpm97Lz/tm98dSt3IoslqFcQjER5fln2zCOfKRS2CRxTEJuozhvFvZeQ46J\naMU8zJ9qN8nbvsPyGVClz5PMQDyvAsDC7BqjIipTLcB6sMfoF8/OZ78LIG8hHm7Tt8gLDUS6DrP8\nuptL6Bh1vn256XEhFb59uWvp82jh7ctNPtc4tz4zEFdkIgJ5G/EUC6tk+6mRqCiKoijK5iKOYzz0\n0EPLbieTj0C+yMvTn/707Ofbb78dP/RDP7To/rfddlv289Oe9rTcY2vRx5nMQCYTL7/8crzlLW/B\nhz/8YfzQD/0QfuInfmIQh1UUZVUYwJQAUwCqXI2rwxM0foH+Q5H/SwB0sOwfqTkKy1emVRRFOVms\nBdpNYK5JJuLINl6vBqKiKIqiKMrpwJVXXokHHnhgyW2uv/56vOc97wEAfPrTn84m8IKgN7314he/\nGJ7nIU1TfO5zn+s7Efi5z30OAFCr1fDCF74w99ha9HEmM5DJRAD4/d//fWzduhVXX301fuzHfgyv\neMUrcMkllyxb1Xnv3r0nfZ+8oqwVrpHRz1Tc5T1+EMNZc7w7vwMUaohuobzH+lEnyy8ky6Z41+cB\nAMETqQhB+qJngSYVV0IBhXNJ49tryRBqTnPxmAYbih16vB7R+naaH0eJcwxHi12MVWmiszbORuJ2\n2tYfoSRHU6Y+rOQutsko8mc563CGTUM2FSPOOMzyCDm3MCjSMYOKtGxLlnlsvuS1yXGkcAi3YglK\nf92klw3nzHH0K4ySOCajGF9xliHH+3Mb8LH9rM2binJLQsQmmc/jMVyURjIVE9PLaBRbTMTCLGeR\n903ZthPjTQw2GV32RSEbiYmYifzyya57Vj+FC4sUuF82Ew0/j+DsSgQtHjsbXd48m9XNB+xXjET2\nlZNzi4+s0NzKcE3GLJdQjpc3FTN7MLMBk942Nt9m82KZNSmFbHh9JH3w9c0yDfNDsGwPimFoHUsw\nswzlNSfbZ3ZsPu8wtt4C09A65mzsGIjGMWc7qbz+aIwltlJHuXDKyCg91+Wd/F46r0YnVCkBURs4\nTnZgepSLnXyGvs2+/wGaVHyssQUAcLSzg1q2ECc7wGzkGIixGIh07CZnIbYMmYhtwxmIoDEtl4Uo\nhVTEPHSNQyvL2ftlmaIqwLpnI/bvV1EURVGUQeJGZ28WVvsr8kqoVqu46KKLltxm69at2c979uxZ\ndPsdO3bgx3/8x/G5z30Of/Znf4a3ve1tOO+883Lb3HHHHfjyl78MAHjNa16zYN5pLfo4kxnYZKIx\nBj/2Yz+Gf/zHf8SnP/1pfPrTn17Rfv/2b/+GZz7zmes8OmUtOO+887OMgjOdfpOJe0e2ASgPdjCn\njAGKwye5bwj6GGlhaUuxsMRjiqIoK8XCdlpkTU83KFfAaBaroiiKoiiKkue9730vvvKVr6DRaODK\nK6/EDTfcgGc84xkAgH/6p3/Cz/7szyJJEmzZsgXvfOc7162PM5WBTSZ+5CMfwZvf/Gaky2VPKact\nw8PDObX4bKQ0XIB34BAAwIZ8LWSCNeD0QjbZrKyXVnLXpPV5f1dXcxG7qk0mjOFKUmiSQWOk+nOn\nm99eaHeBKTJ6/FEaYyWIc5uYEhtsW9gk3jJ/8tEAqIAmFJ2+aS/IZKL3xD0AgNL4FACgMEljrE2Q\n5TM+wdWc61xhuUutWFCFkHMPK12Ut3BF6G00Zm8rj22Eq2EXaF+pCizXwRvKtz6biUXO3hPDy/DT\nYop0bI9NRzEeETi3bEsWXcAKmJFrwYaiz1mLhRRpEmExjMQ8ZiIbG4iRZM4F3Eq1a34tSc4jG4iS\n9xgGbFPy8+n7+WrOUcQZdFH+fSsGWcwXIbbzoxDzllks+XhSqVcEQbHsZOI9yWckJvyyTDp5A84P\npEI1Z1KG3E+bzcQWv7656nNmGcrzkQldSS+jMKtk3M8MdPIIXSvQ+TrYLvc1bHaxsh2oEUNRKiTL\nyyDJ24Q26b0OM+PQqRmSDUEqc0vEYiwmoeQX8mtI8grT/PVOnX5Sm7cHs+dXvFfn1LPK36mHxMnu\n9J3sz25WCZyWS1wpvVqgC3HOEH0OVcY4+3A3v7d3VoG4A0QA4i7siQYwBCQHJgEAs18h0/m+A2Qg\nHmlTVfrjHXpdH23TuKb4I3C2m7cQG0nU10DschanZCFG3ErmYWIdA5FVUDcLcYGBmOUbrsJEBHI2\n4nplI/bvX1EURVEU5fThCU94Am688UZcffXVuO+++3DZZZdh27ZtiOMYU1P09+jw8DD+7u/+Dtu3\nb1+3PlbK3XffjQ996EOYnJzET/3UT+FnfuZnYEz+9+tnPvOZuO222zaFxDWQmZ/bb78dv/RLvwQA\n2LVrF376p38aT3nKUzA2NgbPDa53uPjiiwcxREVRTpkygDZ6FR0AmmgsbcxwFEU5LbFxDHTawExC\n2YfVsY0ekqIoiqIoyoZgYRYUW9ws2E06rvn85E/+JO6880785m/+Jr74xS/i+PHjACjf8KUvfSl+\n67d+C+eee+6697EcDz74IJ73vOehXifJ5nOf+xz+/M//HJ/61KcwPj6ebfetb31rebFhQAxkMvFj\nH/sYAOCyyy7DP/3TP2F4+GRvqVSUzU2CLuwQ33InBqIYiYEYio6xeKpIP9UKH4/z70QkYkPStB0z\nMQsmKwIlNgfZ6vOkYq78+1DlCcGxUQBAuq/fh2UJNJmYgBL88rWk06c+mX64kD4kvRMnqD1O3+qE\nx6cBAJVJsoHSOrVibJmAq7wOBfDG2EAc5+s9WqO2IteBz1vOt8XFZLjyq1ehZa9JZpHt5m1MMURN\nic+BTcesFbL+paowN07WnVQp7llwvedILEi3fG3apR8SNqu6LTqWz8ZVIBWo2TLLchm5AnWhwPYm\n5z56gWhttH2Bz9lviQ0l1qHYbD1LTf7JStAz0ajlMbABZ9kClGrARqw8vjwJPw1xW6xL2V8MR7e6\nM/9QZDPR73K/PCB+/np2Hz8fnQS2mzcPF1QwzrIRZTFvFGb/TveXw5wHnEVn/8w2zHIJ2SpM8jah\nTb2eOZiNJf/Lmiy7bVaQuk/OZuoYiC6ZH5c69iuvl73ciuNRarIcTam2XODXYa1IT/7IMFl95XFa\nH26n17G3rQzEXfqNJO4CJ+jzIZ1iK/BWsr0fvZt+d3hoehQAMNElE/FEl871GL9Pptk8rHMF67ko\nX4m5yTZh21D/LdPobyAmeQOxXzVmMQlXm4UorMREpH7mX/e1NREXP4aiKIqiKMrm4rWvfS1e/OIX\nA6AaG8tx6aWX4q//+q+RpimOHTsGz/Owbdu2BdbfevexFO95z3tQLpfxgQ98AKVSCe9973tx0003\n4Qd+4Afwj//4j9i9e/eaHGctGchk4oMPUlGHt7/97TqRqChnPAEGmKCgKMrphAXQbcN2OkAhAaJu\nL7O1rNmqiqIoiqIoytKMjIxgZGRk1ft5noedO3ee0rHXoo/F+PrXv45PfOIT+PEf/3EAwCte8Qq8\n/vWvx4033ogXvehF+NrXvrYuxz0VBvIXvzzRm3E2VVHWki5asOMbfEseV5CyTiWpJV0Tseu6jr0o\n1mN5jYvKsL2ZisXJVbHMNBmK3gS3M3M8LidjsFwEhshMtDXqw1a5rwKbhGwWWj4n0yTTyJTYsiyR\noShZkkay9yR6IXRM0lD65W+fRBlrS4YfVxUWk4itKMls9Px5RqLkMRb4h5AtSLFZWS/z2mwi1qn1\n2PSSL4S5BZYAAQAASURBVMC8DucQsmUmlaNDNhILZa7mXOYsxVL+mzMxHyXPMeXcN6nyHGVVo02W\nh5cZcG7GXpaZyGNhOyzLgZSCyWwkdtt0Xbuc1xjzMQO+TmLtydhMdv26fO55kzTlKtq2xTZnC0gj\n1wDksWRvBtfuQ56+NiAWZYE9KFbhgv2disiOTZhak1VGzgTXPkWfXFI4lZPlObT5vEJBXs5uZWWx\nUzuOmRiKdciVwodL9HyM1lqo7OBc0K1sOm+tUjnpoQKQREDsUTsRAfBgZ7pA4CO+fwIAMPn/6Pwf\nPMpVl9ujtJ7Nw+Ocr+lmH86xDVvntsm5hQ1QMGebrcM25yB2OBdRKjHHtr2sgShmYb9qzHDMw1PJ\nQqT9+31iL2EjnuItL2okKoqiKMrmZKW/Bw4a/c1h7Th69CiuuOKKbLlSqeCGG27Aueeei9/93d/F\nlVdeiVtuuWUDR7iQpQML14jnP//5AIAHHnhgEIdTFEVRFGXQRBEVfqpPAVNHgOnHgJmjQGsa6DZo\nIlFRFEVRFEVRlBz79u3DCY4Am8/73vc+vOc978G3vvUtvOQlL9mAkfVnIGbi6173OvzBH/wB/uAP\n/gBXX301CgW9lUk5M5G8rdOO9TIQV4kdHcu1GWJKig0Y5rMYl4TPyRbJSLQhff6YMi0bMQtlDKJq\nyecUm47W4wxGMYi4araps+EoVpBkLxbZIkzE/uN+fQNT4r6kMnSZLVI2E2UfNMisSkt0rMCQkmXT\nfN6a5BWKmRgU2EQUI3FYKlJLBXG2/jrUT9FnayphM5FzDNucyRh7JjPUjJOVl6R5Q7GfmZh02Ujk\nvMd2h869xWaiVA+WPMNyQsfOjsYmWMD5h16BTTG21OIGm3Qtet663QCJZBHaxXP/VsoCYdExEF3b\nr992rmUoFmGviLQYiybbJotd5MfcrEKTtXmzMKvGLK2TvSjPo1RcLvFrYKjApuEofZZVdvN22+h9\nZEbLZNHVilS6e2iEnptgCJiapYPM1gFUgLkWYAzSuw8CACbvoL4eOkwh0sfbWwEAU/x6m2ADcaJD\nY+yZh5KBSK+BZsJVmNkibBlpacxtnzIXs9xDqbjstElWkTleUIW5Xwbi6g1E5PbLUBNRURRFURRF\nYV74whfia1/7Gi688MIFj73jHe+AtRbXXXfdBoysPwMxE8fHx/HlL38Zx48fx4tf/GLs379/EIdV\nFEVRFOVkSRLYTgu2MQfUJ8kynDlM/7Umgc4skLRpUlFRFEVRFEVZeyx9cb0Z/9OvJdeO17/+9fjI\nRz7S9/Hf+I3fwDvf+c4Bjmh5BmImfuADH8D+/ftxxRVX4FOf+hQuvPBCXH755Xjc4x6HClde7cev\n/dqvnXKZbUUZFFHa2OghnJmIOXkqFbDFOBymqs+WsxNt4mQlLlNtO/tHM+JbNlm9M7LMNqHhHMSs\n+rNUdS4GQIWtx2GuSF0p5Y8t5mGTK06HZFqBKx0HsRiKZC3FnJ3oScxjUbISe9WvAcDwcWVsVnId\nfRqzVHcud+hcKjHtF1kfaZ9fF7ICyJL/J1mHsZQTpybq0OBanJVYj2gsjYgMxSjJG3MdtiIlvzFh\na7LE+ZmSQZnwdnN1sjub3F8n8bNcR0GsvX4237LKYp/fmPpVRnYtwn7I/q6NuFjfsuRzdmHAFbxD\nNgsrBXoOayP0nJZ28mtmN2eKbuWq57UqHalcBGwCWwzJlisXAZvCcjEU06F+0GDrmo3cbPnwcQBA\nfNujOPIteh0fmaFjTXepj+mI8pKPtsVApOfUNQ8bXIk7a/m26BbnZM6vvgxgQQXmzDxM8+ZhyqXE\nY9ZYZXm+fbig+nJf81BYawOxz/4up2gjrmwMiqIoiqIoyiB56lOfijvuuGPJbX7rt34Lv/zLv4wg\n2BzFTgcyir/6q7/CnXfemVv3zW9+E9/85jeX3ffnfu7ndDJRURRFUVZLmsJGEZDEQBs0Sd2KqfVb\nVJHG4wltnyMO7EBuWFAURVEURVEUZZVs2bJlo4eQMZDJxL1792J6evqk9i1J5VVFOQ2IktZGD0FZ\nKVL1GavIX5wP5zbaCk3GmDm2BwPHogwk35AnaSphz0gcZUtymK0xqcAtOYtzc7lDem3OdWuTPeV1\n2EBkmcnwIQyfkuHqzaZCK8wwTxixLWnYADNsbfpdMr5KTTpOiS3CduIjtfSz5AO61l1WqVgMRbYe\nLWcqdjgrsdWlsdS5neHMxK5kL3J/JTbu2olk/lFb4Xy9YkEMMtpvuk3XrpX0KiJnFh/3JeafWH3i\n+WXnhGUMRami7awOvDT3eCDWID8xRX4NFIt8XYdo7AWOBvVG6Vp4W/l1sbUGjI7QmCv8nEnmZ7EA\nIAXCAIBl49by687yfylcjXIZ3w1ocTXyY8doLP96FwCge+tjAIDZR2iMs7M0jnqHrMNZtg+Pts/F\ncX6OJ7t0Ieos/c6xtFvn10SDLV7JPmyyKdg2ZEG22EAU87CDhdWXAfStwJxYqbgc5dYvlX/YL/tQ\nWGkV5mz1qRqIvQOvbLululATUVEURVFOW+i3u81azXlzjksZDAOZTPzbv/3bQRxm3di/fz++/OUv\n4+DBgygWi7j44ovx4he/GLVabdMdZ1BjVRRFUVaItfRfksCmFuh2YdMUtsPr2z5NSrUTblOga+jn\nMKLWLwGwQCgGoRQyk9vxN8ftDoqiKIqiKIqinPnoXx9LMDc3h1/+5V/G9ddfD+vYAdVqFb/3e7+H\nX/zFX9wUxxnUWJWliZLTtJqzcvJItWg2HU3A3hrbfkayGKWac7GQZSTakWFqpXp1wbEkOQ/DcBVb\n1MnI8mbJxPK48q6N8u95T8zEAk00mSJ/1HMOXlY92uZNPL9FJldYo+OUG3TcdpQgSfPVgT24ZiK1\nCVdztlJFmLMT22wgNuK8kTjDpmEzyVuCPHS02EzsprRihJe3svV3zgtToNPEjv9wMe3AtiXCsGeH\nen1u3ZXq4JKbKfvyskkSUOp12tveApn1JzmcngfAwgYBXQixBMMCrRfjVCqES9V0Z1wrdNVWxtQU\nHeLuewAA9u4DAIDuI2z5TdPYu026Rh22UJtcDXuqRa/Ro22KGZnu0uNz/Hw2+PlqxL3Ky272YYtL\nSDfZDGxber022DzssInoZh/2q8LsmoaSjShW4XIGoqwX5luIq80+zFafTBXmRTtae3tQjURFURRF\nUZTNz4033og0XdO/BPpyzTXXwJi1MUo37WTivffei0KhsGhp7EEQRRF+4id+Av/8z/8MAPhP/+k/\n4YorrkCj0cCnP/1pPPTQQ3jTm96E2dlZvP3tb9/Q4wxqrMryJGl7o4egDBqZUJKJomwCy3k8DHpt\nlScgxRh2JxEZO0K3uqLNt6BO023PpkR9eQFPrvjOpIEc23eOLZOIcuuswDN4ZpgnKWdo0qdQpMmX\nUitGl28fljkSj28dln+LZBJQRiKFWGIukCKTiXUu6jLLk4hTkUxK0X48/wSpX9PmHyK+DTpxTnXn\nicPAzAngnG1YM/gfc8u3/qJL18PIpGO62GQiYOU+c89pY56cnJmh9jG6dRhTtIwZvkX+BC3bqRbS\nBhcJaXKhjzh/gS2PQebHeJ4OcZPG1Jql19T0LD3XU216rc12xwEAdX5e5LbwFj9f7TQ/STjHz0+T\nj8/DQZuXWwmtaCfJgknDFk8WLjtpmC4+aZgVTEn5de5MEi43abiqW5bX+3bl3sFXt/1qutbJQ0VR\nFEU5I0k36T/x6/hrzVnFq1/9aiRJsvyGa8DVV1+9ZgVcNu1k4jXXXINDhw7h4MGDG1Kt5n/8j/+R\nTc79/u//Pt761rdmj7373e/GS1/6Uvz93/89fuM3fgNXXXUVnvKUp2zYcQY1VkVRFEVRFEVRFEVR\nFGVt2LNnD+I4Xn7DNWCtrERgQJOJN910EyYnJ1e8/WOPPYbvfve76Ha7eOihh/C4xz1uHUe3kFar\nhd/7vd8DALzwhS/MTc4BVBTm+uuvx3nnnYdWq4Xf+q3fwmc+85kNOc6gxqqsDMuGjXIWIkZi9gFt\nck1mC4ZB77bXsmMI9sGWuchLkW+llnuAAzHj8l8Lyj8SxueDhrw9G5C2nC9sZUSrr5Ap5lXIIAsr\nZIaFjQSFOP9tmRRgMU4hlpRvh5bCKGI0trlt8G2yc2K+sVE3w7fIJvzVa8jWX5Lmr6dn+NZtQybo\nA7dUER3p4Lz7/5QeZ8nTCwHj/AuXWXxyNzO/XdOIb8nucPGYOH+LdprKrdtyK7aMJX8NxNb0+RZs\n99/q7FZwvlU7YjswYkuzzdZmN6kiktvKuZVvpOXYEnid8HLMY+zy9s0k34ppKLeTt8UwTGQ/m1vf\nZQVUblVuc1GgLr9WWnwxu2wRdky3VziF28h0eJvFDcQk7WMiOrczu7cxwymW0q94ymqKpqzZ7cpZ\nh4P7ql6NREVRFEU5c7Ho/f632dACLGvDI488stFDOCkGMpn4jne8A3feeeeq9wvDcENKX3/lK1/J\nqk//wi/8wqLbbN++HS972cvwyU9+El/60pfQaDRQrVYHfpxBjVVRFEVRFEVRFEVRFEVRNu1tzuef\nfz5++7d/G1u3bh34sb/61a9mP//gD/5g3+3+43/8j/jkJz+JdruNb3zjG/jhH/7hgR9nUGNVVobF\nYLIOlE2IWICujuY5pqLvZYVVVkyJTcKi5DKKkZg/ZCZDZTakHFNMxnm5jQDg8XrR6rl/U2T7L5Td\nUgRs20kBFsFbYN/lLb5YLLxUCqlQ2+K3Sj3OF+2I2HwLs+IkNJaQz7nE6wPe75FGFc3ZCI/+P/qC\nxDdiNqbZz2IQyre6YvPJmKJs2eTOsbeMJRFBtHe8/LJxi9UssAqRG0diTZYN2TMRZZlb6UtsRzkH\nXu445mGHd5T1HT5AxOvFRBTzsJOKiUg7dFjnjECvlY4hi7DjtXl9JzMSY8sZiaks5w1EMRITthvd\nTEQ389C6y6doIi5u8m1+AzF3WLURFUVRFEVRlA2kT4nLteWrX/0qjh8/vuR/Bw8exC233ILXv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JrD9elsrKNkpg2LwyUmWZMxTh8/c7kmnYoPXpHBtWM2xYcXHnqM05hom0ecPOtOR60XE8GQPn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\nGFuTvW3GbjMxYd11mYmDsgE3oZywu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          }
        }
      ],
      "source": [
        "final_sim_data.plot_field(\"field\", \"E\", \"abs^2\")\n",
        "plt.show()"
      ],
      "id": "190324f2"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "And we can visualise how the power transmitted varies over the frequency\n",
        "range."
      ],
      "id": "bad97eef-9ba0-41d9-9414-c6eb58564f2f"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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PPWds3LjR3E/OnTtnDB8+3PIlNH/+/OYhdNG9//77ZpuCBQsaP/74o2WEbWBgoHHs2DFj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\n9Ntvv5mHXqxfvz7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RUS9WeFb1yz2jfFlzpVCm6VdYRLg2Ht6pZbtXa+W+DboT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\n/6yBbfqobIESLF0jF2p6hYSEaNGiRZKkDh06KG/evPeNcXd31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8ND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\niIiIiIiIyOgw6EVEREREREREREbn/wEQZ6Uw3FpBSAAAAABJRU5ErkJggg==\n"
          }
        }
      ],
      "source": [
        "power_transmitted = np.squeeze(\n",
        "    np.abs(final_sim_data[\"mode_12\"].amps.sel(direction=\"+\", mode_index=0)) ** 2\n",
        ")\n",
        "plt.plot(freqs, power_transmitted)\n",
        "plt.title(\"Power transmitted across frequency range\")\n",
        "plt.xlabel(\"Frequency / Hz\")\n",
        "plt.ylabel(\"Power\")\n",
        "plt.show()"
      ],
      "id": "80390a05"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Conclusion\n",
        "\n",
        "Through comparison of the junction geometry and the transmitted and\n",
        "reflected power, we can show that these results closely follow the\n",
        "results published by `Gao et al.` for the design of a Y-junction. This\n",
        "notebook demonstrates how to carry out Bayesian optimization with the\n",
        "Design plugin, and can be readily adapted to other use cases.\n",
        "\n",
        "Learn more about the Design plugin:\n",
        "\n",
        "1.  [Intro to Design\n",
        "    Plugin](https://www.flexcompute.com/tidy3d/examples/notebooks/Design/)\n",
        "\n",
        "2.  [Particle Swarm Optimizer\n",
        "    PBS](https://www.flexcompute.com/tidy3d/examples/notebooks/ParticleSwarmOptimizedPBS/)\n",
        "\n",
        "3.  [Particle Swarm Optimizer Bullseye\n",
        "    Cavity](https://www.flexcompute.com/tidy3d/examples/notebooks/BullseyeCavityPSO/)\n",
        "\n",
        "4.  [Genetic Algorithm\n",
        "    Reflector](https://www.flexcompute.com/tidy3d/examples/notebooks/GeneticAlgorithmReflector/)\n",
        "\n",
        "5.  [All-Dielectric Structural\n",
        "    Colors](https://www.flexcompute.com/tidy3d/examples/notebooks/AllDielectricStructuralColor/)\n",
        "\n",
        "6.  [Parameter\n",
        "    Scan](https://www.flexcompute.com/tidy3d/examples/notebooks/ParameterScan/)"
      ],
      "id": "6793b372-4ab1-4cc8-96be-8e928557627b"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  }
}