assembly_simulation.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/YY535/BCILAB/blob/master/assembly_simulation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Me9r-dhd-Z7b"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m3c8cQe--eV0"
      },
      "source": [
        "Here we create 3 types of simulations, they comes from:\n",
        "\n",
        "1.   Lopes-dos-Santos 13 JNM, Fig 3, poisson spike train with ensembles with N={n1,..} neurons increase their with uniform distribution $p \\sim U[1, 6]$ at random time bins.\n",
        "\n",
        "  Parameters:  \n",
        "\n",
        "  * 20-(random, 2 assemblies)/32-(5 assemblies) neurons\n",
        "  * simulated for of 1 spike/bin for 8000 time bins\n",
        "  * spatially overlaping/non-overlaping,\n",
        "  * 150/1000 simulations.\n",
        "2.Brian2 based network simulation, with non-overlapping assemblies\n",
        "3. Nonlinear-embedding used in pi-VAE and CEBRA.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2xPaTFqCTcq1"
      },
      "source": [
        "## Lopes-dos-Santos 13 JNM linear ensembles.\n",
        "\n",
        "Ground truth:\n",
        "* background firing: spike_trains_noise;\n",
        "* ensembles loading: ensembles;\n",
        "* ensembles activites: activities.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "EvsjcfgmFqxF",
        "outputId": "1d5dd224-a095-42ef-f525-22d72d5f93d4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Set the number of neurons and the total length of the simulation\n",
        "num_neurons = 20\n",
        "total_length = 80 # 8000 is the simulation time\n",
        "\n",
        "# Generate the  background spike trains\n",
        "spike_trains_noise = np.random.poisson(lam = 1, size=(num_neurons, total_length))\n",
        "\n",
        "# non-overlapping ensembles\n",
        "tmp = np.argsort(np.random.randn(20))\n",
        "ensembles = np.zeros([num_neurons,2])\n",
        "ensembles[tmp[:4],0]=1.\n",
        "ensembles[tmp[15:],1] = 1.\n",
        "# ensembles[:4,0] = 1.\n",
        "# ensembles[16:,1] = 1.\n",
        "\n",
        "ensemble_gain = 10. # for a clear view at the ensembles. in the paper it is 1.\n",
        "\n",
        "activitys = (np.random.uniform(0.,1.,size=[2,total_length])<.01)* np.fix(np.random.uniform(low=1.0, high=6.0, size=[2,total_length]))\n",
        "\n",
        "spike_trains = np.dot(ensembles,activitys*ensemble_gain) +spike_trains_noise\n",
        "\n",
        "plt.figure(figsize= [18,6])\n",
        "plt.imshow(spike_trains, extent=[0, total_length, num_neurons, 0], aspect=\"auto\")\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Neuron')\n",
        "plt.title('Raster plot of 20 Independent Poisson Neurons')\n",
        "plt.colorbar()\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Brian simulation for real neurons\n",
        "simulate networks with the Brian2 simulator"
      ],
      "metadata": {
        "id": "DTfhMv0O9bIR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xd8dRwTfqx1z",
        "outputId": "4a5b4068-c7ac-4858-b44a-4fc3785491ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: brian2 in /usr/local/lib/python3.10/dist-packages (2.6.0)\n",
            "Requirement already satisfied: numpy>=1.21 in /usr/local/lib/python3.10/dist-packages (from brian2) (1.25.2)\n",
            "Requirement already satisfied: cython>=0.29.21 in /usr/local/lib/python3.10/dist-packages (from brian2) (3.0.10)\n",
            "Requirement already satisfied: sympy>=1.2 in /usr/local/lib/python3.10/dist-packages (from brian2) (1.12)\n",
            "Requirement already satisfied: pyparsing in /usr/local/lib/python3.10/dist-packages (from brian2) (3.1.2)\n",
            "Requirement already satisfied: jinja2>=2.7 in /usr/local/lib/python3.10/dist-packages (from brian2) (3.1.4)\n",
            "Requirement already satisfied: setuptools>=61 in /usr/local/lib/python3.10/dist-packages (from brian2) (67.7.2)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from brian2) (24.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2>=2.7->brian2) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy>=1.2->brian2) (1.3.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (4.66.4)\n"
          ]
        }
      ],
      "source": [
        "!pip install brian2\n",
        "!pip install tqdm"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Place Cells spike trains on a linear track\n",
        "- Only partial of the neuron are place cells\n",
        "- Place cells are placed along linear track\n",
        "- This is the input to the Hopfield network."
      ],
      "metadata": {
        "id": "y1kpRI5e-sm0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "jJWxjT2Z7C48"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "outfield_rate = 0.1  # avg. firing rate outside place field [Hz]\n",
        "infield_rate = 5.0  # avg. in-field firing rate [Hz]\n",
        "t_max = 400.0  # [s]\n",
        "\n",
        "f_theta = 7.0  # theta osc. freq. [Hz]\n",
        "v_mice = 30.43567842  # [cm/s]\n",
        "l_route = 300.0  # circumference [cm]\n",
        "l_place_field = 30.0  # [cm]\n",
        "r = l_route / (2*np.pi)  # [cm]\n",
        "phi_PF_rad = l_place_field / r  # [rad]\n",
        "t_route = l_route / v_mice  # [s]\n",
        "w_mice = 2*np.pi / t_route  # angular velocity\n",
        "\n",
        "s = 47.0  # phase-locking (param of circular Gaussian)\n",
        "stdanimal = 0.146  # std (param of Gaussian, defined in [0,2*np.pi])\n",
        "\n",
        "def evaluate_lambda_t(t, phi_start, linear, phase0):\n",
        "    \"\"\"\n",
        "    Evaluates firing rate(t, x) = tuning_curve(x) * theta_modulation(t, x) at given time points\n",
        "    :param t: sample time points\n",
        "    :param phi_start: starting point of the place field (in rad)\n",
        "    :param linear: flag for circular vs. linear track -> slightly diff tuning curves\n",
        "    :param phase0: init. phase (used to calc. phase precession)\n",
        "    :return: lambda_t sampled at the given time points\n",
        "    \"\"\"\n",
        "\n",
        "    x = np.mod(w_mice * t, 2*np.pi)  # positions of the mice [rad]\n",
        "\n",
        "    if not linear:\n",
        "        tau_x = get_tuning_curve_circular(x, phi_start)\n",
        "    else:\n",
        "        tau_x = get_tuning_curve_linear(x, phi_start)\n",
        "\n",
        "    # theta modulation of firing rate + phase precession\n",
        "    phase = phase0 + 2*np.pi * f_theta * t\n",
        "    phase_shift = -np.pi / phi_PF_rad * (x - phi_start)\n",
        "    theta_mod = np.cos(phase - phase_shift)\n",
        "\n",
        "    lambda_t = tau_x * theta_mod\n",
        "    lambda_t[np.where(lambda_t < 0.0)] = 0.0\n",
        "\n",
        "    return lambda_t\n",
        "\n",
        "def get_tuning_curve_circular(spatial_points, phi_start):\n",
        "    \"\"\"\n",
        "    Calculates (not estimates) tuning curve (on a circle -> circular Gaussian function)\n",
        "    :param spatial_points: spatial points along the circle (in rad)\n",
        "    :param phi_start: starting point of the place field (in rad)\n",
        "    :return: tau: tuning curve of the place cell\n",
        "    \"\"\"\n",
        "\n",
        "    mid_PF = np.mod(phi_start + phi_PF_rad/2.0, 2*np.pi)\n",
        "    tau = 1.0/np.exp(s) * np.exp(s*np.cos(spatial_points - mid_PF))  # circular Gaussian\n",
        "\n",
        "    return tau\n",
        "\n",
        "\n",
        "def get_tuning_curve_linear(spatial_points, phi_start):\n",
        "    \"\"\"\n",
        "    Calculates (not estimates) tuning curve (Gaussian function)\n",
        "    :param spatial_points: spatial points along the track\n",
        "    :param phi_start: starting point of the place field\n",
        "    :return: tau: tuning curve of the place cell\n",
        "    \"\"\"\n",
        "\n",
        "    mid_PF = phi_start + phi_PF_rad/2.0\n",
        "    tau = np.exp(-np.power(spatial_points-mid_PF, 2) /(2*stdanimal**2))\n",
        "\n",
        "    return tau\n",
        "\n",
        "\n",
        "def refractoriness(spike_trains, ref_per=5e-3):\n",
        "    \"\"\"\n",
        "    Delete spikes (from generated train) which are too close to each other\n",
        "    :param spike_trains: list of lists representing individual spike trains\n",
        "    :param ref_per: refractory period (in sec)\n",
        "    :return spike_trains: same structure, but with some spikes deleted\n",
        "    \"\"\"\n",
        "\n",
        "    spike_trains_updated = []; count = 0\n",
        "    for single_spike_train in spike_trains:\n",
        "        tmp = np.diff(single_spike_train)  # calculate ISIs\n",
        "        idx = np.where(tmp < ref_per)[0] + 1\n",
        "        if idx.size:\n",
        "            count += idx.size\n",
        "            single_spike_train_updated = np.delete(single_spike_train, idx).tolist()  # delete spikes which are too close\n",
        "        else:\n",
        "            single_spike_train_updated = single_spike_train\n",
        "        spike_trains_updated.append(single_spike_train_updated)\n",
        "\n",
        "    print(\"%i spikes deleted becuse of too short refractory period\" % count)\n",
        "\n",
        "    return spike_trains_updated\n",
        "\n",
        "\n",
        "def _generate_exp_rand_numbers(lambda_, n_rnds, seed):\n",
        "    \"\"\"\n",
        "    MATLAB's random exponential number\n",
        "    :param lambda_: normalization (will be the rate of Poisson proc - see `hom_poisson()`)\n",
        "    :param n_rnds: number of random numbers to gerenerate\n",
        "    :param seed: seed for random number generation\n",
        "    :return: exponential random numbers\n",
        "    \"\"\"\n",
        "\n",
        "    np.random.seed(seed)\n",
        "    return -1.0 / lambda_ * np.log(np.random.rand(n_rnds))\n",
        "\n",
        "def hom_poisson(lambda_, n_rnds, t_max, seed):\n",
        "    \"\"\"\n",
        "    Generates Poisson process (interval times X_i = -ln(U_i)/lambda_, where lambda_ is the rate and U_i ~ Uniform(0,1))\n",
        "    :param lambda_: rate of the Poisson process\n",
        "    :param n_rnds: see `_generate_exp_rand_numbers()`\n",
        "    :param t_max: length of the generate Poisson process\n",
        "    :param seed: seed for random number generation (see `_generate_exp_rand_numbers()`)\n",
        "    :return: poisson_proc: np.array which represent a homogenos Poisson process\n",
        "    \"\"\"\n",
        "\n",
        "    rnd_isis = _generate_exp_rand_numbers(lambda_, n_rnds, seed)\n",
        "    poisson_proc = np.cumsum(rnd_isis)\n",
        "\n",
        "    assert poisson_proc[-1] > t_max, \"Spike train is too short, consider increasing `n_rnds`!\"\n",
        "    return poisson_proc[np.where(poisson_proc <= t_max)]\n",
        "\n",
        "def inhom_poisson(lambda_, t_max, phi_start, linear, seed, phase0=0.0):\n",
        "    \"\"\"\n",
        "    Generates a homogeneous Poisson process and converts it to inhomogeneous\n",
        "    via keeping only a subset of spikes based on the (time and space dependent) rate of the place cell (see `evaluate_lambda_t()`)\n",
        "    :param lambda_: rate of the hom. Poisson process (see `hom_poisson()`)\n",
        "    :param t_max: length of the generate Poisson process\n",
        "    :param phi_start: starting point of the place field (see `evaluate_lambda_t()`)\n",
        "    :param linear: flag for circular vs. linear track (see `evaluate_lambda_t()`)\n",
        "    :param seed: seed for random number generation\n",
        "    :param phase0: initial phase (see `evaluate_lambda_t()`)\n",
        "    :return: inhom_poisson_proc: inhomogenos Poisson process representing the spike train of a place cell\n",
        "    \"\"\"\n",
        "\n",
        "    poisson_proc = hom_poisson(lambda_, 10000, t_max, seed)  # hard coded 10000 works with 20Hz rate and 405sec spike train\n",
        "\n",
        "    # keep only a subset of spikes\n",
        "    lambda_t = evaluate_lambda_t(poisson_proc, phi_start, linear, phase0)\n",
        "    np.random.seed(seed)\n",
        "    inhom_poisson_proc = poisson_proc[np.where(lambda_t >= np.random.rand(poisson_proc.shape[0]))]\n",
        "\n",
        "    return inhom_poisson_proc\n",
        "\n",
        "\n",
        "def generate_spike_train(n_neurons, place_cell_ratio, linear, ordered=True, seed=12345):\n",
        "    \"\"\"\n",
        "    Generates hippocampal like spike trains (used later for learning the weights via STDP)\n",
        "    :param n_neurons: #{neurons}\n",
        "    :param place_cell_ratio: ratio of place cells in the whole population\n",
        "    :param linear: flag for linear vs. circular track\n",
        "    :param ordered: bool to order neuronIDs based on their place fields (used for teaching 2 environments - see `stdp_2nd_env.py`)\n",
        "    :param seed: starting seed for random number generation\n",
        "    :return: spike_trains - list of lists with indiviual neuron's spikes\n",
        "    \"\"\"\n",
        "\n",
        "    assert n_neurons >= 10, \"The assumptions made during the setup hold only for a reasonably big group of neurons\"\n",
        "\n",
        "    neuronIDs = np.arange(0, n_neurons)\n",
        "    # generate random neuronIDs being place cells and starting points for place fields\n",
        "    if ordered:\n",
        "        np.random.seed(seed)\n",
        "\n",
        "        if linear:\n",
        "            p_uniform = 1./n_neurons\n",
        "            tmp = (1 - 2*2*5*p_uniform)/(n_neurons-10)\n",
        "            p = np.concatenate([2*p_uniform*np.ones(5), tmp*np.ones(n_neurons-2*5), 2*p_uniform*np.ones(5)])  # slightly oversample (double prop.) the 2 ends (first and last 100 neurons) of the track\n",
        "            place_cells = np.sort(np.random.choice(neuronIDs, int(n_neurons*place_cell_ratio), p=p, replace=False), kind=\"mergsort\")\n",
        "        else:\n",
        "            place_cells = np.sort(np.random.choice(neuronIDs, int(n_neurons*place_cell_ratio), replace=False), kind=\"mergsort\")\n",
        "        phi_starts = np.sort(np.random.rand(n_neurons), kind=\"mergesort\")[place_cells] * 2*np.pi\n",
        "\n",
        "        if linear:\n",
        "            phi_starts -= 0.1*np.pi  # shift half a PF against boundary effects (mid_PFs will be in [0, 2*np.pi]...)\n",
        "\n",
        "    else:\n",
        "        np.random.seed(seed+1)\n",
        "\n",
        "        place_cells = np.random.choice(neuronIDs, int(n_neurons*place_cell_ratio), replace=False)\n",
        "        phi_starts = np.sort(np.random.rand(n_neurons)[place_cells], kind=\"mergesort\") * 2*np.pi\n",
        "\n",
        "        if linear:\n",
        "            phi_starts -= 0.1*np.pi  # shift half a PF against boundary effects (mid_PFs will be in [0, 2*np.pi]...)\n",
        "\n",
        "\n",
        "    place_fields = {neuron_id:phi_starts[i] for i, neuron_id in enumerate(place_cells)}\n",
        "    # save_place_fields(place_fields, pklf_name)\n",
        "\n",
        "    # generate spike trains\n",
        "    spike_trains = []\n",
        "    for neuron_id in tqdm(range(0, n_neurons)):\n",
        "        if neuron_id in place_fields:\n",
        "            spike_train = inhom_poisson(infield_rate, t_max, place_fields[neuron_id], linear, seed)\n",
        "        else:\n",
        "            spike_train = hom_poisson(outfield_rate, 100, t_max, seed)\n",
        "        spike_trains.append(spike_train)\n",
        "        seed += 1\n",
        "\n",
        "    return spike_trains, place_fields"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WRTSz-Y4FyNT",
        "outputId": "0aaf6226-e3a3-4918-dc9e-ea0e9ebb44e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 200/200 [00:00<00:00, 774.80it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3 spikes deleted becuse of too short refractory period\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "import os, pickle\n",
        "import numpy as np\n",
        "from brian2 import *\n",
        "from tqdm import tqdm  # prog\n",
        "\n",
        "n_neurons = 200\n",
        "place_cell_ratio = .5 #.25# 0.2\n",
        "linear = True\n",
        "\n",
        "\n",
        "spike_trainstmp, place_fields = generate_spike_train(n_neurons, place_cell_ratio, linear=True, ordered=True)\n",
        "spike_trainstmp = refractoriness(spike_trainstmp)  # clean spike train (based on refractory period)\n",
        "spike_trainstmp = np.asarray(spike_trainstmp, dtype=\"object\")\n",
        "\n",
        "# f_out = \"spike_trains_%.1f_linear.npz\"%place_cell_ratio if linear else \"spike_trains_%.1f.npz\"%place_cell_ratio; ordered = True\n",
        "# npzf_name = os.path.join(base_path, \"files\", f_out)\n",
        "# np.savez(npzf_name, spike_trains=spike_trainstmp)\n",
        "\n",
        "# the input used in the later session.\n",
        "spiking_neurons = 0 * np.ones_like(spike_trainstmp[0])\n",
        "spike_times = np.asarray(spike_trainstmp[0])\n",
        "for neuron_id in range(1, n_neurons):\n",
        "    tmp = neuron_id * np.ones_like(spike_trainstmp[neuron_id])\n",
        "    spiking_neurons = np.concatenate((spiking_neurons, tmp), axis=0)\n",
        "    spike_times = np.concatenate((spike_times, np.asarray(spike_trainstmp[neuron_id])), axis=0)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "id": "7NhDxlEB_Xys",
        "outputId": "8d534156-49d8-4d94-d1b7-bac5c9bb91fe"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize= [18,6])\n",
        "kk = 0.\n",
        "for xx in spike_trainstmp:\n",
        "    plt.plot(xx, kk*np.ones_like(xx), '.', markersize=2)\n",
        "    kk+=1.\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generate random connectivity matrix\n"
      ],
      "metadata": {
        "id": "oY6emE6AL_hj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.sparse import coo_matrix\n",
        "connection_prob_PC = .1\n",
        "weightmx = np.random.rand(n_neurons, n_neurons)\n",
        "weightmx[weightmx>connection_prob_PC] = 0.0\n",
        "np.fill_diagonal(weightmx, 0.0)  # just to make sure\n",
        "sparse_weightmx = coo_matrix(weightmx)"
      ],
      "metadata": {
        "id": "ErRv6IvcLAW7"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Run simulation with a large network contains nclus non-overlaping assemblies.\n",
        "- nclu = 10\n",
        "- initial connectivity matrix, not quite useful in this case: intsparse_weightmx=sparse_weightmx,\n",
        "- intra-assembly connectivity strength: intweight=50.\n",
        "- extra input strength: extweight = 50.\n",
        "-  to set the poisson noises by external stimuli:\n",
        "```\n",
        "rate = 3 * Hz\n",
        "C_ext = 800  \n",
        "    ```\n",
        "\n",
        "if the entra input strength is larger, then the neuron will fire more following the inputs at exact position, you'll see similar P cell firing pattern as the input pattern.\n",
        "\n",
        "if the intra input strength is larger, then the neuron will fire when the assembly is activated, performing pattern completion. You'll see more reliably assembly patterns.\n",
        "\n",
        "- You can play with the assembly by defining different connectivity matrix weights"
      ],
      "metadata": {
        "id": "d6tYoHToND6y"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RbSb7V6PTzI_",
        "outputId": "889d0350-3a3a-4b0a-af4c-febdc06fd12b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting simulation at t=0. s for a duration of 50. s\n",
            "1.7427 s (3%) simulated in 10s, estimated 4m 37s remaining.\n",
            "4.0914 s (8%) simulated in 20s, estimated 3m 44s remaining.\n",
            "6.1288 s (12%) simulated in 30s, estimated 3m 35s remaining.\n",
            "10.423 s (20%) simulated in 40s, estimated 2m 32s remaining.\n",
            "14.2468 s (28%) simulated in 50s, estimated 2m 5s remaining.\n",
            "17.9204 s (35%) simulated in 1m 0s, estimated 1m 47s remaining.\n",
            "21.0246 s (42%) simulated in 1m 10s, estimated 1m 36s remaining.\n",
            "24.0643 s (48%) simulated in 1m 20s, estimated 1m 26s remaining.\n",
            "26.9973 s (53%) simulated in 1m 30s, estimated 1m 17s remaining.\n",
            "30.0566 s (60%) simulated in 1m 40s, estimated 1m 6s remaining.\n",
            "32.6583 s (65%) simulated in 1m 50s, estimated 58s remaining.\n",
            "35.2086 s (70%) simulated in 2m 0s, estimated 50s remaining.\n",
            "37.9794 s (75%) simulated in 2m 10s, estimated 41s remaining.\n",
            "40.7138 s (81%) simulated in 2m 20s, estimated 32s remaining.\n",
            "43.5781 s (87%) simulated in 2m 30s, estimated 22s remaining.\n",
            "46.1921 s (92%) simulated in 2m 40s, estimated 13s remaining.\n",
            "48.7858 s (97%) simulated in 2m 50s, estimated 4s remaining.\n",
            "50. s (100%) simulated in 2m 54s\n"
          ]
        }
      ],
      "source": [
        "import random\n",
        "import copy as cp\n",
        "\n",
        "def runsimulation(nPC, inputneurons,inputspk,nclu = 10,intsparse_weightmx=sparse_weightmx,intweight=60.,extweight = 50.):\n",
        "    # voltage\n",
        "    N_E = nPC\n",
        "    N_I = 10 # 100\n",
        "    V_L = -70. * mV\n",
        "    V_thr = -50. * mV\n",
        "    V_reset = -55. * mV\n",
        "    V_E = 0. * mV\n",
        "    V_I = -70. * mV\n",
        "\n",
        "    # membrane capacitance\n",
        "    C_m_E = 0.5 * nF\n",
        "    C_m_I = 0.2 * nF\n",
        "\n",
        "    # membrane leak\n",
        "    g_m_E = 25. * nS\n",
        "    g_m_I = 20. * nS\n",
        "\n",
        "    # refractory period\n",
        "    tau_rp_E = 2. * ms\n",
        "    tau_rp_I = 1. * ms\n",
        "\n",
        "    # external stimuli\n",
        "    rate = 3 * Hz\n",
        "    C_ext = 800 # 800\n",
        "\n",
        "    # synapses\n",
        "    C_E = N_E\n",
        "    C_I = N_I\n",
        "\n",
        "    # AMPA (excitatory)\n",
        "    g_AMPA_ext_E = 2.08 * nS\n",
        "    g_AMPA_rec_E = 0.104 * nS * 800. / N_E\n",
        "    g_AMPA_ext_I = 1.62 * nS\n",
        "    g_AMPA_rec_I = 0.081 * nS * 800. / N_E\n",
        "    tau_AMPA = 2. * ms\n",
        "\n",
        "    # NMDA (excitatory)\n",
        "    g_NMDA_E = 0.327 * nS * 800. / N_E\n",
        "    g_NMDA_I = 0.258 * nS * 800. / N_E\n",
        "    tau_NMDA_rise = 2. * ms\n",
        "    tau_NMDA_decay = 100. * ms\n",
        "    alpha = 0.5 / ms\n",
        "    Mg2 = 1.\n",
        "\n",
        "    # GABAergic (inhibitory)\n",
        "    g_GABA_E = 1.25 * nS * 200. / N_I *4.\n",
        "    g_GABA_I = 0.973 * nS * 200. / N_I\n",
        "    tau_GABA = 10. * ms\n",
        "\n",
        "    # subpopulations\n",
        "    f = 0.1\n",
        "    p = 5\n",
        "    N_sub = int(N_E * f)\n",
        "    N_non = int(N_E * (1. - f * p))\n",
        "    w_plus = 2.1\n",
        "    w_minus = 1. - f * (w_plus - 1.) / (1. - f)\n",
        "\n",
        "    # modeling\n",
        "    eqs_E = '''\n",
        "    dv / dt = (- g_m_E * (v - V_L) - I_syn) / C_m_E : volt (unless refractory)\n",
        "\n",
        "    I_syn = I_AMPA_ext + I_AMPA_rec + I_NMDA_rec + I_GABA_rec : amp\n",
        "\n",
        "    I_AMPA_ext = g_AMPA_ext_E * (v - V_E) * s_AMPA_ext : amp\n",
        "    I_AMPA_rec = g_AMPA_rec_E * (v - V_E) * 1 * s_AMPA : amp\n",
        "    ds_AMPA_ext / dt = - s_AMPA_ext / tau_AMPA : 1\n",
        "    ds_AMPA / dt = - s_AMPA / tau_AMPA : 1\n",
        "\n",
        "    I_NMDA_rec = g_NMDA_E * (v - V_E) / (1 + Mg2 * exp(-0.062 * v / mV) / 3.57) * s_NMDA_tot : amp\n",
        "    s_NMDA_tot : 1\n",
        "\n",
        "    I_GABA_rec = g_GABA_E * (v - V_I) * s_GABA : amp\n",
        "    ds_GABA / dt = - s_GABA / tau_GABA : 1\n",
        "\n",
        "    '''\n",
        "\n",
        "    eqs_I = '''\n",
        "    dv / dt = (- g_m_I * (v - V_L) - I_syn) / C_m_I : volt (unless refractory)\n",
        "\n",
        "    I_syn = I_AMPA_ext + I_AMPA_rec + I_NMDA_rec + I_GABA_rec : amp\n",
        "\n",
        "    I_AMPA_ext = g_AMPA_ext_I * (v - V_E) * s_AMPA_ext : amp\n",
        "    I_AMPA_rec = g_AMPA_rec_I * (v - V_E) * 1 * s_AMPA : amp\n",
        "    ds_AMPA_ext / dt = - s_AMPA_ext / tau_AMPA : 1\n",
        "    ds_AMPA / dt = - s_AMPA / tau_AMPA : 1\n",
        "\n",
        "    I_NMDA_rec = g_NMDA_I * (v - V_E) / (1 + Mg2 * exp(-0.062 * v / mV) / 3.57) * s_NMDA_tot : amp\n",
        "    s_NMDA_tot : 1\n",
        "\n",
        "    I_GABA_rec = g_GABA_I * (v - V_I) * s_GABA : amp\n",
        "    ds_GABA / dt = - s_GABA / tau_GABA : 1\n",
        "    '''\n",
        "\n",
        "    P_E = NeuronGroup(N_E, eqs_E, threshold='v > V_thr', reset='v = V_reset', refractory=tau_rp_E, method='euler')\n",
        "    P_E.v = V_L\n",
        "    P_I = NeuronGroup(N_I, eqs_I, threshold='v > V_thr', reset='v = V_reset', refractory=tau_rp_I, method='euler')\n",
        "    P_I.v = V_L\n",
        "\n",
        "\n",
        "    eqs_glut = '''\n",
        "    s_NMDA_tot_post = w * s_NMDA : 1 (summed)\n",
        "    ds_NMDA / dt = - s_NMDA / tau_NMDA_decay + alpha * x * (1 - s_NMDA) : 1 (clock-driven)\n",
        "    dx / dt = - x / tau_NMDA_rise : 1 (clock-driven)\n",
        "    w : 1\n",
        "    '''\n",
        "\n",
        "\n",
        "    eqs_pre_glut = '''\n",
        "    s_AMPA += w\n",
        "    x += 1\n",
        "    '''\n",
        "\n",
        "    eqs_pre_gaba = '''\n",
        "    s_GABA += 1\n",
        "    '''\n",
        "\n",
        "    eqs_pre_ext = '''\n",
        "    s_AMPA_ext += extweight\n",
        "    '''\n",
        "    # 1\n",
        "\n",
        "    # E to E\n",
        "    C_E_E = Synapses(P_E, P_E, model=eqs_glut, on_pre=eqs_pre_glut, method='euler')\n",
        "\n",
        "    C_E_E.connect(i=intsparse_weightmx.row, j=intsparse_weightmx.col)\n",
        "    C_E_E.w=intsparse_weightmx.data*.5\n",
        "\n",
        "\n",
        "    nclut = int(floor(nPC/nclu))\n",
        "    for xclu in range(nclu):\n",
        "        # print((x*nclut))\n",
        "        C_E_E.w[(xclu*nclut):((xclu+1)*nclut-1),(xclu*nclut):((xclu+1)*nclut-1)]+=intweight # *=50.\n",
        "\n",
        "    for n in range(nPC):\n",
        "        C_E_E.w[n,n]=0.0\n",
        "\n",
        "    # E to I\n",
        "    C_E_I = Synapses(P_E, P_I, model=eqs_glut, on_pre=eqs_pre_glut, method='euler')\n",
        "    C_E_I.connect()\n",
        "    C_E_I.w[:] = 1\n",
        "\n",
        "    # I to I\n",
        "    C_I_I = Synapses(P_I, P_I, on_pre=eqs_pre_gaba, method='euler')\n",
        "    C_I_I.connect('i != j')\n",
        "\n",
        "    # I to E\n",
        "    C_I_E = Synapses(P_I, P_E, on_pre=eqs_pre_gaba, method='euler')\n",
        "    C_I_E.connect()\n",
        "\n",
        "    # external noise\n",
        "    C_P_E = PoissonInput(P_E, 's_AMPA_ext', C_ext, rate, '1')\n",
        "    C_P_I = PoissonInput(P_I, 's_AMPA_ext', C_ext, rate, '1')\n",
        "\n",
        "\n",
        "    INPC = SpikeGeneratorGroup(nPC, inputneurons,inputspk)\n",
        "    feedforward = Synapses(INPC, P_E, on_pre=eqs_pre_ext)\n",
        "    feedforward.connect(j='i')\n",
        "\n",
        "    SM_PC = SpikeMonitor(P_E)\n",
        "    # state_mon = StateMonitor(P, 'v', record=True)\n",
        "    SM_PV = SpikeMonitor(P_I, 'v', record=True)\n",
        "    run(50*second,report=\"text\")\n",
        "    return SM_PC, SM_PV\n",
        "    # return SM_PC,state_mon\n",
        "\n",
        "spiking_neuronstmp_ = cp.deepcopy(spiking_neurons)\n",
        "random.shuffle(spiking_neuronstmp_)\n",
        "# SM_PC,SM_PV= runsimulation(n_neurons,spiking_neuronstmp_,spike_times*second)\n",
        "use_spikeneurons = spiking_neuronstmp_[::2] # spiking_neurons[::2]\n",
        "use_spiketimes = spike_times[::2]\n",
        "SM_PC, SM_PV= runsimulation(n_neurons,use_spikeneurons,use_spiketimes*second)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hopfield network will \"complete\" the pattern even if you just see part of it. To play with it, you can:\n",
        "- shuffle the spike trains\n",
        "- change the weight of inter cluster connection\n",
        "- change the input strength\n",
        "To see the \"blocks\"\n"
      ],
      "metadata": {
        "id": "HwqNw8jLAyI-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "7O3ju5QYm0Ky",
        "outputId": "1983bb8d-4ffd-46fc-c977-a81516609864"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "spiketrainsP=SM_PC.spike_trains()\n",
        "plt.figure(figsize= [18,6])\n",
        "plt.subplot(311)\n",
        "kk = 0\n",
        "upt = 50. # range of time you want to see.\n",
        "for xx in use_spiketimes:\n",
        "    plt.plot(xx[xx<upt], use_spikeneurons[kk]*np.ones_like(xx[xx<upt]), '.', markersize=2)\n",
        "    kk+=1\n",
        "plt.ylabel('Input patterns')\n",
        "plt.subplot(312)\n",
        "kk = 0.\n",
        "for xx in spiketrainsP:\n",
        "    tmp = np.array(spiketrainsP[xx])\n",
        "    plt.plot(tmp[tmp<upt], kk*np.ones_like(tmp[tmp<upt]), '.', markersize=2)\n",
        "    kk+=1.\n",
        "plt.ylabel('P cells response')\n",
        "plt.subplot(313)\n",
        "spiketrainsP=SM_PV.spike_trains()\n",
        "kk = 0.\n",
        "for xx in spiketrainsP:\n",
        "    tmp = np.array(spiketrainsP[xx])\n",
        "    plt.plot(tmp[tmp<upt], kk*np.ones_like(tmp[tmp<upt]), '.', markersize=2)\n",
        "    kk+=1.\n",
        "plt.ylabel('Inter neurons')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Variations"
      ],
      "metadata": {
        "id": "qYoRLKKzMkYR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generate connectivity to memorize current input sequences.\n",
        "- generate the stdp trained weight matrix. it could be used to train a sequence generator/ring attractor.\n",
        "- Not used in the simple hebbian attracor."
      ],
      "metadata": {
        "id": "ar4iv1Bw8b_3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xqwrpi6CSM68",
        "outputId": "42dc181c-99de-4fcf-950f-8634b5a34389"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting simulation at t=0. s for a duration of 20. s\n",
            "20. s (100%) simulated in 9s\n"
          ]
        }
      ],
      "source": [
        "from scipy.sparse import coo_matrix\n",
        "connection_prob_PC = .1\n",
        "# w_init = .01\n",
        "# taup = taum = 20 * ms\n",
        "# Ap = 0.01\n",
        "# Am = -Ap\n",
        "# wmax = 4e-8  # S\n",
        "taup = taum = 62.5 * ms\n",
        "Ap = Am = 4e-3\n",
        "wmax = 2e2 # 2e-8  # S\n",
        "scale_factor = 0.62\n",
        "w_init = 1e-6  # S\n",
        "Ap *= wmax\n",
        "Am *= wmax  #\n",
        "\n",
        "\n",
        "def generate_wmx(nPCs,spiking_neurons,spike_times):\n",
        "    PC = SpikeGeneratorGroup(nPCs, spiking_neurons, spike_times)\n",
        "    STDP = Synapses(PC, PC,\n",
        "              \"\"\"\n",
        "              w : 1\n",
        "              dA_presyn/dt = -A_presyn/taup : 1 (event-driven)\n",
        "              dA_postsyn/dt = -A_postsyn/taum : 1 (event-driven)\n",
        "              \"\"\",\n",
        "              on_pre=\"\"\"\n",
        "              A_presyn += Ap\n",
        "              w = clip(w + A_postsyn, 0, wmax)\n",
        "              \"\"\",\n",
        "              on_post=\"\"\"\n",
        "              A_postsyn += Am\n",
        "              w = clip(w + A_presyn, 0, wmax)\n",
        "              \"\"\")\n",
        "    STDP.connect(condition=\"i!=j\", p=connection_prob_PC)\n",
        "    STDP.w = w_init\n",
        "    run(20*second, report=\"text\")\n",
        "\n",
        "    weightmx = np.zeros((nPCs, nPCs))\n",
        "    weightmx[STDP.i[:], STDP.j[:]] = STDP.w[:]\n",
        "    np.fill_diagonal(weightmx, 0.0)  # just to make sure\n",
        "    sparse_weightmx = coo_matrix(weightmx)\n",
        "    return sparse_weightmx\n",
        "\n",
        "sparse_weightmx = generate_wmx(n_neurons,spiking_neurons,spike_times*second)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wMoLPYAZUios"
      },
      "source": [
        "## pi-VAE and CEBRA  nonlinear embeddings\n",
        "\n",
        "need to download the pi-VAE file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nfh-Iw8VWCTz",
        "outputId": "75738f92-0731-408c-ac6a-ddef9b144bd0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: keras in /usr/local/lib/python3.10/dist-packages (2.15.0)\n",
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.15.0)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.3.25)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.5.4)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.9.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (18.1.1)\n",
            "Requirement already satisfied: ml-dtypes~=0.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.25.2)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.3.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.0)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.20.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow) (67.7.2)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.11.0)\n",
            "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.14.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.37.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.63.0)\n",
            "Requirement already satisfied: tensorboard<2.16,>=2.15 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.15.2)\n",
            "Requirement already satisfied: tensorflow-estimator<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.15.0)\n",
            "Requirement already satisfied: keras<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.15.0)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow) (0.43.0)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (2.27.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (1.2.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (3.6)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (2.31.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (3.0.3)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (5.3.3)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (0.4.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (1.3.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (2024.2.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.16,>=2.15->tensorflow) (2.1.5)\n",
            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (0.6.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (3.2.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install keras\n",
        "!pip install tensorflow\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f_6RMsZDXpV5",
        "outputId": "255b875e-f946-4fde-e853-c99f563698df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 1s 10ms/step\n"
          ]
        }
      ],
      "source": [
        "from pi_vae import *\n",
        "\n",
        "num_neurons = 20\n",
        "total_length = 80\n",
        "n_cls = 2\n",
        "\n",
        "z_true, u_true, mean_true, lam_true = simulate_data(total_length, n_cls, num_neurons);\n",
        "np.random.seed(777)\n",
        "x_true = np.random.poisson(lam_true)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "x3RTWig0cXT5",
        "outputId": "28013c9a-1bcc-424f-9d6c-baba9223fa6c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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933oZ1338l+hTzn8XFOyy3n0n0HIo8XuqaD200UcFhFaANUWUrmzXvl6oE443ahijOoTxAODRSYaq09q565vfKyPzwhSjZ0xnGhkQ8SCGCNFakH+6f063B3+TbgefJufJRqBGgxxMHcLIXCSi9UooitXQBj2vAoHgwkCNBrmOFqWSBGx/DX1N5FpnjaVgBItr+R2yMqqI1q1JojXnWjsWrfVOazmmDewRCC4xRuapYDMRcLER5zzrssZEYUAVrXsyPDqXPPhh4Nu/BZy5Lz+PLxAINjXHRxYRl4ETI4v5PhRnTJ8FTv2Q7t/8l6nfr+FM68ENOySVwAjdFlUCxVWJ32s6QLcblWudPISRUZ3W5xzpNbIso29aEa0XhMlBj379PbMcEnqNAUK0FuSX9QVg6Fm6f9OHye0XWtq4VuOJ43RrFA3CqKL1SM4PJ1dEYnHc/h9P4PZPPIloLD+t6QKBYBMyqMuzZkSuddZhUQsABty4MbMAZ1q3VZckfF11WjsV3OYH6dZToDzAsUwOTyC4IFkJRbGgFKQm3LTKL0/RbXlT4tfrdtBtvpzWvDYffTE/jy8QCDYtkVgcsyshAMDw/AUmQD7+cQAysOeNmhCsh53WgVEgGt7II9NFg3Skfk8VrTco15qHMDYlidZVnUBBKXWq680LaZheDmE1THMfxhbWEYuLLh4m2TQiIkJSEaK1IL/0/pIcWvW7gdptQNcN9PWBxzfm8a2GMDLcnnMBO63HF9cxHghibHHd3aZKIBBcfMRjwPARut9xnfb1rpsAjw+Y600ctidwjb4VMh9Oa1mW1Uxr/SBG/b9dZ1qzM18MYxRcgugHKGXktOY8ayaf8SCrs8DSGN2f6bb+WYFAcMkxvRxSk4MuKNF68pTSPSKlZlkzpfVAQQkAeeP3/kZ51kzjfrrdiGGMsqw5upM1Eo8HaNjj+FjYZQ0A0bjs7np5kcKiNQ9GHxQRISkI0VqQXzgaZOcddNt1E91uxDDGeMz8hKyHq50XsGg9qttUjTltARcIBBcnU6eAUICGrOidFEUVQPur6L5wW2eFUV0rZD7y6uZWwwhG4pAkoLmqKOF7rjKtI+vAsjJ0bv9b6HZciNaCSw/9Z3syEETcqXtsWRlIVd6Y+HUWred6gfgGd8ixoQMQorVAIEhhUic4jlxIovUTH6fbfW/WhhsmI0m6XOvBjTgqjUUlR9vQaa2I1jPduXeALwzS/sBbCNTvSv2+mmt92vafTM4+F7nWRCQWx+QSmUYOd9YAEKK1EUK0FuSPWAToUTJTd/0K3W69mW5HXqBNcS6Z6wUia1RNrd1u/nNc7QxcuKK13gmkvy8QCC5huP17yzWA15f4PTXXWojW2UC/qRueW9vwmCY+7zeUF6LQ5034HovY44vrkO0OXeONXGEFsP21dH++nyK/BIINYDIQxNAm2NjpP9uRmIzZ1ZCzP2DmtK7uoOidyJrmet4o9Pn0gWEaDiYQCAQKkwHtPHfBOK0nTgBnfwpyWRtkWevhoYcLG9xtaOW0rmynrOt4BJg5l9vj4GtAwx7AW5D6fdX17US0Trxei1xrYmopiLgM+L0eXNlRDQAYzNPsm82MEK0F+WP4OarildQCbVfR12q3A+UtlJPEbeu5ImEqrtf856qUeJBgAFhfzO0x5Qi9E0g4rS8B5vuBL79BKwoJBEYMGeRZMyxEDjwJRB2KMIIU9Ju6aFxO6H7ZCPjxkvOsAaClkpzWq+EYltZtDn/haJCaLqCkRtvgbdQ8CsElTTwu482feQav/9RTWApG8nosyZ/lCacxOysmmdbeAqBmK93f6IgQvdM6H48vEGwSTo4GcNO/PoafnhjP96FsKvTRDlNLIQQjsTwejU0eV1zWB37D2D2sJ19O6wV2WhuI1pIENCq51rmOCFHjUw8af59d6tPOndYlftJcLiiHfg7haL7mqiJsrS8FAAyKTOsUhGgtyB/dD9LtjtdporEkAVuViJCBHEeEqCfkQ9Y/5y8lYR3QpvpeYIwKp/WlxXOfAYaeBh74vxvfViy4MJBlzWltJFo3HQDKGsnlxz8ncM1I0nl3o3OtxxaN86wBoNjvRU2pH4CDYYycdc6iWusVygOJiJBs8mzvLH7rC8+hd3o534eyqRiYW8VEIIi1cAz9eRpsyqSI1k5zOs2c1oBuGGOPiyPLAF4f+5QoIRERIrhEeejMJIbm1vDj4xvc7bDJmUyaj7TpBcixl4DuBwDJA9z04fQ/n7d4EAunNaBFhOR6GKPZEEamQRGtF4fJ1GcDvlZft400lU3/ntkgOJqvtaoYnbUkWm+GLrLNhhCtBflBluniAQC7Xp/4vY3KtbYzhJHhi8fiBSpa69zVwml9kSPLwPlf0P2FAaD/0fwej2BzMtMNrM0BvmKg5fLU70uS5rYWESEZoR+CuLe5AgDQv8Gitea0ThWtAaBFFxFiC3Zas8O6RYjWueAbzw/hSP887ntZuPz0nBrTNsn5bk3nFudSxT3meKApZ1qXNaZ+z8UwxrVwFP94/xkcG3IZ1RNc0j7fvD6fFaK14NKEr53C+ZjIxFLieS6j8/DaPBBxeN50CrusD/6mVgy0Ih+idWgZWJ+n+9zlnQzHcugjnHIB/30zjaSkBqhopfvTZ9P+ufVwTNUfbtrVACDVzHGpws9Li060nloKYS1ss/PxEkGI1oL8MHueBDWvH9h2a+L32Gk9cTx3cRzxuDPRulK5eFygwxjHxCDGS4fps4n56y98KX/Hwpx7IPdxPwJnDD1Nt+2HAZ/f+GeEaJ0VZlZCCEbi8EjAq3fUAQAGZjc2I5avAa1monWlw2GMCyZOazGMMasMzpIQMCQcSQnoRet8u7VY1LpCyaJ05LSORYDVWbpv6LR2Llo/eGoSX3p6AP/8c5eZpzygvLId2HId3RdOa4Edpk4DnzoEnPhuvo8ka/C1c3huDTGnQ1YvYqYUp7XfS1KSa9F6bR745AHg05cDY8eydXiJjL4I9PwCkLzAjf/X3u/oRWu7sz4yhY1xRVWUXW1Eky4eJFfHtTylxFZJ2sBFI9RhjOld3/3Kmre6pAAHW+n/Ld/X7s2CXrSuLClAVQlliA+JQlkCQrQW5Ifun9Nt5w1AYVni9ypagNodgBwHBp/OzeMvDAChJfOpuMmoTus8iNZH/wf45EFgttfVr0d1U2kBOjk6nm4vuHA4r8Tu1O/R/s0Zaflg9EXgO78NfOMtuXdSCOxjFQ3CbL2ZWilnzl2wXSabAZ6Q3lxZjJ2N5QA2Ph7EKtMaoMUyAIzZdYnqM60BKv5KHoo6WJrI6FgvCtYXgR+8G3jle67/hCzLaovosGgVTeDU2JJ6fziPG7ulYASBdcrUvrqzBgAwHnBwnVuZBiADHp8WQ6dHFa3tx4OweHRucsn+YFU96ryXg9r6ONdDvwQXBy99nfZXz/1Xvo8ka3CXVDhpL3WpM6Gc5y5rJwHStWg99hIQXgGWx4H/fT1w/NvZOkSNxz5Gt5f9NlC7zd7vVG0BINGxrc1l/5iMSBcNAgD1u0l8X18AlnLUgcUu67qdFJFqBkeETJ1J+yd5COO2+jJsqaF16PRyBlno6wsXzb5Eiwehjkd2W4thjIkI0VqQH1i0To4GYXKda82L8sZ9xlNxk6nqoNtAHkTrl74KLA4Br7hzLkwEgojFZfi9HkgSEI7GnU+3F1w4cDTI1X9AoiNk4NiX83c8z/4n3YZXaPhqvjn+beB77wTCl3AFW5aBQYshjExJDdB2mO4Lt7Vr2E3SVl2MrjpajA5kksN75ieOJrbLsqw6OYwyrQEtHsSWSzQW0TYL7LT2l9JmChBuawA48hng1A+AR/7e9Z+YWw1jNUwbOuG01pBlGafGN0c8CLswq0sKsL2BDBgTTrrZ9NEgHoMtWd12ul2ZtJ0byse0FIxietnFWk/fFs6f6YVBUXQWpIeHO0+eJAftBU44mihUDwkRCQANwp1SnpfDSrHOtWuWo4e8fiAWAu57H/DgR4CY82gEwyLd8PNA3y+pMHjjn9v/YwVFZKIDNi4ixI5oXVCkFTNzNYwx3RBGhqNKbKxH+5UhjNvqy1BVUoCyQh8ArSjkmG+8BfivwxdsB7yecZ3TGgA6a0nUF5FEiQjRWrDxrM4Boy/Q/Z13GP9MrnOtnUSDAFq21EafHONxYEZpCx1xF68wqmsLbywnYUIMY7xI0X+2drwOOPweuv/S1/Kz4ZwfAM7+RPt3X57zteMx4Bd/BZy5Dzj/8/weSz6Z7ycRxOsH2q6y/lkREZIxvJlrrynBVkW0Hg8EsR524TAZOQp87x3AD37f9q8E1iNYCdEG0DzT2kE8yOIwIMcoD72sSfu6GMZIhFeBF75I95fGXDvP9YN4Ftc0R++lzvD8GpaD0YR/5wv9Z7upktZXyQPKLFGHMDYZf7+oUvuM2ey200fAdU+6GOCpFyzKGqhVXY4Dc+66/QSXCMElnYgmAwNP5vVwssFkIAh9Y6ooHhKzqyFE4zI8EnClEovk+jzM0UPX/Yk2IPHIZ4Bv/LqjwscT52dw8P97CD85keQ+flxxWR+6S+sMswtHhPDg6VyzOJT4uGZwREiucq313TZWcDzI9Jm0USXstN5aXwpJktS1KHciOiIwRlEy0fULfm8iy7KqyaiidZ0YxmiEEK0FG0/PQ7QAbjxgPmig89UAJKrA5qLV2LFonad4kMUhOikDwOgxV5Vn3sC0VRereaYi1/oipfeRxM/WzjuAijZqbTtz38Yfz5HP0vEU0vA59D228cegZ+K4NuTExuCQixaOBmm9EigwFjFVWLTufwKIhnN7XBcpPKitvboE1aV+Na9u0M2CdPApup3pBiL2zuNcuKwrK0RRgdfwZzTR2obgxhu46s5EdygPY7zUndYvf1M7zwDA2Iuu/kxynqHIfyQ4GqSlUusOCEfjeTkW/YBT/gxNLYfsZ9+uKE5rozxrhoeG2cy11q/vzk85FK0j65qI1HwZDeQVESECO4y8QOs95iIQrZNdoK6u2RchUwHq4KgvL8TWeuowGZ5fcxdHxOe1+j3ALX8FvO3rQEEpdVp/4WZg0p6b+KHTk1gORvHQ6Unti4PPAP2PA54CZy5rZqOHMdpxWgNAEw9jzJHTWu22SSNa1+2g5za0BASsozr6pjWnNUCFXsBlsYM7OoDcxchuEEvBqNpR16o6rZWOTNHZkYAQrQUbT/cDdGsWDQJQWzoLytle+Miyc9GaBzGuL9B0341Cv0mIrALT9lvCGV50tVYVqydE4bS+SOE8652vo1uvD7jqXXT/6AYPZFybB17+Ot2/8xN0O3WSBnzki95favcvadGao0GuS/+zzYeAkjogvKy5+AWOYCfJllo6/6oRIW4WpKMsgMq2c25H0wxhBLRBjJNLQURjaQTA5CGMjN5pvVGDizYbsSjwnBKJVEwONO01c0Zya6gYykNwNMhNuxpQ6PMgLjsYIJpl9FnxdWWF8HkkxOIyppdtuq05HsTMaQ04GsYYj8uY0BWeeqYcDnydOkNdFKX1mpCuitZiGKPAgmGlGF7WSLe5infcQEaT9kpDs+IcDGgxYk2VtK+UJCAYiWNmxUUcEZ9X6pXz3N43An/wCAnGi0PA/9wGnL4v7Z/hgkLCdfLxe+j2inekF4KNqFac2ZtNtFZjOXIgWgcD2v9vOqe1t0C7PlhEhMTjsrre3abEaHGutatiPJs3ABKtM1hvDsyuYjmYYRdbPE6dUC6Og9cutaV+1VTSocSDuFrzLU/SMNyH/tb5725yhGgt2FiiIS0iYJdJNAiTq1zrwCi5oDw+66m4eooqqEUS2Njg/2Rny4hz0UjvBMrYab0yDXz7t4GeC7sd56IkFtFEWX3szhXvpEr46FFg/PjGHc+L/wtE1sj1vf8tWoGo//GNO4Zk9G1kQrS2J1p7PMD219D9nodzd0wXMXqnNZCBaC3LiYUDh85Ls2gQgBxTmuCWZuOZPISRadhHkTPBRe1nLjXO3Ecbz5Ja4Oa/oq+NHXP1p5KHLw7NC9cNAJwaI9H6QGuluvHNV0TIqPrZLobXI6GxgtzftjoWAC0epCw7ovXsSghhXdHp/LRDk8XEcbptOkgua0DLtRZOa4EVQ8rMkuv+hIbyzvVSG/8FzGjSLAjhtCY457upohB+n0ctejsWIFdnla4kCajdoX29cS/wnseArbfQPuL77wR++VESB03gOSGDc6vk+B54ksRNrx+44f84Oy5m0zqtlXiQuT6KI8smkyfptrKdDITpYB3FQkCfWApiPRJDgVdCu7IO5dsRN5nWgzqn9cqU6+iqgdlVvObfH8d7v+Zujaby7KeB/7qSZpA5JDkaBND2CJNLLmIET/2QjCUvffWiM48I0VqwsQw+TQPZyhqB5sutf1afa53NDx4vyhv2AL5C+7+Xj4iQaWWTwPEKI887/hNjOidQxk7rY18hp/wv/97d7wtyx/ARIBQgVyw7HgHKpNz7Jrp/9IsbcyzREPDCF+j+de+nze+2W+nf+cq1Xl8g4Z6Z77cdr3BRsThC5zDJC7RfY+931FzrX1r/nCCFSCyuOim4HZJzrfudDmNcGARWZ7R/2xStWVhrMxnCCABej6Rm8qYdxmgmWvv82mZq/GVbx3ZRIcvAM5+i+1f/oRJzBnou4s7zy9lpvauxHAAwLJzWNIRxE4nWI7r1FeBwoCmgdR5ZOq05HiR9ZwWLbB5Fb+6dWnHWsq8fwsgIp7UgHZGgFoO08/VAi7K/u8Dd1nztvH57LYAMIjA2I+FV13vrCSW3v1kRq9tr6NbxeZjPKVXtgL8k8XslNcBdPwCufT/9+6l/A77z24YDaYORGMaVY1oORrGwGgYeU1zWV74LqGxzdlyMKlpvQKZ1aFmLFas0iU5lyhqUjgaZumOyyYTBNcAKVbQ2d1pzNEhHbSl8XpIe21WntcN92NIEMN8HQNKc4HrntQNOjCwiLgMnRhcz+1yf+iHdvvI9x786HmDRukj9WlWJH5XFFCPo2KzQ8xDdBgNaRvpFghCtBRtLtzL8bOcdxpPS9Wy5liqkS6NUTcwWTqNBGBat0+Q2ZZUZxQ164K1060K0Hl1U4kGy4bTWTwZfmXb3NwS5gaNBdtwOeJJya69WBjKe/AGJt7nm5Pep+l3eAuz7dfqaXrTOx6K//3HKW6zbRcI+5EtzE8551s2XAYXl9n5n260AJIp3ycWMgYuYiUUa5OT3eVBfRkXSrjpqjxyYddi6nxwzYfP9q++2sYKdHmPpXKKcaZ0cDwJoudaX4jDG/sdJ9CsooXNu/S7AX0aFehcuVR7Cc8OOOgD5HTi4WRgPBLGwFoHPI2FnU5lu45tfpzV/tljEmbDttLaTaa04ref7qaPKAjYkHGithM8jYTkUVQUmW+iHMDLstJ7vS/v4gkuU8ZeAWJhiZWq3AV030tcv8FxrvnZe01ULjwSshWPuIjA2G90/Bz7W4jo2cEo5p3ChWy0ezjncW84qa5i6Xcbf9/qA1/0T8OYvAL4i2ud88TUpBbxkB/zcyYcorsZbCLz6z5wdkx4WrZfGcz/Mng1xxdXU3Z0ONSLkZHaPgwuX6aJBmAYWrc3F8/4ZzrMuVb+mjwdxJBizDtF8ENj1K3Rf77x2QL/S7ZjR53p1TnOnDx8B1hcd/TrrMS1JppJOJSJk0EkkUWhF2+MBWgHCBc/2zuKtn3sW5yaXXP+NbLNhovXHP/5xSJKED33oQxv1kILNhixrwppVnjXjL9GcgAOPZ+841EX5IWe/pzqtN6hyFY8DM4qT7vK3A5DoouZANIrp8g3bqotVp50rp3UskhhPki/HrMCY5DxrPe3XUExHNEhDwnKJLAPPKpmur3ofuS/5GApKgNVpy4p8zmCX8PbXUpcFcGlGhPCCr/N6+79TWqc5p/qE29oJIzpRy6PYH13Hg3A0SM02urUbD2Ij0xrQBttZ5gPH41qrbHVX6ve5y+NSHMbILuvL30EuMY9X+9w4zLUOrEewsEYC4Q076wGITGsAODlKLrudjeUo9Hnz6rQOrEewHKTh2PzZalbcUuO2ndbKeq680fxnKlrp2hmPAAvW60/eAHfWlaJTOc/YHsYYi2jCg97UUdFKxZd49NKN/RFYw0LJlmupsy5XnbIbDF87O+tK1c/4RXEePq7sA879zNWvcyGsqSJJtHbstOYhjCaiNXPZbwK//yCdi+Z6gC/eCpx/SP32YMJaSkbNC/9Gd6/6faDCoiCYjtI6OvdBzr1hzW40CJOrYYxGhUsr2Gk912Mq7PcpXYU8tBPQupOWQ1EE1h0UQ3nwYsertW42l7nW+jW46062wScBKI8txxxrIxwl1posWivXb0eRRINPUfGQmXQvWn/tuSEcHVzAvS9vnoinDRGtjx49is9//vM4eNDmB0BwcTJ1mk76viJtQZMO/cInW2TqtN6oeJDFISC6TpXipoPahcHBMLSppSCicRk+j4SG8iJ10eX4IgHQ8xbRndRFVMDmYbaXMr08Ps3RrEeSgMPvpvtHv2SZC5cxvY+Qq9BfTm15jK9QW2BsdMFDlnWi9WuAhr10fzrLbXUXAmqetQPRGgB23Ea3+lxwQVrYAcqbOgDorKP7C2sRamO1/ceUc//lb6fbuV5bsROaG7TE8ufY6WEpWi+PA7EQnWuMWlhbr6TbiRM0lPBSYeIE0P8Yxe5ce7f29bar6HbMmWjNG6i6skLsaaaOiInAOsLRHJ67LwBOj2vRIEAGYkkW4M92XZkfJX4fAG2gqS2ndTQMrM3SfSuntccD1G6n+2kKVWqBqqoYOxtJILA9jHGmmz7bhZWJBSlJ0tzeItdaYMSwkmfN64otr6K9y/K467zZfBOJxdWYn/bqYnTWKiKSmwHKm4l4THPAu1wDq5nWSqHbdceL6rTemf5nWy4H3vs4FUZCS8C33gY89e+ALGNA50a90fMKaheOA75i4NV/6ux4kpGkjcu1dipaNypRbJNZdFpH1rUOPrtO6/ImoLiGOllNrg99qtNaE62L/V7UKd2HjiJCWLTuvB5oO0znmZVJVx35+m5H18UontPkVQxaDmf/jCfl5jMdyvlmyIlozdEgHCmbgdOai92bKZYu56L1ysoK7rrrLnzxi19EdXV1rh9OsJnhaJCtt6RmV5nBwxgHn8qO0LY8SbEFksf+EEZGFa03KB6ET/51O6hFqv1q+reDYYyjuoB/r0dCid+H6hLKSXLstuYLBW+u+h7NrfgpsE/PL+i243rztrKDb6PN6MIA0J9D0fjZT9Ptle8EiioTv5evXOvps7R58hXTc3SpOq2XeWCJRJtKJ3Cudd+jl5YYmSHJQxgBoMTvUx1KA3YXpOE1bdDN/l+n4m8snHYjtRSMYIndoBaZ1oBetLYQ3NhpWbWFrkvJ1O6gglVkTduQXgo8o5z39r0ZqO7Qvt6qiNajzgb9cI5hZ20J6ssKUeL3Ii5rBYhLFc6z3t9K17kttdyWvvFZs6NqB4P22W62mwsPUNcRQIOSi9MMvLI5jJGd1q3VxdjRQMUO205rNnQ0HdCGMDLqMEbnn+nn+ubwbO+s498TXCDEY8CwEl3YcS3dFhRre5Z8Dt/OgMmAFu1VV1aIDuVcc8E7rcePa7nQK1MUb+AAWZbV81tzcjyI20zrdE5rpqwB+N2fkIMaMvDLfwC+/y6MTtOsj7JCL/7M9wP62cPvtu5gscuGi9Yd1j/H8PyQqdPZ24tPnyG3cEktUNFi73ckKW2uNc9v0ceDAFoWuu1hjMtT5OiGRMWLgiISrgHHudayLKvDOwFgyG3hm89vV7+XbnsfdvR6jJvEg3Qp5hbbHZmyDPQ8kngsfE13SDASUx3eg5vofJdz0fruu+/GG97wBrz2ta9N+7OhUAhLS0sJ/wkuIs4rovWuO+z/TssVtAFeX8iozUGFP8B1OwF/qfXPJsOuso1yWrNozZsFjkpxkGs9tpiYtwjAfa41t/9d84f0mqzNApPuToiCLKNGg1h8tvylwKHfofsvuMuxS8vECXJwSF7gmvelfp9F66FnN3YIIruDO19NixzVaX2Jidbssm7cT7l5Tmi9Eiiqos3OWIaTti8hhud5CGPygtSha2viOLXnlzXRpqaWh7PZc15WlxSgtNBAZNbRasdprQ5hNMizBsgV2nJIefBL5H2yMAicvpfuX/+BxO+x03rmLOUN2oSFkS21JZAkSRUFXG+sLgJkWcbJMdoX7FOc1u1uW4yzQHKeNaAr/NjJkVbzrJvSz3hRRWvrYYx619ZOZYDn+Wmb7zujIYyMy2GMK6Eo3vXlF/CurxzFSkgUOy9KJk8C4WVy93HOLqB1yrrItZZlGat5fr+M6AYYezyS5rR24nzcjPQ/lvjvGWfr4KX1KIIREuUak+JBJpeCCEZsDh0OLQNLSvSAHac14/MDd36C/vMUAGfuw3vOvw9t0jTe19yHQ54+BFEIXP8h+3/TChateZZHruDoUbtO69rt5DKOrGZvUCQ7c5sOphYurWDR2sC5vxKKqs58fTwI4KLYMaSY5xr3UwQbkBgR4oCZ5RBWw9p7ddjN53phkP6TvMAN/4e0kdUZYMLeIPJILI4p5blJFq01p7XN52amGwgM03viVX8EQCIHuov5Y73TK4grHoChudVNM3w2p6L1d77zHbz00ku45557bP38Pffcg8rKSvW/9vY001MvNKJh4KtvBD5/Y+4D/Tcby1PaBtZKWEvG69OyV7MxhXr8ON06zbMGtAvJ2ixNXc4104po3cCiteJaGD9u+/0zOp/adtKq5lo72PzGYzRgAKCFKA9ZEREh+ScY0AoKRnnWejgi5PyDabMxXfHsf9HtvjfTNPBk6nZSJl0slDgsItewaM1uYf5MLY0aTiK/aOHnvOM657/r8WpFh15n7W+XMtwu254UzdFV7zDXmjts2q6izUQ9t+tbi0h286wBm3m8VkMYGc5xvlSGMT73GXInbb0lVfArbwIq2qh1dtzeRgbQWkJZKMn3wMHNwPRyCLMrIXg9EvY2k9O62O9FfTm1GG90RAg7rfWfbW6Xn10JpY9y4TzrMhtuwDpnRaq2ai0epHdq2d6m0yrL1KXT+uzEEkLROMLReIKrTXARwdEg7VcnDgHPoFP2r+87hcv/4WH02O0SMOPMj107vZOvnfmMIsoq/HxIigTk0LwxsaQVwosK6PWuKfWj1E/3R+128fK5rLReEyCdcNXvA+/8KVDagM7oAH7i/xv87tLnAQDfke4Ayuqd/00jNms8iNendY1OZSnX2qpwaYXqtE49Dh7CWFdWiMrigoTv8bXT9rqGBy6yUK2/7zDXuj9p7e3KEMDRtW2H6T287Wb6t82IEH03R22pP+F7vPabCNgsBPG+rPPVlMXOkWIuIkLOTWrn3c00fDZnovXIyAg++MEP4pvf/CaKiops/c5HPvIRBAIB9b+RkQ2KYdgonv4ECa8TJzTH20XAn33vON79laOIxiwWJewEbbmCNnFOyGautds8awAorqJ4BWBjIkK4+s2bheouoLSBhvFMHLf1J0bVDYy2qWqtovuOnNZTp4FQgKqITQeB7XmKeRCk0vcoOTBrd9DUdivqdgBbbwYgA8e+nN3jCIwCp35I9697v/HPSBKw7Ra6v1HvndCKtqli0bqokoQkQCsOXQq4GcKox2audSy+OarymwF2Y7bXJIrWWxWndfLC2fwPHaVbLl7WKc7HNM5L1Q1alT6Si50ei2sRrIVNXG7stDYawshcSsMY1+aBl79O96//oPHPtCk53w5yrbklk1vSO2ouktb0DOBokO31ZapYAmjPzcaL1qlO69pSP/w+D2QZqoPKFL3TOh36eBCTjXlgPYJlxZ3aUlWMzrpSFHglrIZj6dd78biWjWrotNY9vo0cfebMuNYx2z9rv9MgBVkGzv8CWF90/zcEucGsGJ5Bp+xj56YRjsXx/MC8++OaPgt873eBb/0mEHTeuZ28f+rUDVDeLM5Dx4TXtG7d3W+gW4eD0dUhjJXaeU+SJOeFVR7CyGsZN3Rci5V3PoIT8a2okVZQsTaEFbkIn1p/PZaCWeq84bXOZosHAbSIkGzlWrPAaXcII2MRD2IWDQLo40Fs6hFGexh9rrWDQcFsGFGL3m7WVlwA4gLdDsU4xtnSaVCjQSqL1EHtTHVJASqKqDvS1rqPhXLep/F13EVHfHKk2GZZd+ZMtD527Bimp6dxxRVXwOfzwefz4YknnsCnP/1p+Hw+xGKpi57CwkJUVFQk/HfRMHUaePJftX9nkPEVjMTwFz84gcfOObf8Z5uZ5RB+9NIYfnluGr0zFgtSFq13vd75g/DJYPg5cqtnQiaiNaC5R3M9RTge101VViqpkqQJFux6ToM+35BxFQ/CF4ot11B1d9tr6N8jz7taDAqyyHklz9pu7M7h99DtS1/LbsfHkc+S27DzBs1paYSaa/2Y+c9kk8GnKfu3qiNR1FdzrS+RYYxr89r/6xYXTmtAe+3GXwZWZgx/5OEzU9j3/x7Ej49vnonT+WItHMXsCl2zkkVrjgex5T6UZU20blOuAaqIlMZpbXANMKOiqADlSoSIaa71gh2ntSJaT52+ILvK/urek3jLZ59FKGpDnDv6JcrvbjqoFAQNUHOt7YvW7LTmFtGLJk81A04qovW+1sS9Qb4ckKM6VzMjSZIu19quaG0xhJGp3QZAAoKLwKpxPrQ+CqjE70OB14OtdTaHMc73A+EVmvvA0UN6qjqUHP2QI/GGB2cCDrpKjDj7Exq89pU7gejmcH0JQNcmFq2T1xUJnbL2I0KWghH1s+NoCFkyZ35Mt9GgqwHSyZ9vPs8sB6NYXNvYKKKsMfwcrYcr2oA9b6SvOXRaTyqvDZ/nGMfnYV678FrGJQPhKrwt/He4XyKt4H+9b8UCKrI3QE7vtM5VsSK4RMUdwLhL1QxVtM6C0zoW1ZzSTQ41kvo9ACSKxkiKo+AhjMnRIIDmtB61855ZmdFiU/WD5AuKtBg2B7nWHM13005y5M+thp1FWMXj2nmN135sjBp7yXSPpIe7GpOjQQBaS3ChLG0kUWhZOw/vuJ1uufDgItda77QGNs/w2ZyJ1q95zWtw8uRJHD9+XP3vqquuwl133YXjx4/D6/Wm/yMKF/y09FgU+PHd5JAtbaCvZSBaP3ByAt97cRQf/3n+HYLnJjXBsnvSpI0rsq4JVE6iQZiGvdQ+FFnTNu5uWJ2lOABAO9E7RR3GmINoBT2LQ0B0naqHfMEEdLnW9oYxGjmBtHgQF6I1Oylquki0iEcdDz8QZJF4TKvo2v1s7byDFqxrc8CZ+7JzHMEAcOyrdP+6D1j/bNfNACRg+rS2ac8l+mgQfUYbR4RcKrnW7Dav2+W+bbK8STt3mjjlHz4ziWAkjofPTLl7jIsI3vRWFPlS2iK7nLi2FodpYJLHp+VFs/Nyxtx5qT+GNhuiNaAfxmhwfZBlXTyIhdO6agtQUkfXh2y1rW4QoWgM33lhGMeGFlRnrymRdeD5z9H96z9ongHJGyqbGd/r4RimlkiYYxfxFkW8Hp7fHJuHfHBKybM+0Jo44Dcf0SmyLBt2sgEOhjGqorWNeJCCYm39aRIRYlSg2qFEhKQdxsjde437jAeserxaRImDiJDTOqd1RqI155VOnQQetxc5KdgA5nopMtFbqHXY6OEoQQfxjvoCS0ZDwM78RLvf/YDjX0/ePxUVeNXP9gWba8151ttuTpzt4kCMnVSd1hmK1tlwWoOGWYfgx1cb/xL48CCerKfZPVl7jaraAUiUHb2aXoh0BRvhimuAwnL7v8cZ8tlYZ831UIHHX2ZtSjDCX6L9TpLbmkVrY6e1IlovrCOerkOTdYiGfalxMi5yrbnL8bK2SlSX0PrcUZFs+jSd+wpKNWNCRTMZGCDbKpSxOcRsSLqao5/u2jnwJGmM1V2aOauJRWvn8SDdira3R4lh2yyRSDkTrcvLy7F///6E/0pLS1FbW4v9+/en/wM6eJjcBcuR/yZnWmEl8HalfX7yFccTe5nzygW9b2bFnhMoh+iFalMnR/8TJMBWtLkTiyXJ1cInBa421WwDily6+FXROsfDGLmaWLcjcQOhH8aYZpERj8vqCVEvWLQ5dVrrnRT66ia7rUWudf4YO0bic1Gl9t5Ih9cHXPUuun80SwMZX/oaDeKp26VVms0ordWEt41wW6ui9WsSv64u2C8Rp/VgUuHJLds5IsQ4s61PcQ5nJFBsNnoeAf5lm+2cOkbNs05yWfPXvB4J6xFNoDSFi7VNB0i8AiivTvJQbJPFoBU+zycLa2aoudZG14fVGXJjQrJuYZUkTcC4wHKtB2ZX1QE0vekG2B3/Jp1/q7YAe3/N/OeaD9GgnuUJIJC+A4E3CBVFPlQpmyl9BMYF25qeIeza3Z8kWufDaR1Yj6iurOSCUEslF37SOa2VTGs7TmsgMSLEAJ5Tot8Aq8MY0zmt7WSZclRdmu4OJhyNJ+wLMrom6M8jz3wKGHrO/d8SZA/eG7ReCfgKU7/P8Y5Dz9rulNXnWLt2y871kaik/tGHgJgzd7RRwfeC73hR4wxuof2l5KU1xNK47T+hitYVSaJ1bZ6c1jO6+Q/F1epxZO018hXSLB4gdxEhTvOsGY7lCIxoTm23sLjZuD/9YGCrY0kSrdV4kIZUp3VzZRG8HgnhWBxTy2mulyxIG8Ubusi15utRV12ZtoZw8p7hz1Ln9TQclOHZUjYiQnh9buS0BoBO5b2ctnjHj8XRIIB2LV8YcDS7aXEtrO5JbtvbaO/xN4icDmLMFptF4XfFbA/w6D/R/Ts+RnZ9roy5FGD5gh6Ny+k3VTnm7IS2uOg2c3Kc/znd7rrD2TRaPdnItc40GgTQidY5jgdh0Zo3CUzzZYDXT9W9NNlNMyshhGNxeD1SwuKCF2CzK2F74f6z52lj7ivS2r4BTQTsE6J13uhWPlvbXwt4C6x/Vs8V76Sp26NHteGkbolFKBoEoCxrO4sdNSIkx7nWc310wfb4tMIXo8aDXCJOazUL7tXWP5cOLkr0/jIl21SWtWvSBZ37mMzxb9A59/nPO/q1YZMhjABQ4PWoC+W0Wa/qEMarta/5dF04FiISb7zNnBzJWDqt2WVd2UYtmZZ/6MLMtdavqSzXV/EY8Ox/0v1r32/sTmX8JUCjUiSzkWutDmGsK4WkrJlaqorhkYBgJI6Z5UsvHmF2JYSJQBCSBHUII+NYLMkCI8qQ6/rywoR8bUAr/KR1Wq8o3Sh257yoorVxjv14gF1b2vmGhzH2TKdzWttYH7Mj0qbTund6BWHdrJuBGZfXhGhYE9W7bqShpvf+IbVEC/ILd3CZFcMb9lLXTWTNdqa/vsAyNL+a3oFpBEeDbL2ZHj8YcDRLKhqLY3KJTT/a56mjhpyPrgTR8BrNfskXq7Na9nHXjbSG4O4JB+aNiSVjp7WjjpdoWFtPZOi0Zkc1D7e27U51AneWbTbRurhK+51MI0LUwqXDPGtGdX1ronUsLquO5m11qaK1z+tBi3K95GuqKVZ7mLbDpI0sT9jKtY7FZXWd1VVfqutkcyJaK3oU61MMx3P0/ZKSFiwYS7M+53g4Swe4LJOxRv/YALnReXaTg/cGm1Fbq4qxv6Ui/eNvIBsqWj/++OP45Cc/6fj3spZLtNHE48CP308ZcNtuBQ7dRV/n7BuXESHndYvPcxP5XbR1T2mtf4ZTnuNxLXN3p4s8a4ZzrcdepMFqbsiGaF2pZE3l2mnNw+EakkTrgiJybQFpI0K4ta2pogg+r/ZRrywuUKc823Jb84Wi7XBiNbHzBhI+FwZJHBRkhf99egA/esnmwlb9bDmM3SlrAPa+ie4f/aKz303m9L3A0hhFHx14m73fYdG6/zHHU+UdwV0AW65Nbbmr2wVAIjHSRvbYBU0woC1It1yb2d9qvxoorADW51MKHrMrYQTWyc20FrbhIL5QGH+ZboeecZSnyotwHjaTjD4ixJLkIYyMGhFiLCKthaOYXyV3m51Ma0BbPI8b5fGqQxg7bfwhdlrbi8TICvG4LSezFbZF67M/oWtfcTVw+dvT/2EHudYsiGzROfT9Po9aUHA15X6z4VC85KiWrXWlKC1MLBDw8zS+GETEaiB4FjGKXmOanTqty+yK1orAZOq0NooHoetez9SKufgny7r1sYVgUc+itb1oQnbGX76lCpIELIe0jH9HTJ+mHN6iKuA3vwFUbqEIvQc/4vxvCbKLGh1osq7weHSdsvZyrfUFlmAkjmk3RToWrff+mrY+Pmc/ImRyKYhYXIbf60F9meYg76hjF68LEeeH7wY+dVn+uo/YKNe4n/YAgKvZLlMmmdaOuoHm+2gGjr8cqGix/dhGsCjalcv5D9VKZ1muROsFJXLUqWgNAI1KB3umESGZaiRcmNd1OIwtrCMcjcPv85iuQW11Sq3Oae/RDgOndUExaRSArcjSsYV1RGIyCn0eNFcUaYOu7a6tomHt3Jc8y6T1Sop5CQaAUWudZjyd07rORgFm5hxF3/qKUgV9dRij/YgQjhLb3VS+6YbPCqd1Ljn6RWDkCOUD/eqnNJdxBqL1WjiqOqeAxEzpjSYaiydVxNewHk5y7k4cp4W5vwzousH9g1V3UjtyPKq1ozmFM/s4msANGxYPorg/k53WgCZc8ARoE8yyTCVJ0oYx2sm1VmMFki4UhWXAllfR/Vw7Zi8RxhbX8Q/3n8GHf/hK+uifxWFaHEie9JEcRlytDGQ8+QP3bWWyDDz7abp/zXvTuy+ZtqspB2x1JreZt2bRIICSwaa4Jy72iJCRF8idVt0JVLZm9re8BVoRMSmzrS9pGG9aB7EVsz1aJ0E+WZvXNiqRNdvzBABgZCFVfNRjaxhjZF1bcHI2MpM2LoDO7+UGmdpmtFjFg9gZwqj+IUW0nu3ZuGG9T/8H8Im9wMN/5/pP6IXqHjPRWpaBpz9J969+L+BPzWpMwUGu9dC8rt1ZxwXfms689HXgn5qBf9kKfOZa4KtvBH74B8CDf0XP6/FvkXNo4gSwNAHEomo2cnI0CADUlxWi0OdBLC5jIp1QnCV4fWXURdFix2kdDVMHG5C1eJDRxVTXVkdNCfxeD9YjMXOTQmCU1gAenxabZQSvR2fO2yo2n5mg1+zy9mp1HdpvNbDdDBb5Wi6nKLQ3fxaABLz8deDcz5z/PUF2CIzROlTyJHYBJcOitc1OWXb68ZbZcTbxwhDt9yQPsPtOYPevKH/4AdvFslFdAcjj0TqEVRev02NamqD1TDwKvPJdZ7+bLdRokJu1r+lzrW3C57Vk0bq1uhiSRIaFudU0xSkutNftcN+BDeruG1DOKSlO62y6Q7lQz+7wbMNzsqxi18xoUhzOmTitZVlbZza5dVor8SDT51SHcZ+yB9haVwqvx/h15muopUOfBeL6PUBpnfHPOMi15r1JV10pPB5J69ayu7YaPUr7gdL61Gumx6vtyS0iQmRZ1onWxntnjgcZDwTNO+P5MTpv0OIDGRfDGHkI486m8k03fPaCEK03S5aKIxYGgUf+nu6/9u8Tq2dbrqXF4eKQ4xNg3/RqwjU3ecLnRjI4t4ZwNI7iAi+qSwogywbOpPMP0u22W4zzzpzAQombWJX1BU14cHtCBrTXcXWaxIRcEI9rAyrq96R+3+YwxlED1w3Dm5rRdKJ1Qp61Qfsfi4Ei1zorcLdCJCanFybYZd1+TepQCju0X0MV+mgQePmbzn8fIOfM5EmgoAS46t32f8/n14pYuSp4RENaxd1M1HexYL8gUd1QGUaDMCa51snnf9cZposjwJdeC3z7tzSXc75IXuw5eL/yIrwtnWht9TyNH6eNbmlD6oaGnY9mIpbJoDgrNJeoUTyI4rS2GsLIlNUrnUmyVjDOJbKsDYN95lPAc59x9Wf07+GxxfXUQjxA55WJ4+Rsufq99v4wO63HX07bMqo6rWsTX7ctSmv68CZp1XTNiW/TnJM1xT018ARw8vs0/+WR/wfc90fAN98CfP5G4D92Ax+tw+8+dSt+4f8L/MXUX+gE7k8Ag8/A45HU1vSNMrmM2HBaTxh1KzAcDeIpsH/9ZtF6cRiIpP5to1Zjn9eDrYqYYzqMkc9x9Xus1+k1XXS8kVVtqLkFXGjY21KBLqU13NU1gSOGuHuj89XAdX9C93/ygYu/U2qzwtEgTQes5wTx3m30KBC2fv0DaxHVWX15exUAF67msz+l247r6Tq09RbAV0y5vxyPkQYz04/rwuGZ+wAom/ez9zvuNMkYWQb6Hqf7W2/Rvu7Qab0aimIpSNevxqRM60KfF83K19Keh3nNwmsYlyysRdTj4egWFq2nl0NYC1tfa21TvUnjQQBtVpgDN23q4w+RM9hTYGyWs0NVJ5mRYiFy0gPoU9ZTWw2GMDJqrMyCDdHaKM+aUUXrZ9J+vrQ8a8WdrzqtbZ5rWIfqutE4EpNjOizm4CytR7GqrC/NnNY1pX6UF1Fnmamoz4+hz7NmXAxj1DutN9vw2QtCtN7IieBZQZZpIRVZo4tmsphTWKZVpR26rbltqkJ5E5/NJB5kbR44fZ/j4RQMu7x3NZXrhr0kHQ875TKJBmEyybXmhUrVFncCH1NcTa5xIHfZZItDtKHzFhqLA+y0nj5jGa5vJVioTut0Q04XBoHlcbqQceuNHh7GOPCk7SErWSMeA+67G/j5X27s4+YQ263pgC4a5HXuHkySgMPKuenol9zFdHCm66G7nH+ucp1rPfwcnYPLGrWstWRctEZmlZGjwKcOaa2suSJbQxgZLgKMHaPriEKKaG3lIDYjFiVRKrhI/873wC0WzQuURXe/veGhsixbujEBcp8AaYQcfTRIsiNJzZi177xMhz4eJKUdUBWtbU6Wb7mcbjeiHXr0RSAwDEB5jn7xEeDUDx39CX3+otcj0T7fyBn6zKfo9vK3m7t+kqnbSbE6kTWtk8oE3hyYOq0vtDWxnnhcW4+97evAO+4F3vwF4PZ/BK77AHDwt+ja0HiAzt2SB4CM8ngAuzyjaF14QSdw/z3w1TuB6bMbPozRan3FgxjnVy3mhixP0m15s32nYWkdRWRAVkUBJhiJYXaFxL5ko0LaYYx2hjAC1GVTu53um5xzmHhcxllFtN7XUmHvXGfGmHIObr1S+9qtfwM07KN4r59+YONFQIHxgHYjqrso0iUeSXs95/jLlsoitavCsXHt7E/ods8b6dZfQsYpgNzWNhg1GGoKaBmzc6thLAcd7J1P/Ui7vzS68cX4+X66PnoKEqNc2Lgx050yo8QIzvkuK/ShvCi1e8t2rrXqtM5wCKNyPmmpLEKxEntZWVKgDjDO2vWAndabUbTm/c3MOdd6jipqNuxJjAF1gsejRYQoHbQ8mH1bfWqeNcOFoVGrTGt1CKOF8UbNtR5Pm2udIlorn2vbEWNGXQt6tr8GgETPg4lWxJ1PdWX+lLkYjCRJ6jrQ8NoZXAKGjyiPaWDOYqf1zDnDQncysixrTmtl3bCZOvwuCNF6fJEycfJJ7/QK7vn5WTUf0pKXvkZVGF8x8Mb/NK7CuIwI4UXn7fuaIEk0nMb1UJ4H/i/w/XcC93/I1a9zC9fupnLsajIQrQOjymJYci+s6WHReuokDZRwQjbyrAHaXKgRIUOWP9o3s4JXfeyX+NJT6YcCJMB5gXU7qc0kmfImxXEnW+Zj8gmxzUCw4EE9aeNB1MngV9DCL5nG/eQAjKxSFM5GMvAEDUh7/rPAyvTGPnaO6NOJfH1WonV4VcsHdJpnrefg24DCSmr773coHk+dUZy2EnDtHzt/bBath5+jATXZRo0Gea25KJDvYYwvf52e+/v/LHdDpcJrmlPNyqXghMpW2uzI8YSiAwt8B5TNpiuB4omPJ55LNjIT2QjeXF75LuXfxxOEejMW1iJYCZHLx8iNCWjtrMPza+YLZc7EMyoacsbt8rhhBIdV7q4ZjRVFkCQgHI2ntvhyZ5hd0ZpFpo0YxnhaEQX2vwW4+g/p/r3vs52jCtBGOxyNo6jAg8va6D2cIlpPnqJzi+QBrr3b/vF5PJqIb3HdDkfj6nW5I8VpvfEDB7POwgAQWqKi/K7X03Xgst8k5+ztHwV+/fMkZP/R08Cfnwf+dhaBu8/gdaGP467wR7B252eB2/8JuP6DJGzLceCJf8mDaG3+2aoo9qFY2YSauq05z7q80f6DSpJpRAh3RnDnox51GGM6p7Wd9XE95+hb51qPLqxjORSF3+vB9oYyVRzod3pNCK9qRR79IHBfIfDrXyCRovsBupYKNhZ2WqebkyFJulxra9MR7yF3NJarQpKjuVZL41p04p47ta/vUiJCbMbJmDmtywp9qCsjUc+2iLM4olzHJe25One/vd/NFqw1tF+TGGdV3UlaRTRoq/Ob86yThzAy6nk43XPDw6PdunoVeI3J2bsMO2cHZ7MlWisGsuVxW+KfI4IBzaRR1e7896s6KBs8FjYd0puWTIcwMlwEmSIjEMdBWYnWaa/da/PacEerAllBsdbRliYiJFm0bijXIsbS6iLBJW0NZyZal9Roa3YTt3W6PGvGUjQeeIKKgTXbgNptqd+vaAVKaik/3oY5ayIQxHIwCp9HUl+znMTtuOSCEK3jsrZAzBf/+WgPPv9EP779Qpos48AY8NDf0P1b/8b4TQRob/SBJxw5HHnReVlbpfpG6nYTERIJai7ol79BQrtD2OW9W+e07tYvijkapP1q+24kK8rqtYqi04iQbInWgE60HrH8scfOTWNyKYj7jjscDMUCmlXbFGdJW0SEWG2qNKe1zWm9Zg5Nj0cTHzc6IuTEd7T7Nlv+Njt6gcTQ4cf0P0EtWFVbMlv0+UuBQ79D91/4krPffe6/6XbPr9oXsfTUbqf4gFjYfU69Ffx+NMqzZvTxIPlwarEguzarudazzegLFC9R0eouL88Mg2ggLrTctpeEGMeidf8TwJP/Rvev+F26zVC0znh4CA+b3HWHEtck2yo2s9OoobzQ1EXRWF6E4gIvonHZOKpJlsmNDxiL1sVV5EYFgLnUzcqYycbbCr/Pg4ZyighIiAhZX6Thm4C9QYyAbhhjjl1l8TgNhAVItL7jHho0GwsD37nL9vWBOwW21pWpa5qUjhfO8N/7JufnPTXX2rrYHJeBogLtdWBsCwKbGd4cN+4j5246PF6cXCxEt7wFo9XXoOSq3wGuez9w2z8o2cYATt+LA/5xABvTmZnQRWEQ/SNJEprT5VqrTmubQxgZVbRO/Lzz0EfKlU0s0vIwRv0Q9wTsDGFk1Fxra9GahzDubCpDgddjf+hsyrG9QoWJ8magIin7u2k/7bMAGsqYq7xZQSpr85oIYme4s814xx7FmLWzsUzNc3UklpxVxOC2qxMH/O28A4BE5580ezdAv39K/XyzmG5btOZrU8d1wOE/UI7zp/Z+N1uYOUM9Xm2vaVPUAlLzrJktdgbaxePAbC/dzzAeZNBMtFZfoywJbSU1JAwD2Z9pxe/H4prUgfF28Hi0PGm3M4LYad2UoUbCGo0iMrMRy048yNRy0HiW0/BzAGS69vEAUTNs5lr3zySK1h6PZO+9C9B+VY5RIcPKGb/TOiKE9RfuzDJDvXYavZetokEAKhiqESHpc61Zw9taXwq/jyTiLcJp7Zx8K/x8IT0zbjFQSJbJtRxaomrPq/7I/Gdbr6AT4PqCoxwiHgy0vaEcuxV3s6thjANPkjtWUt4CP/tzbXNuk+4pjgepUDd4Pfr2w25FtM7ECZqM24gQdVF+KPNjqFQqoWkuXLw475teNZ/abgS3TTVYiJFphjHKsqwTLAziQapsDmJURWuL6iaLV332RetTYwH8x0Pd5q2z6QgtJy76Mhjmt7Aaxp9+9ziODbkcRphF+hNEa4tzHheEdr4+oyEmALSIkPMPahOs07E8qQ2Tue4D7h5XkrSWzWxHhATGaAEueRLz+5Kp2UbtkuHl3MX9mBFeTdwkPPtfwPJU9h9H38Kb6XtFj5pr/QgQj2M1FMW4sqFh0drSQZzMygzwo/cAkEmwvu2j9PWFAVvOZiOWghHc/G+P48M/cJn1tzqrRE6ACp5coLMREZJuCCNAC2VtOrdBkSowAqxM0gwMdukmwyKWQbu+mVssHez8SBCteQhjaYP9jVXzIQASPYe5zJ0dfo6cq4WVdD3yeClyouPVtB77xm/Y2mhq66sybG8gl0mCaL04TINrAXfnPXaej5oXYoZ00SDJ4iM7buZWw6qL3zYPfgT43u+mzdNOxzO9s5heysBp5kQgVTilCKApQxibDlDRFDKuH/tfABvjtJ5fDWNNzaI0Fm94I2o6GHJFFw/iBO6uSHJac9SbURSQvgCTshZdmVZc35J5jJYeFpl4nWoC51nva6bXjEWLoblVxJysh7lLQ++y1nPt++naFl6hzgobEQeCLMAt6bU7yFCUDnZaT7xieT03cloPza3ZLz5zNMjeNyZ+vaxemwdkY8Cz2qlqcO3scCqmcxfQvjdT1q3XT5/fNJ8hM544P4NTY+bRkCnEY1rHkZEzVDVvpBetOR4kOc+aUQfaWZ2HA8NKBKY/YyMFC3lbk0RrreCRpeuBJOkiQrJcHOO1SXUGz0WmudbZclqr4vlpBNYjamTVVgundW2pH8UFXsiyiSZhJxqE0YvWJueMYCSGcaWY3KV733SowxjTfK7TRYMwnGvd/zjNWErCvtPapAAjy5povd1EtAa019TGe6M7KRoEEE5rV2StxcMF8bisCpBnJywE4le+R1M8vX7gTf9tHO3AeAu0D5fNiJD1cEzdCO9sLMPupgrlmFw4rbk16crfI+ErFgK+9w4S0W2wEopiRMkfIqc1nZDGFtcp5yu0olXUuS0rG7gZxhha0VwpWXVaW2+C+QO+HompF3pbcCuk0RBGhhdfoy8aLtJnV8IIReNUZDOoiPNCbHLJIr8pMEb5XZJHezwjWBScPGk7puNjD5zFpx/txf2vTNj6+RTO/JiyQZkMpiZ/++gw7n15DJ/6pcu2qiyxuBbG7IrWjt83Y7DBBOhClWmetZ66HcrFVwaOfdne77zwBWpJan8V0G7gALULZ6JnW7TmAkrrldZZ2z6/JgJsdETIxAnNQdZ6JRURn/hnV3/qgZMT+Ot7Txp/lq0GqWbClldRzvPqNDB1UnUu1JX5saux3NpBnEw8TsPXVqYop/mOfyYXca3y2rh0Wx8bXMDQ3BruPT6GqF3xXA8Xcmu3A0WVuiLL42md+Xx9NHJi6uHNVr9RkYrzrBv3G8czAbphjKkb4DE109r+IEZAE9zG9IKbkyGMTFGF9vnKZUQIiwJ77tQGyRUUAb/1TdqQr0wC33hL2uJHr0603mYkWh/5LDlsum7UXORO4NbVmXOGcS6AbgijwfumvKgANaXcmu5gA7E4DBz5DF03M3gdnu+fw11feh7v/3YGznkXXW8s0OxvqUz95k0fBgA0jvwc26XRDRGt+ZzWWFGIQp/xOp+diGmd1twpYReTeJAxi8HbW2pKUOjzIBiJpw67Yodd7Xaat5MOdlrPdlueA88oe6V9rbRPaaksht/nQSRmo/1aD+fht5oU7Txe4Nc+SyagkSNa3rwgtww7XFeUNynvHVkbkG3AedVpXY72mmJIEu03U6KqjFiZ0Yw2e96Y+v3dyl602zoiJBqLq8UmI9NPpxMX73w/RYxJHurOKarQzFcu3NZji+t415dfwLu+fNS+kD9xnOInCiuNi9+N9kVrPp+ZOa1tZVpzgb12O+D1pX1MK3huSur8hyw7rQFNVM52rnUmedZMk1JwdLMXdlq4tILfS4FhDI5R91NTRRHKCs1fZ0mS0F5D160RK9E6XXY+YCvXmopgNBuO11OANug6raPYrmjddBAoa6K9HZ+XdKhOa5PCN6MWYJJ10Okz9P/pK7aOfnQwjPG8LvaXEZnWLsinwj+1HMS64ggdmFs1nia/Mg08SItn3PRha5cs4zDXund6BbJM00Rrywqxu9ml0zoe1yrNu99ALZbVnXTivPd9tuJKuBrTWFGI6lI/qkr8aKygzWLP9Ao50WJh+rsZtv8k0HEduc4WBu27QidPApCB8pb0rSV24AtLwLrFTD+EzDLqQU88rl3QrWIfGvbSQMjwsuFCg1vbmiqK1BYPPfVlhfB7PYjLwKRZ5qI6Gfyg9WTwsnptA2pTfGQ3BbeQOub4t+mWB5pm4LQ+pxR9TPMeNwh+jzSUF8LnkbAWNil2TJwgEaag1F7l2Q6H30O3L30tfV5baAU4+j90/7r3Z/a4XTfSYn7mLGURZgvOs2ZR3Ip8DWPkTLTWK6nVHQCOfUVrm3TAP/3sLL75/DAeO5dUNIqGNOHTzoLPCb5CrYjY8zB6Z7itrAwej6RlmNo59x35b8pH9xUBb/2yJtCyM9WlaM2Di8PRuDvHzYQizvFGr+M6WhAHhoG5PvPfg+a0bk/jcrZsm+doEO6sMcLEaR2MxNR5F86d1orgpndaOx3CqP4xjgjJkWgdi9JAaQDY9+uJ3yuuAu76AUXjzJ4HvvWblvn5vTM6p7XiDBqcW6WCx/oCcOyr9IPXf9DdsZY3Kp1asukgLt4YJLc7M64iQrjICWQ0AOzYMJkajg7Oqw4qR8iyqzZkVbRuNViHKG5rCTI+4LsXgfUIAmsuh1HZJN2AVQBo1g00NUTNtHbqtNbFg+jW6lZDV726fMqUYYwTx5UDtvl61G6na3YwQEVGE3htt7eZXjOPR0JXLeda21wPA+md1gCJSa9XCr6PfcxWG7QgQ3igopNiuJprbTxnYH41rJ5XdjSUodDnVQuotsTHc/eTEaD5kLFrddcb6HbwaYq7MmFqOYRoXEaBV0qJaAL0Tmsb52COBum8Qdt/7vlVunUhWp8cDUCWaZ6VrRlbgKYxdN1gLBI7mO0yGaDXJ12m9eRS0LyTlgvsGQ5hlGVZ1Ya6kuInOutyILRxwX4zitaNitPazV7YaeHSiuJqWm8BmB+g8/C2BvNoEMY013p9QYt3s7Pf9Zdo5gADoRjQuhq76ssSutlsDbpenlKMhbqcfjMkSYvtMIgIGbc5KJ3XguOB9cTPFP/Nrhsoz9sMThiYOp220y55CCOgFYDmV8MIrOd2bZWOC0a0djXQKUvoHVCynDRskHngz+nD1XTA/oaGRevh52wF+/MGfIfiANqjOK17placucjGXiR3XGEFXUiLq4G3fY0G45x/EHj6P9L+CRbK2e0N6CaUTy7rokGyEF+gp7BcEzLsuq2zmWcNaIMSLJzW1H6ivaaWQ/X0LA4pbVOF1o42j1fLxzSICLFqbQNoA8GZi6a51k6qmywO2si1DqxFVEexqzz2hUFg6GkAEg1uAkiQMGi/sQO/l2kAQf5OyH3TdJ7Z1VSuXjwNix0cDbLtFs1VmCk77wAq2oC1OeDMfdY/e/yb5Nqo2Zp5F0VJjbYZ7UsfuWCLWJScsIDxNOVk8jWMkYXYtqtoMbbjdeTifPQfHP2Z5WBE/QyfS/48jb1EA3ZK6zXHazbR5VrrXaqAtolIe+0eOwY88vd0/457tPZCIHPRWifQuDrXsNOaRWt/qdZ1kiYihJ1GbWmc1paitdUQRibNYLYSvxdVSYPZ0qHGg+hdovODdOtUtGZHcq6c1oNPUSZ8cY1WRNFT2Qq8/YfklB99Afjhuw0X7rIsq9fpHQ1laK0qRnGBF5GYTJuYo/9DjpnG/faKYWao72njXGsWZ8xiZVwNHOx+QLufQfGAXTiyDDzR7SLuZWmcXivJqzmy0v1KMKKKQ4ZOa0B1W9/pPbIhbusRGwNOWyoNCj96OArKaaZ1dQdFWkXWyGWloDqtTTbA3A2Zsn9R28Jtro99hdpQMpNc69mVEKaWQpAkYE+ztkdwnGu9Nq8Vy8zikZhDvwPsvpM6wH703uwPSxNohFe1YoedPGsmTbwjvzfbqotRqjgzO8xchkaYRYMwddvpehmPasYGA0aV80dLVTE8ntT9q6MBkad0sxaYXb9ChZ+J47bytfXou73TZu8y6ZyhHA8y15f2czO5ROeZJpN4kNpSP0r8StSD2bmPY1EyNLRNL4ewFo7BI6UWEPk1Gg+sG+cku0GNBxnMzt9jFhUDXiZRKQ176D21OuM8ZnDSeWSXJcoaPjxGYvPWuvRCOHc0jCa/p4ePAJBJULd7rWTXsUmuNQ8DTo6UUaNtrD7XrDs1H7Tu4mU4IkRvHFDQz6GworbUj7JCH+Tk+X5qnvXt1sdQs5VMjtF1w9k3TDQWV40bem2Phs+S1pDveSoXjGidT1t6slMsxdl8+j5qu/T4gDd9xt5wGYBO2GVNJCqYZBPr0bdNAXRhLyv0IRyLOxP1eXryjtuoPR6gBesb/p3uP/ZPaQWkboMWAk20XtSEtV2vt39cdnGaa5110Vq5sCxPmAqlyZ0BlvnEengTULfTOl4G0MQTg2GMo2k2MPrvmbZqOokVUHOtH03r1O/TuWzOTS47H5R2QslS7rqRnoOiSlqEphkMZEQoGksoSqUM3dpA+nRTlln4Myx2qHnWWcyK9/qAq95F949aDGSMRbUBjNfenf49agfOCc5WRMjYi0AoABRV2Wvhd5Dnl1VYiGUR67V/T4vOMz/WXNg20L9nU4TZIS48XZfd4iHDRYGR5zE+QQtldqiqsRdW16ZgAPjB79Pnd++bKK5Kj160djFQsVd37e5200nBrlS9YKJGhNgTra3cmIDmohhMfp4iQc0BYyVa88Zvvh+Ias4rfZ51cjZyOli0NowHqbYophqhvoYv5WbY6akf0u3eN5qvvRr2AL/9XSoGdz8APPB/Uo5laimElVAUXo+EjtpSeDySmsPbPzEHPP85+sHrP5jZZ4mLzSa51oO6TGsjbLmB9ASXgAFdO34GTutuXRHosW57UWAJsEBav8vaGaSD58i0VhWjWtfKm4DitvYobutci9ZWQ9oYdlpPZNtp7S3QCke6QpUaBWSyAd6hzp1JOg+6yBjXhjEaZ/Lya9ZVW6qKj4CDQibD79XqrvQCgSQBv/opytyfOQc8+lF7jyFwzuhR3XBnB+7QzlfTGmeux7Czjt+bRi6/tOe79QXNwb3nTeY/x0YLfSEviXSzILhdf3IpaNx5zcz2AFMnSRtgdzVA3aks9nNUp030+oMt93l4TcsfNxOty5tpvSzHUorfyXBnrpnTWpKk9IVVfowMndZ8HmmvKUnpKK4t9aNUEc85qi1jWLTO9sDXbDit/SUk7AK2B0+rqN1P2RWti+bJCLTNYggjo8bKJMdXOTHPMWlyrQeShjAyHbr3rakuYTcahNl6MxWZ5/sSujMjsTimlumzlC7TWpIktXNggIt3wQDFYQHpzVkejxb7YhERMji3hnA0jhK/N+Xc16muO/Oba33BiNajC/Rk5gMWHHmfkpAhvTZPLmsAePWfOlv4SZKjiJBedlorjgmPR8IuRTQ+68RFxhfr3W9I/PoV7wAufwe1V/3w3ZRpbAJHKnBECaA5OeIjx8hNU1iZ/RxVQJdr/aS9TXC2ReuSWqBA2ayYDG9LFiBsi6Hs9rRTgbYYxmg1hJFRRWujavjKjNbCZec1bLuaKnlrs2nD/vVC7PxqGDNO2oxlGTihRIMc+h36DHFblIssr77pVUR1udE9ya2zTohFbGd6Gx4Li9YNZWorb2+y03p5UtvIpauuOuWKd9LFdfSo+VDWcz8lV0BxDXDZ72TncfXD7WxEE6VFjQa51Z6ozk7rme6NG+K0PKXEC0maINq4V3tOH/472wJfz7S+CJRUUFULT1mKkUmmupNyp+UYqqbosTgLWHXVmRXsZBn46YfItVK5BfjVT6eKgU376T25Nqe5UWwiyzJ6E5zWDmO0lqeApTEAUuJinjP8B56kz7wBsbisnlc5q8+MrWrrX9IGeOIEuQZL67XNkhHlzZTnKscSMvy0bhtnedaAlmltOIjRqdO6cT9t2tdm00ZqOSYa1lqs9U42IzquBd7yJQASxfA88S8J3+ZOto5abQPMXW2+k98lB1NlOw3TygRuXR17MeUzHovL6gabxelkHMeD9D1K7yPOTp49T4OMHRKNxROu3U+en3GeE59JnrVRNIgedlt7jmB5xH1cmB3sDDhlp/W40foqGgLWlXx1p05rQDeMkVxTsbisiknmTmvFVKJf46wvaq5BJ4KFOozR2CjAQxj3tCS+Zl1W+f1GcHeG3fz40jrgTf9F95/7L+cD2wX20EeDOCngFVdpreoGESH83uQ9LqATS9IJtN0/JyG9YR85qs3gfW/PwwlFXj3q59tkFkRViR+VxVQgtSyQnVJmLWy9JbXo4jIiRN9NZ8vMN3KEojorWjVRMxlJ0pk3zDsOQ9GY2iXbXGl+7rPMtZblrDmtWbQ2KvBKkpT9XGsu2C8MZrcArzqtMxCtAU2YnHIoWmdrCCPTQKJ17SpFHfKewIot6nsm6XqpDmG8wf7jt11N+4alMcOhmfy+SRat26pL4JFoDhlH6yUgy85F66IKWnsCCREhk4EgZBnw+zyoNSvG60h5L/c/Tue72u325szYGMbIpqcdjeUpHSb6obiOeSp9eoNdLgjRuthP2bujyRWYDYLf4Fd1VANIEgYe/EvazNTvBm78v87/uAPRWr2gN2hCMTudLQdE6pntoU2Lp8B42uiv/CstXtfmgO+/0/CiLsuy+hzsakyNB+mYUxYj219j33XuhLbDFDy/Op2+pT+yri2ssyVaS5KSSwnTiBCuhvFJ0XamNV/M7WSit14FQKILaFI7EH9WrNpO+HuGTmvOs27Ya68FxufX8p14CJ4Jya5zR237w0foIuQv0xZ9PDXZRZZX91Ti54aFC1f89EPAv+8Gzpk7OKzg52VbfakqWnNkiHaAD9FtyxWUj5pNyhrI7QoAR7+Y+n1ZBp79T7p/9XvMB8M5pe0qEt3W5txPvtbDorWdaBAAqOqk80kslH0HhRnssq7fTZFHzC0foVznoWe01zoNeufcwOyqlnkWiwDDSkErF8VDRsls27NCj7U9WbQ2c9W9/HUaoCd5gd/4X9rQJuMr1D7fDiNCppZCWA5pMRCO40G4/bluZ2LOX/NlVLQJL5se09RSEJGYDJ9HstzcAVBmQtB1MqFDRx8NYiUMSJJOxNKcj+o1II2LwwjOtJ5ZDlFrbXhVc4Y6GcQI0EBEjnzJdq51/+MUVVTWaM+Js/eNtMYBgMc/pmVUQzeEUTflfntDGTyIY9+Q8nPX3p35mqb5MnrPr0ylFL0nl4IIx+Io8Eqmg64056HNjTh35hx4K0VAQXaV+Ts4t4ZwLK7GzSwFo3h5ZNHZH8lAtD7QahINwjQdQE/NzfBIMvac/6yz43KImmltEf3DTuulYBSruvMQAG0Io9dP8XxOSYoEml4OIhqn802jSds+m0r6ZlYQ40I9O/Iqt9hb5zGq09rYlcl51vuSROutTuNBxrjTxcHQ052vA658F92/748ts4sFLuEhjE6iQRjeJxgUFDgeZKduj8vnu7T50Wd+TLdm0SBM61Xkxg8taZ1oSYwtpo//UYejWQmiPCB4/6+nfo/F8+HnyCRkg9VQNEE0slW41ItsVusIG7NdppdIyPP7PKi2iByzLKyuztA1G5K5iG6TQRPxkWF3qqt5JkZUtlOnQHQ9I4NSAuuL5Jrlv58JboYxBpc0s4ODOROWKOu9zuggABlb69OL1mzuSCgCBQPantBq0GAy/hKto80gIsRMtPb7POp63bCzY66PhHCvH2h/lf3jYYNZjxYRMqbLs7bTCZlyvrEbDcLwmsti7cfdqLt1nS4pj+80qjkaBo58ztnvWHBBiNY80TNfwxh5aMivHKA2vrMTSqRB94PAK9+lk9ib/ttdviy7hsdfpvYmE9bDMbVtYqeuCr1byYs7Z1e05miQrhuNh+sVFFO+dVElOS4f/tuUH5kIBLEUjMLnkRIC9rn98LqostnONO/WDF+hVrlKl2s9dYYcaCV1QEVL9o6BK6KmojW9Z16zmwZvTC+HsGQnL3mGndZ70v9scZW20GCBQ8GOE8jSac0DDJyIXWq+rXXMAwv4fJ4+N+FATGKX9d43Ub4soLtQO6wuQ3MtlPrJkZsypMguskwXJDkG/ORPbC9CmVA0plZQt9eXqZXplGIH52JlMxpEz9XKQMaTP0g9Hw0fIZHOW6gNbswG3gJdwSPDiJDVWc0lzu/HdHg8WpFooyJC1DzrKxO/XtkGXPOHdP+Rv7fl/NY7reOyrqtj4hXK4C2q0lw0uUB5nm/wnECJ34NmRTThBeHkUjBVtJk+BzzwF3T/NX8LtFvEX+jjJRzAzwNPBx+aX7Nu5U3GKBoEIPc+X7dNIkLYYdRaXQyvQSZmMoYC/4hOtE4HO5b0cQE2rgFm1JT6Uai4jacCIc2NWVTpTmRThzG6yyY3RY0GeZP9qKKr3wPc8H/o/v1/qs7f4PeL3uW3vaEMt3leREN4lD5Hl78j82P2l+hE/MQYIL4GtFeXwOc1Xp6zA3t8MYhIOqdzPKZdM3a9HmhV3ssuigcsKO1oLMdNO+sBAI8mD35Nh4s25FOKa3dfOtEawMA+Ggx8IPAonWNygCzLungQ889WWaEP5UUUjTERSFpj8QDD8iZ3UTNJojV/1psqi0zPN+3VJSgq8CAUjWvCgJtoEACo5+Gvxs/xGWU/si8pg5zPc2OL6+ZD2vSoTusrrX8umdv/iVyRS6PAz//C2e9eiCwMUmxeNjrV0hENawOC3RTD1U7ZJ1LcqryW0ceDaAP1rGLGlrS14540orXHA+xS1s8mBhN1/2TRJbUlnYt36gx9Prz+1K5mgPaQzYeos9kiqkRP8swSWxFRvEbhDjEzbMx24cHwTRVFlkKbZTwIG7OqO2xHRJnRn0a0zrrT2udXCr9Im2u9HIzgdZ94Eh/+QRojDnefldRmPgSRr6tODFz8sxVtQGltZo/P1O2A7ClAmbSObQXz6p7ACo7RC6xHNK1k+Ah9Pmq2Otdv9BEhOgJrEcwpA0yN3jeWBReeY9N+jTPTFgvLg0+TAQRaBxYbRNLB3QSDs2t03nRqzuL3xuQrpl0C3I26sylVtO6oc+m0HnsRiGbPcHyBiNZ0YrM1iCHLBCMx9QJ2295GeD0SAusRTM1M04YHIPcNV3WcUtEC1O0CICfmDibRN7MCWabNZG2ZJo7vUd5cKQO4zGDRereFoFzTBbz5C3T/+c+RgKWDHWtb60tR6NM2imWFPlxVsYRdnlHIkhfYYfPD5Aa7udb6yejZzHRNI1rze/VAW6U6fTptS2Q8rjlX6m04rQHDiBBZlm21hqtO62yJ1jycauQILSJNYCH2cCc5e2y/dyPr2iTuy35b+7raEnXKccsWC+a376MWXdeZ1ktj5CAAqA3+px90dCxDc2uIy0B5oQ/15YVqlmpCsSMS1BagO1/n7jjT0X4Nxa1Eg8DL30z8HrusL/styuTLJmpOcIaidd9jAGT6f3DSds2irotcdFewWGW0GX/1n5JANn1GK9JYwJE2XHhRP0/6z7Anh5f6jlcj5i1CszSPW6pn1bayqhK/KhgnFJwj68APfo/cKttuBa5LM7jY5TBG7pq4qqMataV+qis56aRIHsKohzeAJsMYh23mWTOGojXnmvM53goWsXTOR3WugQvRWpKkxKImdyDUbHV3HVWHMbrPU04hEtTWM+miQZK59W+BQ3dRkfH77wJGjqYMEgWA7fWl+CMftW/Lh/8g800lo+ZaJ4vW9L7ZYhINAgD1ZYUo9HkogsZsHgUz8gLFUBRVkTOI38suXgde9+1qLMMtu6gY/5gT0Xp1jkREQOueSPcroai6XjAdwqijsusKPBg7DA9k4Ml/tX9sDphdCSMYiUOSrFvkAX3MTlKuNXctlLmIBgF0ojXFg+hdW2Z4PJL63laHMapt4YfcPf7aLBWKdayGoup5bG9zojGmptSPCkXIT2tCWpqg50nyOBfVC8uAX/8C/e4r39XWjRcrP34/cO97gWNfzv1jTZyga3dxtbJ3dUj7q0jIXRpLyHidXQlhfjUMSUo8B7OItLgWQWDNxPjT8xBFYNRu18RXK3YpInL3zw3X6aM24hW12BITXYJd1ttfS8VeI7hb1GauNXc41yt7yrRi7Kque9FoSLEeLqRaGDcm0uRZM5aiNXeDuXnvJJHWaZ3uNXJDtTLTyiB2Qs/RwXl0Ty3jRy+PWheXs5FnzfBeePY8rbPt4LZwaYW3ACsV2wAAN1VOGw4zTaa00KfuF9RYGTd51owqWj+T8BkfUD4zjRWFCfMWGMuZIU6jQZi6nTQLLRZWY5FU0TrNGoJRZ9/MrZLWsTxBMbV2n5v63ZSwEAyYRi2yaW+3gWhtq7PEiDSzf5xygYjW+XNaD82tQVbEpNaqYjVQPv7g39Lk7pptwC1/ndmD2IgIUV0uSdlAXBGZCASxuGacz6WyPEXuaSC9C3rXHZob6ScfSHCtnFUunPrposyvl5HbdarqcneOLLvwBXjoGRoOZ4ZetM4mVUobj0lGp74CrEU9pBFEF4doMegttN+CzS0qumGMC2sRrCmOQrMWY0DLaxtbXEdcl+uM9UWtvcjJxaKmi0SNeBQYNC7ARGJxtYL5BqVzISWH14xzP6OWvsoticdVv5vardcXDIe7WMEb8TsP0rGMLa5jJdkVagcWAcqa6MLQ/TNbgiPD742tDWWQJAkVRQVorChM+B6GnibnbHlz9t/PjCQBh99N949+SXPuzPZobpBr35/9x+Vc6+EjaiXaFWr12abLmrHRGpk14nGt7bnVoNhZXK2dex/7mOXiczUUVQWL1+6luBg1u9lN4ckNBUUYrSRh+XZ/osPDMMP0F39Fz3NpA/Dmz6cX1Fm0Hj9ufa5PokfnnGXnlqOIEDOnNaAVWUZf1Fo7dYyo8QH2FqRbk5+nwCitLySv8eMno4pY+ngQ95nWANBcpcvkdTuEkWGn9fjx7LkBex+hiJaKVsowdAIPbdt+G11zv/U2RKboudter3P5rZ7AIU8fQnIBpna/MzvHDehyrRMLMby5NhvCCJD4mHbQFcPn7B2307Bd9XVw77Te2ViOG3fWQ5KoQJbiIjZjUtkc12w17vIz4OzEEmSZnH0s1FixpbYEn45S5rh86oemgwIzgV3WTRVFKcO/kmFxZzJ5GCPHg7jJswa0zN7lCSC4ZGvwNqDFLqiRUm4FC3+pJrIkPcfnJuk1aygvTHnNJElCl7IeNp11wPB7tH6P1lXnhParEzsqliac/40LgeCSNrvi+c/nZtitHjUaxGUx3F+iDZHXdcry+aW9ugTFfs0MVeL3qcYf00gkjgbZ80Z7RdWtN5HgszSa0i4fi8uqoGT1ebLMeJVlLc96n0E0CMOidf/jhuuIZNhkc5uy1ptdCVvvV/j5bdhH8X9WsFEqMGJqOppUzvVW+0ogMdM6ZaCdaszKbAhjLC6rz/2GOa0Bbb5IGqc1zz6LxGTrx8+maF3eRI5tOZ4+OpXJ9hBG/rOFNPvk8iL7e/KULHQ3edaMmms9mvBacRe82XuGDQPDya9ZPKbpGk5Fa0nS3NZK5xsPObdrKuE14fjiOqLdSnRk140Uv2cHn1/b5xoMY1wPx1SNdadBPEhHjWakWws70EhsRB874QIRrelFtZ2DlkX4Db61vhSSJGF3UwWu95xES/936Qfe+J8Zt7jYE61TW1cBoKKoQG1RTOtYPf9zADJtXOy0Wtzy1+RojqwC33uHOrxHddwYVGM4GuSlYgd5P25oOkjuodBSgmvo2NAC/vz7J7SKPC9IWg5l9/EtnNbLwQhmleGCnXWlqmsgba41uzzrdtpvdWYX3vjLNNwH2qaqobwQRQXmf6epsgiSBISjccyu6oYOjDwPQKaCjNNNFbute41zrYfm1hCNyyj1e3Gj0mLcM71ib6ATi8CX/WbiYrmgSBNtHLRFLa6F1Xa3q7tq1A2WK7c1uzJ33Abc8ld0/+cfBhbtDR7j94Y+T1UtdvDmTo0GeV12uwaSOfg2GqK6MAD0K87n5/4bgAzsfH3Gi01DarZSJToeocq4G+JxLU/dbssUY6M1MplgJOZuOPBcLxAKUI62WWzH1e+lfLulMdqImsDv1bqyQrxqK7X2nZtcpgWWOizJhUvBIccKSFi+MpzoHE3JMD19H/Di/wKQyAmXbiMFkHuqsILExRn7r48a99BQrl6rbIvWSxPAyiQ59YxcoVVb6LjkmGGH1Og8xwfYdVorQo6y3lCLkI377Ak2ajxIDxCPIxzVJpO7ybQGNAfIRGDd/RBG9fh20/s9vAzM9bj7G8lwNMi+N7sTT7wFwFu/Quuh9Xl8OvZR1GMhIfLM9xx1l/wgdiPOr2a4ztPDTuvx4wnDPHlzu8UiKxlI4wbS0/1zut31errlddDCIA0Rd4B+3VdT6sfl7VUAgMe7bUZh5XIIo0JjeRF6PVvxYOwwJMgpwzazgZpnbeOzza2/48nCvipaN7s7iKJKzaU916M5rdNsgDnCr3tqBQivaXFCborgLHLNJorWZzjOpcX4NdvGBbp0+zmOsGm1UbQz46YP0//b+gLw47tzL+jmg6Fn6ToE0GuRZZEg9fF4XeEiz5rhODi9aD3JRbHUbpZOq1zr8KqW78ozWdJRUKwZJZKiOaaW0ufD0zFZOA8nXwHm+2g+CUeRGFG/i/YusXDCkDYz2OBzdWeN6kq1zLV24gwtqdHORyYdh5MB2iemc1qzJrEajmF+NclMlyWn9fjiOsKxOPxeD1pM1jh8nRxdWE8fpWUX26K1Jvxbxk5mU7SWpMTOYztkewijQg/Ikb4L9geotyvvm5H5ddKbeM3gJM+a8ZdohhddRAgXS3nNnQyLsylrq4njVFgqrHTemQTocq0fBnTd8Gbv3WTqyvwo9XsRl4HIOUULcLrP5eu8weyo3mlKc6gt9RsaBCpLCtQce9udC8FA1iMBLwjRWn0TZbPFwyYsGHGQ/P56Lz7u+xJ98/B73H2Ykum8nhxV832mcRO905rLJRl2PKfNteb8LqtoED0eL/CW/wHKW2hx+5M/AWRZrfbuaU46lmAAW5ZJQH4wksFC0+6xdSnVt4HH1S//y4Pn8INjo/jeiyOUvTalOCez7rRWWoQMXi+OBqkr86OiqEB156cVrVkwczJRuWYr5XXHwuoJfsxmW7jf50FjeVHC7wDIzKHJDleTYYz8HGxrKENHTQmKC7wIR+PpB2UsTWjREfpoEMZFrjUXeVqrilFeVKB2MegH29lG78q8/oNU5Q0tAT/+Y1vOQnUIo04w0UTrFdps8UCtXOVZM/5S4NDv0P0XvkTtv1wwuO5PcvOYkqRtItxGhEy+QhEt/jLNyWMXFo/netXijxWB9Qhu+JfH8I7/eT7tz6bAF/GWQ+R+NKKgSOvgefo/TAUm1U3cUKYKs+cml4Gp0ySM+8uz7qAw4qEwff5alk6oxU0A6KrXidYLQ9S1A1AECruV0+HxaG5jBwsgfdyDKlrb/Wzz57l+t3l2nUVECM+fSCc+MinxIE6iQQByQHsKgMgasDSGicA6ZBko9HlQV+a39zeSaFHjQYKa09rpEEbG69ME02wMYwyvaudDoyFXdiksA+76PoLlnWiTZvGt4n9FSVy5Fk2dAXp+gTgkfDH2K+6jo4yo3UGbn+h6QncHXwc5x9UM7j5McQPpme2lAoHHp12Xi6u1woODiJBgRHPh7FLWoI4jQtjd42AtdnLMOBvZDI9HQltNseq2Rg7c1iM28qwZjg+ZSIkHydBpDeiGr/Zoa750TutG3Rpn6jQ58kob3B0Hr1OTnt/Timi910S0Tjugl2GntZMhjMl4Cyjq0FdEa9KjX3L/tzYrLEx6lLXEC1/I3WPF49qQ9i0ZdHBxvOPAU+r6+LxBnjWjFumM3jO9j9B5tGqLs30e50wn5VpzUaqlynoeRYfO+RiKJuWzs8t6x+2Jg7YNj+NOuj37U8sfk3V7793N5bpuG4vPkdM4gzQdh5NLSnZ+moziogKv+jMp3UCq0zoz0ZrPH1tqS0xfp8byIhT6PIjq3PMZw2sgB6J1jy3RuiPDA1Ngk4WdvXA0pBUosrxPeCnUCgBoDvbb/h3Vab2wRkPk5Rg9L5Vt7g7CINd6QFljbTV155tkWvNnqesG832bFV030HVoaRSYPmurm0OPJEnoqC1FBVZROKnsD3bc5uwYLIYxckHM6PzLOO5cGHyGXkO3HZoGXBiitVotW3PnbMuAfrUqQy/Wr0x/Ae2eGUxKDcBr/192HqSoUqsImWQ0q07rhtQ3FIvHlk7r0Ir2oeOLpB3K6oG3fZUWRKfvRfS5z6nC467keJDeX8IjR9EXb8aTcxWpbUHZJinXOhaXcVJx5vRML5MrLx6h5zdbFwSGp/wuT5A4roMzk9gdoA3VS/NB58U/D4WzgyRpAp2Sa+2kLdww15qdrq5ypG4g8WRhMCGvjlFF6/oyeDySGm+TNiLk5Pdog9V+DVC7LfX7TqvL0Jxj/Pnhk3WPU3FClnWi9SEqqLz5c9R+OPAkcPSLaf8ECyLbEpzWSrFjeoUWFovDdNHj930u4YiQ8w8CD/8dZVy3XJHbqIlMRWuOBum6iVqhnFDeTOeJeJSE6zQcG5rHzHIIzw/Mm+csmmGVZ63n4NuorTMYIOHagJ5pzZ3E79+Z5RBWeyg3DVuucbfAcoAsy3h6vhJD8QZ45Iia2QZoC8OhmQDww3eTkN52tdaNYBd2ptoUred0+Zjb6sucO62tokEYixz2kXmOB7EnWrNIubAWwcJqWBusazf2wuvTzouz3QmFSzuTyY3gxXRCPIhbpzWQUTRFCucfJIG+ujMzUQsASuvw0JWfwYxcgR3yIPDdt9M1Xcnw76m5GYNyM3rTFZ2d4PFoDlKlQCHLsipCsyhtRoedrM7zisu689WJmaouXofe6RXEZaCqpEB14dyiDJl+unc2VbQxgjdKDjbHp8dpPXfAxhBGZktNCc7InRhpvBXIgdvazpBrhtvoU53WSlRFRqK1Noxx3KbTmq8R/TOriPE5zq2hg52SSa7M0+PWhYaEQqYZsqxzWmf4+W7YDbz2/6P7D/2tmgN+0cD7upv/km67f67NIMg2M2eB4CKtbTNxZrZeQeaC9XlgisS1Hl38UDJanqvB+e7MT+jWbjQIs+N11Ek1dZIK6gpji/aKUnVlfpQozsdRvelHlrU8azsFVY4I6XnYMgpudGEdy6EoCrwSttaVqdcAU8PPfD9FTnoK7K/b2bwxZSxac6Z1ungQwCTXOrhEsWeAdv5yCRdRzWIeACpipn2enGLDaR2MxBLOb+etZqlwvnDWRWsbe+HpM7TnKa52Lwyb8FSAOqnLVgdt52tz99LI/JoWxeEmGoTRi9aKHmU3HmRuNSl6h7U5t/vvgmK1w0Q+/wvdIEb7HXxddaW43nMKHjlGnx9+L9qF114G8SBWCQpMp9PPEl+b+HXIAheEaF1XXqi7ODg88axMA198DfCZa4Gvvxm474+BX34UeOGLwNn7gdFjQGAsoUVTT78uHgRDz6Lt/NcBAH8RfjeCHndZkYZYRISsh2OquyM5HgTQnNZnrTbkfb8EYiGqeNgd8se0X03TuAF4H/kbHJS7UV7kQ0vyhUtpRX0kfiUW1yKYWUnvWMwIfs5GngfCazg/taxmOfdMryS2o2Y7TqGsgQREOU4t/DoGkgodLEQOza1atyhx63u9jUEietiNN3wEAHRDGNOfDNVhW7zoCq1oOeBuuggKy4AtSjSMgZjTN604ipWNyx47YpIsA8c5GsTAZQ3onNb2RetzSSfp7W6d1oERWnx7CjTxvHYbcNs/0P2H/y5hQFoysiwniPnMdqVA1TuzorkKu250NrXYLXU7lM+XDBxXBjJe9/7cxpJ03UibiNluyvR1CkfSbL/V+e9Kkva5sxER8sqolj941m4mO8PCazrR2uMFblM2289/wTBqhh0c2xvLUVboUzcKwV5lwZfrPGvQRPmVUAxPyIfoC1w8gNaC9yuz/0vzFIoqgbd8iRxwTlCHMdoT2rgI1FZdjGK/V90ITy+HSBROB58DrUTrzhuUDqn+hE1vMBJTY4fabebVlfh96kZwcHpeu3a1H7b1+wA05+XM+YzzrAEt03p6YUn7PGYiWrP4lA2ntT4vNAvnpOPL1fi98F8g5CmhlvXvvYMKpQAm9r0XQAZDes1IyrWeXQljNRyjOnSaLHRbmdbd3Jnz+sSvq8MYj9s+VM6b3dVYrhZB9jZXoL68EGvhGI4OLFj/geASdRICtkXSYCSmFpD3OxStAeCRhnfRF7LstlY/WzYKUrwhnch2pjWgij7y7Hlbgxj5+8UFXoRjcawMZiha8z5C99xGYnG1m8UsHsSW03q+n8RRr58Kt5ly9XtpPRNdB370XtP93gXH8qSyb5CAq96tFP7l3DnKOTu7/Wrn13A93gLNFDPwJGRZNo3ABPRFuqT3TDSkxebZjQZhSmuBLUrECccoARidt7d/YucjkOTKHDtGBpOCUhLG09FyOVDRRjGcFkPLeL+yvaEcfp8HHTVpCpesJbRfbX+AMIvWZk5rdRBj+nVNSj4xoMURlTUCxVX2jsmEZEOhGVnPtWbX6PKEqRh7fmoZ+jFRlnvKbMaDADoD1+n0cUj67qcs7u3mV8M4v16GebkMkhy3ff1NWNdwx3cmaQbtibnWsixr+ky98fumokgfg6G8ZyLrqr7iOM9ajxIREuv+ha25Y8l01JbgFs9x+sd2hy5rQBm2KlH04Upihxxft42GMDJbnH6WuAuV46CywAUhWusvDo6HMT7/eXK2TZ8hEe34N4Gn/g144M+B794FfOlW4BN7gY/WA/+6A/jcDcC3fhP46QeBxz+Oy2d+jFs8L2Ov3EcTmgH8CLfiydiB7G5i9KJ1UpxA3wxlzdSU+lFXlpo1s1txip6fXEYsbnKSOvcz5Yff4O7kdM0fAvt+HVI8is/4P4Wr62OJDq5YlCY4Azhdfp1yPFne5CVTu52iS2JhYOQIjo8sqt/qnV6BPO48Q9E2kqS5rZMiQvg9yu6ApooilPi9iMTkxAu4nnhc1zbltKjATusXAFlWCzt22k5SnNajR6nyWtnu/iLKjlmDXOtkcZYFYx5aYcjEcVqYewspv9SIRqW6PN9HWY02YHc3dwywsGWZP2YEO5Ya9wI+3efz8B9QhEA0CNz3PtMhcpNLQayFY/DpXAGAFhUyPLeGOC+qd9pYBGeLw+/R7lduAfY43BQ4pbhKE3KcThwOBtROAzVX3SkOhjGe1InWaWOZ9ESCWmElnWgNUGZZ5w1UcHzsn1K+zU5rjrahz5OMkgnluejIXoXbDC5EnStVimc9j6iL5Y7aEtzoeQV/gPvoe2/8T236uhP4uZo+Y2tQJ4tdnBFfVuhTN6FpI0ISOicsROuiCqBNEZV1ESF8Li3xe9XcSTvw5mux7xhd00pqnbXVsfNx9jxGHbYeGsGCmxQYpuJsQQltNl3/QeW5nHwlpTspsB7Bb3z2WfzXozZckMElLf8zk2gQHb0zKzglb8WzV36CusrOP0jXwY5Xo3YXbZrSDlJ2CncPKE5rbvNuqSxGoc96poU6LMho0BVAcULcxp+cqeqieMCfGb0Lx+ORcLMyl+Kx7jQRIdwBVdEKlNbZesyzE0uIxWXUlfnVocR24I3vi6F2paswu25rLa/evtN6YnE98XVayTDTGlCLVPHp8+oGOJ1ry+ORNFFQHVLu0jHLsy2WJ9Qhcn0zKwhH4ygr9JlmfnMH4vxq2Hx4PJ9/mw4475oywuMB3vQZKpqOvwQ89e+Z/83NALv/mi+jTOJr3kf/fvnrmQ20NiMb0SAMCxn9T2BmOYTAegQeKdG4wXBEaIrDr+8xmpNQ3mI81Dodu5SozO6fqV9yUvA1zLXmguquO+wZTCQJ2KN0P5+73/THeJ3JRh9VMDeLB3EaDQJYznaJxWVML5MRzbXTmsXLDF3WgD2nNaC9RlmLly2uphkrQIJZQQ9Hg+gLdIaGtfVFbQBnVXt2jq9uJxX7QgHTuFkVF91PduifWQEgYcDbSV+YOm3r97hYP7+wAJnXJ5nM5PGXJuRazyyHsBqOweuRLGdSbEkuRg0foT1YeYtmDnGDEufhHXsBFVhFXZnfcu5YMp21JbjJeyLhbzmisEw7/iS3NRsHd9pxWs/a+CwFxqhIJXk0I2MWyKlofc899+Dw4cMoLy9HQ0MDfu3Xfg3d3e4cD46eLCYWAV76Gt2/5a9p0XLr35IYs/tOushVtClZYDKwOk0bqvMPAse+Ajx+D/5O/jy+7P9XbL33ThLEyptxf9MfA7Ax+NAJbYdpU7g2myKasMuFhYlkOmtLUejzYD0SM3bexCJaNZpzvJwiScAb/xOzRZ1okhbw1+v/mijCjTxPzojiakSaafFgOz/ULZKkE/ufwPHhRfVby8EoIqPKSc9NaL4d+CITSHRA8oAZbo33eCRy6sMiImRxiFwg3kLnuaEth6iauDoNLAw6al9NcVqzkyIThybnZw4+lSBOJDiKlfcydwl0T1kIfye+Q7e732BenS9vBErrbU9NjsdldfALLwL58zW2uI5Vq4ncyahttocSvy5JwJv+m7JLx44BT3/C8NdZ9OuoLUGBVzslc7GjPB6ANHqUvmjHuZEtdt6BOS+JEsM735nzmAkA7iNC+p+g7Kza7e5zd1WXSfr3D8cQAWkKLslMnqTIotJ6e0UhSdLc1ie+k9BJsBaOqp/1HernqRzbpHEURxaoE8RKdM0SPG8h0PQqWiwHhtUW7KLgLD7p/ywAYHrX2527oZjyJhK85LhhHlvqMbFrS1uA7bYbEbI0RtnoHp/iTLDAICKEC5Pt1SWOojnUzRcPYWy72lmBuV4nWjvI3TWDBzE2RJUog+quzNw4NVtpeHIsDEwnbmIeOTOFF4cW8IUn+9PHinU/QBuIup2aqyhDWJAu23c7nbOZ6z+oFg/nVsP2XPp2YZFl9jwQDKhrW33h0oy26mJIErAWjmF2xeCYeh+h82HD3tQW0qaDtJFYHtccv2nQhqQlbmhuVSJC0orWboYwKjET+1srHX2OEhx+N/2F8sd+aNntZJd4XFYLQnYGMXKm9Wo4hqWgsqaIBGkwIJBZEUgRfqSFfngRQ12Z9eBtZkdDOQoQRVkggyGMAAnA5S10X3luTysZ5HubK+AxyZktLfSpebemwxhZsMg0+kdPZSvwBiVm64l/AZ7+pK35FZuaZGFy+210ng4GgFe+m93HkuXsDGFktipt9kPPomecZnZ01JYavoe5SDe7Ekpcm5/5Md3u+VV3w3h5QO3gM+pnctRmPAgfL6ATRONx4PS9dH+fg4IqR4R0P2BqbmG9gU1qHVaaSDymxbQ5Ea3rdwOQSIdYSRywO7sSQiwuw+uRDM1zyWyppecvQZPgIYwZ5lkDWqcGF8HMyLrTWpLSRoTwnuDW3Q0oVQxrho/P2kFJnb2B23bw+bXnN12utTqEMbvGPt7nz5QoAqlN0bqlqhgeCdgfPwdJjpFZyo3JRQ9HUww9o15v2quL4feZny/ULgZ+7/LA2K03ZbYGru4E6nZBkmO4wXPSsalkr2cYjdIi1lDkXqPhAsWkto9aWA2rBamsZVrzc9ZyORV6skROResnnngCd999N44cOYKHH34YkUgEt99+O1ZXnZ88tEwrB7977mck5JU10uCny+8Cbvxz4A3/BvzWN4H3/BL4s9PA38wAf94D/OGTwO98D/jVTwE3fwQzO38bj8QuxzlpK/2NwkrgTf+NLS20UHPksEuHz69VlJIiQtRhWwZtUwDg9UjaEC6jYxp+jgTlklrnA8r0FJbh32v+BqtyIbauvJTo/OMJzDtehx1NVXTcuRatAW3hM/BEgtPaixi8M8qJMhdOa0ATnXTVTGo/oderU1cBThiqZwTnAtbtpFgAJxQUq/+P8sjzqgDtKtM6kyGMTOMBEuXCK5r7FcDMSgjLwSg8krbgYiFpZH49MT+KiYaBk9+n+zwc0PRxuS0q/QCKscV1rIZj8Hs96utUXepXh5alHZqph9usjQTCylY63wDAEx83bMk2igYBqMNkW30ZbvK8Qi1WjfuzV423QUzy4u7wB/AvkbfhodJf3ZgHZdG6/zFaeNuFIymcTlPWY9NpPbUUVC/wgI08dj36PGu7i5/WK5UOAxl45O/VL/dNr6rTnmuVTcTupgq8yqOI7m2Hs+NSSwNn/W5pqtfOG72P0Abu3veiBgGcjbfjqa0fyuyBVIdo+lxr/RBGhhdjaYvNXIRq2EPnVivU9+sT6vt1ZMFZnjXDonXlnPL4TqJBAM29NNOtuwa4F62LFad4hzRFX3BbDGIkSTdQM9Hl+9IwCQZLwWj6vLwsR4OshqLq9W97fRlw2W8Bv/Fl4HX3ADtuQ4nfp24uspprXcaFK8rv5Q1SR5pNOAAU+rxqUcHQacfrMRZlEn65THPl2xzGyN1HyXmH1++og88joX9m1Xoj42II42mlMLjf5hBGJsHh13yZ5rZ+MnO39exKCOFoHB4JaLLhNiz2e9VW4wnOtWaXtbcws81cRStQUAJPPIJ2aRqtVfbajHc2lmGHNAqvHM183os6jJHWr2cmrIcwMqoD0czEwXnrmeZZJ3PgN4CDv0UFnUf+H/Bfh0lkzPX8nVwgyzox5Wa69XgoCgWgSDGH/19feLIP33rexJ25MEiFLk+BO1dzMg37aE8aWcVCL7XemxmzKosL1K4lVSCORbTz3N43ujuG2m0k1MoxtXtn1OZQU8DAaT3yPD1HhRXO1qJbrqXnYn1B24Mlwe5dNvqwkD8RWE+d9TX5Cv0tf7mzwo+/RLvOJxWWOeKoobzQckAls0UtHuoiNLhwWJeZaB2JxdXXaatJzAOT9UxrwIZorbjimyuw3aqDl53a2YoGYbjz2GrGUzymiclZdlqzOS9Uo3SNT9sTrQu8HjRXFuMa3sNkEg3C6HKtjbQZI1JmhrjpWjBDcUjf4j3uKM8aADrnaaDkM7F9CMNlPBN3VunMP2wwbasuRlmhuTmNz3fjgSCCkTT782w+ZzpyKlo/+OCDeNe73oV9+/bhsssuw1e+8hUMDw/j2DF7w5T08JOVduK0nhf/h24vf4d1/pbHQxnFzZdR+/2V7wJu/ks8tuOv8QeR/4t/bP0c8OfngY8MA9tfY2/woRtMcq2tBlQwLP4Z5lpzNMjO1zsXRJN4bLYGH44oi6Kn/0PLAuPM3V13qO62nDutATUUXx4/jslpcoQdaq/CNmkc3liIhn3UGAzuywYGovXCWkR11OgrwCxImkbKsLvTbQVaKUaEB45gWRF/7Sy62vRO60hQbVXOKFbA49E5ZrWIEHYUt9eUqO3P1aV+NCiDnQwdkL0PA2tzVDTaeov14zrIteZFxbaGsgR3Mw86tR0RYidK4MBbaUhMPArc+z56nnWoQxgNFuzbG8rwGq+ygdvIaBDQwIojkW34TOzX0De/QY6k1itpwb++YMtRC4BeAzXPOgui9cKgZWst51mXF9HFvXvKIpYpGbt51snc+rfk/O19WHXQcDSIXpjd1VSOqz0kIMQzaatzAH+ut9eXaTlrvQ8Dz3wS6H8cYU8R3h/5AHrmHRQhjFBzrdOvH8yeG0DrXDLFrHPCiJYrqJgdXFTb7Tk+IF0ucTK8+WpfVRb4bU5Fa8XVsjaLpXkSmjMRrQFqA+6UFJEtU9Ea0F7DpCGAx4a0TORXRhfNf39tXrumZCkahLMx68r8qOY4l/2/Dlz7x6oozufm3OVav6iKvnac1oAmCqS0PUfD2vkwOc+aUXOt04vWy8GIKurvTBoEXlFUgKs6SXh97JyF29pFGzJ3s+xvtRZAk+Fi0eJaBIH1iOa2PvmDjN3WXJBqrixOWDdYwW7riUXlur+sFIHKmzIrung81FkEYJs0nnYII7OzsRz7PIP0j6aDmR1DkmjNgzPTitZWwxhjUe39kk2nNfNrn6X/ypupw/H77wL+9w7bQ343DXO91BXkLUxsv778LspTnjmbMBQ5HUNzq/jYA+fw1/edNO4o4WiQlsuzM1fF41GHrBWO0AwOa5dfUq71wJN03S2t17Kp3cARIed+hlhcVgek2cms35IsbvEAxl2/AhTYz6qFx6sdx9mfpnx7PRzDgPL/zU7r+jKLWV8cr9d1g/MOSZOOw0ml6GanWAdo5+FxvaiuOq0ziwcZmV9DLC6jxO9V949mdOqiHmyv09OhitapA09lWVa1oT3N5diprB0M153ZzrNm1L2whYFrrpeGWReUaEO8s0S/Ig4XtLJ4bk+0BmjdrBpvsjHAr/1q2jsFRjA/3gsgfaSMVvhepTUnm83cDmHUo+Ra3+Q5jpZK+7FnAFA68jgA4PH4ZeqcO8cYDGM8byPPGqCI4nJF1DaNugVoT34hitbJBAK0oKmpqTH8figUwtLSUsJ/TGdyG046ZnvooiZ5SIR2gRrzkFTJ29NMCzJHDjs78Is79ExCrII6oKLBSrRWjinZaS3LwDmlGr37VzI6vMW1MCaXgrg/fi3CVyrC9Y/+kCrUc71Ugd/2GlUc6JlaSd/qmykVzUDdTkiQ8SrpLFoqi3Ddtlrsl5SLSdNBd21jdmCHim5AGi/CmyuLUOzXCgTpndbKxbzBYZ41s4VE6/gwOZvryvwJj28Gb3SWQ1GsDLxAbdelDZlfxFg81OVamzmKdyufJ0PR+vi36PbAW9MvvuxUlxW6k6JBGO5m6LGa9qxnYVA3MGiv8c9IEnDnJ+h5nTkLPPaPCd82e14AYEdtIW7yKBeXnXekfD+XnB7XziWmsTbZxuvTsg7tRoTMdNOgDW9hZvlnpXX0GgFa54MBLKbctrcRxQVeBCNx+x1AXBRyKlrXbgOu+n26//DfAbJs2IHTWVOMaxTRerra4WO4pFcf+cOf+4GngEfpff7C7r9En9yqTu12jU3ReikYwdQSFVn0ojVfI89PLltfl+zkWTNeH20MAfX9OqyLB3FCV10ZmjCHenkWsuRxLtj4S9U5C+UrdP3LZBAjQO2aWyRFjMxkCCOjuuU1sXQlFE3Y0J0YCST/lsa5+6n417g/Ky3GgHauNzr/MtvTFZ3douZaH1OdYJ2ZitZDzwChJRJzzM4zDnKtef3ZVFGEypJU88ctuzgiZCblewBoiBGfT206rUPRmPqecDKEEaD8+lql+DCSZbe1m9idFsUBPc5O62UlbieTPGtG6a7YJo3bbjXe0ViGfdIgACDWlGEXoipad0OWZZxR1gxmQxiZrVbDGGe7SUzxl2eWH2qGx0Nde39yDLjpLwFfMTByBPjirTSk0c0Q6HzAosCWaxI7gooqqVsEAF74gu0/d0Ipxsuy1vmSgBodmIVoEEbplG2Zp0gs6zzVpFzrsz+h2913ZmbG4sjM3kcwvRBAJCbD55HQmEYM1R/T6MIaopGIFlfipqC6R3GLn7s/Za7V+allyDLt6xrK6XwiSZJ2DUgWkFTBKI3RxwiTjkMewmh3cFx9WSGKCjyQZaWTNxLUnMkZOq35vNFRW5o2Oqq5sggFXgnhWFwdkJ0xFk7riUAQgfUIfB4J2xvKtD2lkREqZ6K1jb0wi5aN+zM2MybD+8WajoMAJIrbW7EoauvYWinhoKQMbc6G8UaXa10yRoW3rbaHd64Bg08DkKkjoyIL1+wt12LdU4J6aQkHPalFD1PWFyApneuPxy5zH3fDa7CFATVP/ZxJ/FsykiSho85GRvz0WWBliq6tmaQ7GLBhonU8HseHPvQhXH/99di/3ziH8J577kFlZaX6X3u71grPlZHRhbXUVhgjjn2Fbnfc7rqlnqtFyW/wHQ3l8Eg07X16OUsnQYBEr9J6WrApGbbr4ZhaUTGLBwH0QnqS2DZ5kjJGfcXuLmA6+G+3VRfD//p/oszNUAD4jhLb0PlqoKgCnbWlKPBKWAlFMZ48NT0XKNWv6zyncGhLFXY0lmE/O0lyFQ0CGA5iHJw1Hg7BuZh90yZC/gw7rfe4O5Y2GoJWNH8OZVizvYEp8fvU9tW184oro+O6zNuu+b02+Yp6sdLE2cTnhqt7KUWgtXktiz1dNAigVZdtTE3m93JyuzN3CRguMIzgYUaN+6yjGErrgDd+mu4/+1/aBgDmzwsAXOnpRoW0hoBU4VzozBC9aN2/UaI1oHPp2xzGyNEgnddn7gCyGETDnFScoIfaq9RN1lk7UVFr85ozw03b841/QZ0j4y8Dp+817MDxLQ2hSZpHWPbiFSnzYTfpCKxHMKNEpWyrLyURo7KdcrvlGLD/NxC77C4ADrukjGg+BECi8+2KiUAGTVhsqihCRZEmsnXVlcLnkbBsdV2SZeu4HyPUXOvHAUC9XjuNB2mrLsZVXlqsR+v2UoyDUxQRqwtj8Hs9qLeRPWlFa1Wx5rR2MhTSDBbiZ86q3QwnRhahN0CdHFs0/301GsRkIK8LjKJkktm+AU7rYaWos6XGXrYlu/xSHC/c9bbzdeYFe73TOs21Mt2AnluUXOvn+uewHjboppg+Q+eCklqgosXysZieqRVEYjKqSgpcDRNNyLUGsua2djKkjWlShzGy05qHMGaQZ82oovWE7eeptaoYl3kHAQAzpRleI3ho+Gw3RhfWsRSMosArWRpsAG19bJhpreZZH8q6mJKAvxS45SMkXl/22/S1V74L/OdVwKP/BIRyPEg+U6ycbBwR0v2A6bC4ZE7p5nS8OGQgWmdzCCOj7N12hs+iGEHstNjjakW6VYo2OKsMLXQbDcK0XAGUNQHhFSyfpTVnc1URfDY6KZoqiuD3eRCJyZg/8zgJNUVV7vbaW2+iQs3yREonEu+NuPDOqO5z/ecosk6D4wB3LkcTp/WEIvg2VtgTrfWi+vD8Gs0Ek+PUSVne5Py4dAwkzY2ywuf1qAaCoUzXoAx3nRmI1vxabasvQ6HPq+0pjYxQuRKtOSpzYZAGVxvBmcZuB/GaEI7GVeNGV0u9ZoCzYSYDgMPeXvilGBYKGlLncbhFcWy3LpJpqKvOem3Nn6vxxXXE1K6FLLisAcDnx/GCQwCAvatH7P9e32OAHMe4vwNjqMeAk/l+ekpqNO1K6UrvNtFDjOhQi4cWnyW+NnVcB/gy24Mks2Gi9d13341Tp07hO9/5junPfOQjH0EgEFD/GxnRHKz15RatMMlE1oHj36T77E5zAS+oupIcOMV+r5qJc87JEK50eDzaB0N50ftmViDLZMu3Gn7Awt/w/BqWgxHtG5z5te3WjAUdfmPvbionge6tX6EBAjHFFa7kJ/p9HnVRej7bESpGKNX66z2ncai9CjsadO2PuRSt+UKzNKYOz1CHQyRdTDtrSyFJlNmZMjgpHtc2UvUundYVzUDVFkiI4zJPn6NNFbutvSPspMhCdbOsXnvuFQciV19TnNaqaJ30Xjn5AxK/mg6mH4gG6KYmL1HbpwW8sEgRrRscOq2duDJ3vR64/O0AZIoJCS1jWecKNYoH2RGg1+Tx+OXkvtxAuNUXoCzPJf15JZewaD3yPBCy8TpwXEAm0SBMmmGMsizjpDJs6kBrJfZyVJSd6wA7hGu3u8syLasHrvsA3f/lP2BgijaWCWKbUgw5IW/D2Zncv156gbi8qICKXfw6VHcCd34CW5XP+2Cm7ZlFFZqzb9zcIdo7ZSxC+n0e9dzTbdYltTgMrM9T15Cdcw6gbVBHngdCK2qGo9N4kAKvBzeWUFFjrtrldUsRsbZLY2ipKjIdhmaX1soCtElKgSAbTuuKZnKY6gZqcjTIXqXwfmpsCdGYgTFhZUbLcM1SNAiQZ9G6+SC1rq7OoDRIDly78SCqYKEXrWVZl2dt0VnXuJ8ed202ZZB0Mux43mUiKO1oKENrVTHC0Tie659N/QH9EEabxfCTujxrJ0MYmQSxhB971xuQqdt6VC1I2f9sq/EgXCjjTOusOK3JibzNM45Wm2s+SY5jj0Tro24pw446Xq8uDuPcMP1/7WgotxxyBUC7JsyuIp58TeBze46GCIeiscQ9ZGUr8ObPAe95jATZ6Dq9R/7zSuDlbzibreGQhdUwPvDtl/HCwLyzX4xFqZsJMBYmG3bT1+U4cPRLtv7kyVGdaD2YdDzLU9RNC0nt6swKNVsRLW9FgRTD1d4ey7b9zjpdfvTQs3TuKqpSI0Zc4/EAu6iLsaCX4i7bqux9ljweSR3aFjv5Q/rinjvdzRLxFQI7KTpAdZEr8GC/5PZ91RGqvwYMH6GO2fIWd50K+jWwzvHt1GkNJJ2HuZu4bmfGpihtn+3sWpm1XGu90zqp6Ku+VsregE0lA7OriCSva1TROsNhg8mU1NDMA8A8msPFcGQ7DM+vIhaXUVboo+gWfj9NWc8KYvaGKdLklG9/VmaWAFBF630R+ttdaXLQG8qpSyAuA/Hex+mLWYy5+GWUnvPWmaft/5JizhqtJX0mo8GiakTICciyrOp0yUUxIzqTI5GMyFE0CLBBovX73/9+3H///XjsscfQ1tZm+nOFhYWoqKhI+I+RJMmewg8Ap++jTNTKdtdCRjQWV98URtW8PRzHkauIEOVFN8rmNKK61K9O5E7ITjqnVKO5BSoDUqq9la3Ab/wPRbBInoT4gp3q8IENEK07X40YPNjuGcfhmhC21hWr7Y+LlS6dy3YoaySRVI7R8A1AzR1Lfs8UFXjVam9KRMjiIC2UvYWZ5YYqbRhXSj2O2ldbq4rhQxQVs8pmIRvDDwBg22voVokI6TPJbtYPEU1woZ9QokHsuKwByq1XpyabV3WDkZi66OEOBYZF69GFdayFjad4J+Ak/xag4V6VW0hU/8Vfqw7mhvLCBFcoUzVKgv+DkUOqo3UjkGVZdVrzumHD3NY1XeTqjEdoqrsV4TXtZ7IiWlsPY5xcCmJ2JQSvR8Ke5gr1XGjLae02z1rPtXdThMnCAK4P0KyCBFeb8lw8H9+T/WuTAWqXQIPufHfjnwOH/wD4ne8DRRVoqSqG3+tBOBpX8yJdw88dx6wYYHXNZLdo96SJ+Mif58Z99h0CNVupgBmPYK33ScrRhfN4EAC43EOZe/1FLq9b9bq4gAzzrAGgyx9AoRRFFD6g0nzt5gh2WyuOSm5F/40r21Dq92I9EjMeeHj2xyTCtFyeHQFdgR/Lyh3K76WxRZvXBbsUFKuuqENSL+rLC1FqMQhHT0eNQWTe9FnaCHsLrTcMBUXaZjJNrrXmwjHe0EiShFt21wMAHjtn0AHhYgjjKTXP2lk0CJMiWgPAzR+m2wzc1lyQcmIK4HgQdRCj6rTOzG0IIKFI1WpXTJrrQxFCWJML8fJabWaPX1JD3aEApvrpdU4XDQJQV4nPI2E9EsNUcrcqO62zPYRR4aP3n8Gr//mxVKG49Qrg9x4A3vY1EqVWJoEf3w184SZH2dBO+PbRYfzkxDj+7aFuZ784cZy6XIsqzdee17yPbl/6Gq2TLIjH5QSn9YnRAEJRnVjPLuvGfZkND01GkjBbT3ncry/tVmfdGNGhyybWokHeYD2vyi67aH9cP/4oJMQd7Z86akvhRQzVQ0qHy74MCqq776Tbsz9NEEPVIYxJ+xX1PKe/BugFIzeiX+02KtqHVxIKmixaN1Xaf24SOl5m2ZiVeawXa0DpHLOMJu5naQ9T2U6aRzRI7nod+iGMANBSWYRSvxeRmKx2YqvkymkNaG5rI4ezLGvX5SwPYeyd1mJ1JUnSHYe9XOuWAO2Tno5kUbtpvwayx4dWaRZbfbNoTtMtwF0CLZhFQaAfkLxZ00XC0Th+ukbPSfHMCcuuUZV4XB0Uu7qFTDIZFWDYXT/5CsYDQSyHovB5pLRZ34ANp3UsokSq4MITrWVZxvvf/37ce++9ePTRR9HVlVl7qTaMMc2L9eL/0u2V73TdXja2uI5ITEahz2PYdqe6Q7PptAa0F3nsGBAMqHmCVm1T6jE1c6u6ckyLwxQPkiQou8UwUmHrzcDbfwT89neAaq1auHMDhzFOhItwKt4JANgbehkly0Mok4IIygXojmXBzWKGx6Nt5JWLD09D1w9hZHjjmyJa6yvQmbRDsmjtOe9IsGitKsE+aRAFsXVyLriNKElmuyJa9z2K9ZA2zCnZab29oQxej4SlYFTLHJs+R5tpj4/yrO1iI9e6d3oFcRmoKilIGeJRW1aI2lI/ZFkbMGeKLAPjPDDIpiuoqAL4tc/Q/Ze+ipWT5IozzFOd64NnrgdRePFU/ED2XX4WjAeCWFyjXLZD7VUAtKLDhqBGhKTJtR56hlwlle3qBj4j0jiteQjjzsZyFBV4zWOZjFDzrK9yf3yFZar48gHfj9BaHEVdmc7Vo0yefyG+2zgjPsvwe2K7/v1b2Qa84d9VAdXrkVSnS8YRIWoWr3muda9B1jezWxWtTQR9J50TjCSp79f1s7SwrC312xYfVaJhdIV7AAAvx12+l5WsyO3SmG23mBVbQNFO41JD9lr1Wzma4iXE4zJeHl4EAFzVWa2KlK8Y5Vqfupdu978lO8cB2kCw6GtlDKgp9aNGyUnOevFOybU+5OlVXXt24HiQ2ZUQVpXhy6rLeuvNFH9ghc1ca81pbS7qc671o+emU+PPXAxhPKUUTJ0OYWQMResEt/W/uvq7bjKtU5zWnGldlrlovVbegbgsoUpaRVuhzU2s8nqclbfg/EwWnIfKOSc4ToVeO6J1gdejvkYD+s9TJKit3XIxhBHAI2fonPZYt0HGqiQBe98E3P0CcPs/0pDdyZPAV38V+PbvALO9WT2W00rX1qmxgLMupH5uWb/R/Ly843ZycAYXgZPfs/xzQ/NrWA5F4fd5UFPqRzgax6kx3TVSjQbJYp61wrkiuh5cK1nHB/CeaiKwBvmMMqyQc6AzpetGoKAUZeEZHJAGHO2fOmpLcJ3nNIoiCxSBlEmMwI7bqOA436+uQZMH++npNHJa83vDrWCkN//o1sG8N2uyGQ8CQL2eDc8lOa0zhM8ZXQ6d1kNuIxWS8RZoe//5xFxitcCgrDUlScJ2NSJEt4daX6TCE5Ab0ZpzrSdJnP6Ph7rxts8/Rx2zgRE6L3h8mlEnS6TMaOKORTvxIJF1lM3S9emh1W2pznS3+EuxVEPrjzeU99nqQNxSU4Lrvcoxt15BBcIsMBkIYkquxmm5ExJkLd7S8pdeAVanAX8ZSreTazylAOIENhBMvKK6rLfVl6XtkAJszBccPQpEVimFgQsWWSSnovXdd9+Nb3zjG/jWt76F8vJyTE5OYnJyEuvr7txWHLlgaYufPAmMvkAfxst/19XjANrGpKuu1PANzhXPs9kWBqragZpt5N4dfMYwt9SM3cnubx7AuOVaoDQzR0U8LmvD65IunNh2C+Un6thIp/Xx4UU8G6cTY+Hw07pFeQd653Kcqc0Xm8VhyLKsVp+S40EALbM4RQzlhYHbIYxMO+VaX+7pQVuV/Ryh1upiXK0Mb0PHddkbXNl2NWXwrs1ivJsGCFSXFKibf6bQ51Wd6ar4xy7rHbdTHrRdbExNPqeLuTFqPd5uNyJkvp8WHd5CZxf+rhuAV90NALjs5b9BFZYTnaqMkud9vuggVlBiPsQzB5xWXDc7GsvV80p/poP0nGBXtOYL/rZbs9NKxov15QnKoE6C3UgHFDGFC3hji+sIrFnEcchydpzWAHDFO7Fc2ok6aQkfKvmF9h4OjAKLQ5AlL47Fd2JwbhXBSO5amwF70QqAlmGauWitG8ZoksXbYySkK7DwZlpkUEXrQ86OS4kI8Q+TI6/NYZ41AGDyFfjkMOblMry07NLNprx/26RZbKnI/PPQGB0DAPTFGjKLdtGjc1r3z64isB5BUYEHe5orcJlSIDuh5MarLE2oBZls5lkPzmmtrI0V1tfMnA1jbGXRuk91sdihsrgAlcXkMlTFWc6z3mXDpKDPtTZhdiWEudUwpf5YfMav3VYLv8+DscX1xOcnFtEcVjad1pFYXN34729xt1FMybRm2G19yrnbOh6X1cK7o0GMimg9vrhOgn4Wndbjq8CoTOuj8pV+e7+kZJmeineqppiMUM45/gUquO21+ZoZ5lpPnaJBqyW1ORFyppaCqvimdxan4CsErvsT4AMvAYffQ0677p8Bn7kGePAjhmsDN3AMzlo45uy80q/EJFkJkx4vcPV76P7zX7DMrufj2NtcgSs76NpzbEj3/5iLIYwKT8fIKLAl1GP5vFaXFKC8yIfLpV5IKxOU/8zzJDKloEg12dzmPeaok6KztgR3epRs2j1vTD8w3orCcm3te5aE+cklGuznVQb76WExdnh+jWJ2Vuc0B+3WDMTzpI5DWZbVopujeBDd8WXLaR2MxNSZJHad1p12u/SdYDCMUd/Fu1fnit+pvG4Jegi7rEvrM5/FY4S6Fz6FWFzGF58awAsD83jglQntPVK/J+uZw/1qDKiylmlUjEAz3WqMqimjRyHFwpiSqzEgN2bemaljsOwQAOA63zlbP7+lphTXexTROouOYV5DvOw/TF/oeSj9Lykua3TdhI7GGgAO5vsZwQaCmXPoGaNIN6shuHrYPGz6+Gqnx03Z05J05FS0/uxnP4tAIICbb74Zzc3N6n/f/e53Xf29TjturRe/TLe778xo0AkLRGZ2eRZue6eXs1cNYnQRIT02RQH9Manu725qH7fMNrQJxSXE4Pd5DF3EybCY0zu9kr3NrgnHRxbxTFw5QQ88oW7CTsa77A/Uc4sqWo9gejmEtXAMHklz+ujhymOq01o5iWbaNtWwD2soRIW0jq2y/QnorVVJonW28PnJwQAgdI7ERUNHMfQRIcuUIfiK4gzhATl2sWqJUug2GWrCaAWXNO8d3uw37XfeoviavwXqdqEsMoePFnzZ+Hk5T/l64w20+OzbwGGIHA2yr6VCXXxs6DDGrhtoozjXkzDoNAUWrbMRDQKQE75S+UzPpC5u2Gl9oK0KAAlH3IljGcexMEBZyV6/tph0i7cAj7b+IQDgjes/orxJQNtYNl+GwtJKxGUHA0VdkuKqMIEz5DIWrRv2UZEouEhFoyTWwlF1WNoOg0Ivn2f6ZwzyBWVZG6zqNE+160YAEsqXetGIebS7ieYYeQEA8HJ8Bwbctv6V1GJZKodHkrG7YNLd39BRvkbtwYPxxuwNnebndmEAp3oGAQAH26pQ4PXgYBsJXieTBaUz9wGQqZsoWzEl0ATobQ1labOTtzmdd2AXxWm9XxpAV7WzLFS9aIGVaa2bw05nHRcPxo8nZJfqYRdOR00Jiv3mTvsSvw+v2krGiAQH6+x56oTxl9se5Nk7vYJwNI7yIp/tfO9ktqgbq/XE9Se7reW4Y7f19HIIkZgMn0dy5DZsrCRRIBSNY2EtohOtM+8CHF1YR59Mwy2luR57v6SYOk7LnRicXXW/8WWUXOumMOVkp5haTDAsZKpDGK/IXp6pjhMji+r9k2MB46HoekrrgDf8G/BHz5KBIh4FjnwG+PTlwJHPUVHGJYG1SEIngP7YLAmv0vwEIP3Av8vfDhSUANOntZZtA3i49IHWSlyliNYvDirDGIMBzQSSzSGMCi/OF6En3kquQy5MGiBJEjprS/F6L10nseuO7ApuSoTmbZ5jjopSnVUFuIOPKRuzFvYoESHnSLTmAt62+tKU+JTmyiL4PBLC0TgVYwafBCBTx2AmRbEk0XphLaKeJxrSFHf18D54bH4F8qxyfsrQac3Cc0WRD9Ul9vZdHboc3rSfebvw9UwnWp+fWkZcmUFWr+vi5T1lwno8l9EggNZ1PH0WA9MBrCsGll+cnlTd19kewghoewKeW4CqTqCglNYB833Wv6yco04VHAQgJXZKZcjLHtp37Q29YuvnO2qKcb1HKbhnUbRmIb63Sokb6ftlejG/VxGtd9yGhvJCFBfYnO9nRkULFYblGFZGaD2QnJdvRr3u8ceMigo5zLMGNiAexOi/d73rXa7+XlpbemiZpj8DGQ1gBLTq/1aTwPbWqmKUF/oQicnZd0AqL3a87zH1Q+vMab0MeW1By3rdnblofVYRZHY0lNmaqrylpgSFPg+CkXiq2yXLvDyyiBfjOxHzFNBQRCXv7JTcmftIBRa4FofVxXdbdYlhm8U203gQFq0za9NZjsh4ObYdANC8dML277VV+nWidZbyrBnFNVA+Ss4QM3GLYxa6J5fopLc8QVElSQ7+tHBLlMXUZMOYGx0cK9CbTpxwK3ABlGX65s8hBg9+1XsE1649nvj9YEAVIcNbaTjLhjqtE0Rrej42VLQuqgTalEo0T29OZn6AhgNJ3sxcJcmY5FrTEEYS0w7qslb3qLFMFqI1b8abDmRlo3V/5DBejm9HYXwdeOKf6YvKhk/quE51FJ/NYa51MBJTr0/piqrb6kzOfU7x+bWFtkGsAb9Ha3VxDnpaq4pR6vciHIunttctDNDnzlvo/FxcUqPGLdzgPak6PR0xehQA8FJ8B4bn19wVwyUJAxKJulviY85/PwnPIrW+DmXT9VJSo2ZSz/WQQ+2KLSSUXKYUg85OLCVmqp5ShlxlMRoE0EXJ2DAF5GwYY802rEhlKJIi2F9gPRQxmYRM0/O/ACBTxm1FS/pfbtgD+IqoW8igAARo8W521p+37jLItVaHPR207bphB+y+lgpXQxgBamEv8EqIxmUtS5px6bYeUTaIzVVFttbATKHPqw5Rn5hdoIIbkJGphhlfDKqiNWZtiNayrL4mA77tiMblzAuJ9VokUWdtCQ3ktYFhIZOHMOYoz1rfwbG4FlELnGlp2A3c9X2KQqzfQ6/hgx8GvvIG14Ma9YOuk4/NkuHngFiYItHSZfsXVwMHf5Puv/B50x87qXaQVeKqTnLyHRtaIIFv5AUAMol0FdmNW5RlGT1Ty3g2rrgx2UFuwpaaYk20zlY0iEJ8++2Iyh7s9oygw2MQHWPCrtVjqJTWMC1XId6eBVF/5+tpTTt5ElgY1A1hTDXZ+Lweda0xNLeWPcEoKSaP86zryvyWuePJsGO9MjwBKRaitRU7lF3C67au+vSFZv1xeCRgPRLL3mwgA6c1Gwb3NCd28W5vtHBa50q0rukisTi6jsHzWufxM71ziI4dp39kOc9aluVUI4vHo7mt00WEKJrVSAXtqXmGRDZ4KrgNEdmLyvAEsDCU9uf3+MZQLwUQhF/bi2YBXkevN1xG5+dggNIhzFibV/cG2HGbMt/PxjBEKyRJfe190/Sa2Fnj0a9KusGmSWuH4JJmnLgQRetsw5V5U1v6ye/T8IDa7arD0y2cmbTVpP1EkiQ1QzrrudZdNwCQ4Jk7jwZ5HjWlfnXRa8XW+lIUeCWshKKYP/5Tihhp2JuVoUXdaYS+ZPStTLnMtY7G4jg5GkAQhQg1KVmxysXgdLwr96K16rQeUhffZu58PomPLa5jPawsdONxbeOUYbbU2OI6jsk0LbpwwnxQWTJbYoOolNawIhchWLcvo2NIQWm5a1k6gVKsG8dgIKlt/8S36YsHfsO5wFdSQ1OzAdNhevp4ECN4IFdPuvfO+HG6dTnlPtJ0CP8do1b3nS/+PbXAM32Pkqundgcau+iCv5GZ1mfGWTioVAt3A0or/YaRLiKkjwZ8ov2arOWNAdCJ1om51uOBIOZXw/B5pITzoK1c62zkWevonVnFPRGlC+HYVyhrk4uUna9Wjy+XudaDc6uIy0B5kS/BVWJE1pzWQGJESBLsgk0e9sp4PJLqwE65Luk7J3zOHK8AVNfbqz0nDTtt0qIsTE97diEal+0LKjricRlno+SwagynX5inRclrHJQbMb6YxagtxeXrmaDnnFvS26qLUV1SgEhM1tZVi8PKc6PkzWYRJ51sOROtPR6cwjYAwLaQvdZVRt28zK8C3dSZY7uzzlugFXlNIkLUPGsb676blVzro4PzWA4qDtRMhjC6jAYBaP3JgkmKW6v5MnqOHLqt1TxrF1nxPIxxYUoRKnxFVJTPkLHFNZ1obUOAXxymTbKnQI2jyzjCTxGtO6QpHGyy70BX40H0hUy90zoHcKcUYxkRYsT21wDvexq485MkCI08b+kOtoKF4lKlg8G2aK1vv7Yj2l1DXVk49zNgMbUoFo/Larb2gbZK7G+tgN/nwdxqmK7XajRI9l3WY4vrWA3H8DyU89CAtWh9TdEw2qRZhD1F2euuU5iJleBonD4TDWO/tP17dUP3AwB+FrsGU6vunfcqpbXac332fl2etXFnqJbfv5p90Xr2PBCLYHKJ1iKNDjpMAKCowIvGikJsl5QCeu32jGdjsKGwy0EXjt/nUXPKMxpgp0cVrbVM6zNqnnXia8WC4MCsrsMv16K1x6uKxUuDmsEjHIsjMnqc/uHgumyH2ZUwloNReCQkdkmpudYWwxgjQXUNHGik+Vwjbp3EBpybj+MVWdHCLLpOmK3LdCxH5T2QvS72AyaMK0X05uoy7RxmFRHS9yitVer3qF2GWYm7UV77umVac9p1WgP6jPikxx96hnRHHk6fAy4o0bq+vBAlfhNbvCwDR5UBjFf9fsatZZzfaua0BrQTU9bdbMXVqhB2veeUrQ0VQMNNtiuCW/RM9qJBAK31fY9JpIIRu9SWmNwJJz3TK1iPxFBW6EPRzlvVr8ueApyX2zC5FKTBA7lCl2k9mEa0rin1o7qkALKsE28WB4HoelYq0KPz63iJB3hx+6ANyiepyncsvhNjS1l+rmq2AtVd8CKGaz1nTJ3WXACampmGfJYWgbjsd9w9pkWu9dxKSK20m1UW2Wk9PL+mFReSicc10dpsensahufX8OnIm3BK3gpPaBH4yfu13EElzxo7X6fGc0wEgljhgVs5ZH41rGbG7Wkup84BrwfhaDyrGWNpYdG6/3FjN1OvsrHggZ/ZwmQYI7fQ7mqiIYyMeh2wdFpnKc8a5HAemlvFC/IehLbeRouE+z9EUSqQgC2vUt3fuRStOZt/mw3HC58TxxbXM8/ZthKtp9I7Z3ebCfpuhjDqUfI1r/ecQnuVs80dliZoOI7kwVItbeAHXGTITy+HcD5GIlbZks2MWzNkWRWth7OcL8hOyrY1+oxdvqUKAJkBOHrnFRZxTisDGDtfnZUcYD29FvnnyfA6bGjOpQvehPVwDM9HaDPVuGQ+i8GIjhr6XI3PLGgDuOzkWTNqrrXxMEb+jNhx4XTWlWJrXSmicRlP91BOYiZDGA+0ZVaINM21BoCbnLutRxXXV3uN8+gfzoFdnlVi28qbshJ/Mbawjr64A9GaX4+GPdjWRI7ajNfnZY1Y85TBK8m4rmrR9q+xIWhkYZ1MSKFl7f8hB05rWZbVCA4e8JkSQ2QHrw+46vfIVAEAJ9zFXfJjv/mKVgBkfrJ1bVSFSZt5zg17yMQlx4GjX0r59uDcKpZDURT6PNjRUIZCnxeXKZ+9F4cWcjqEka/XkzVXAZKHXn+9eSOJw2tPAQCOFx7Oeg7w6MIaHo7T2sKrRPOlJRKERxl+e3/sVRjM1qA/dpGf/SnOsRBqErvDAtLCWA+5fj2+zAsMle00jygWBub6XOVZM1tqSjTRuj7zIYzaPtueLsJonfpZ6hg1clqzTpJUYGipLEJZoQ/RuKx1+OVatAbUuEyP4qZtrixCDZZQHJwCIGUeVZgEu6zbqksS9khoYNHa2EgGABh7kSJEyhpR0kSF0Gx16QcjMYwtruNIXDEk2Sg01kxSse7J6L7sufMBjCnmj9aqYoqcArTMaiM4AnPHbeqXeGZaZsMYaU22RxpEqd+rxlzaQRPNk14f7oq2e21ywQUlWpMt3aTCMHYMmDpJ4p/THNwkVkJRTC3Rm9TMaQ0gd05rQK2UXu89hZ2N9k/Oe5rKUYgwqseVirWS05Up6SIVjNAcbblziB5XFqEH2yrh2Xaz+nWpcS9qKui9klOHalU73S6NYWCGLlhmojWgua172V2in6icYQV6dGENL8XJaY35fmBlxvoXFKRhOjk/H9+NMRfuvnTI20hUvNHziqlozXE7t+EIpOg6PR9uNy4Wuda8Cd9SU4LSQuOBKbW64oJpnMF8PxBeJsdUvbsBmn3TK4jCh/+u+nM6b/U+Ahz7Mgm0XHndeQeqSvyoK6NK78AGRHRw2yq3+no9WjvQRkaUoOVyclAHF7UCARMNa62kWXbcJMSD6PLv9C20elSBeGrZ2IkeDWtiQVvmTuuBWXI4VxT54H/d/0ebvUHazKFxP1BcjV1NNtzfGWJ3CCNAn6nyIh9k2cD56BQWrSdOpGSK2ol72Gk2jDHDzgm57TBW5ULUS0vYGh9I/wt6uP2vYS+a6ylmwU0cz+iC5rz02M24NWNlGoisIg4PRuX67IrWipPyoKcPHbUlCZ1kLJiorkg1GiQLeaE6YnFZdXnaeQ+3VBahxO9FNC5nb+ML+jwcj1Osl3/SfCiiESzM1s8+D0TWgIpWZy2/aq516uPKsqzOdbC77mO39WPd01TY5cKxTUdXLC7jjBpNlZlovUURlw3PNy2HHLutufPByZA2plkZxhicH6cvZCHPGqAioOq0Xhgit5oValzLZer6PONhjJKEfiWS6ECh/Rz9xgrKxozFZXLUjR8HIJNgVtaQ2TEZMDi3hqVgFH6fB79xBR2vK9Ga4diNMz8GIs7PjRzBdvveJtSVFSIal9WvmbI6p32mnHQTX624rV/6asqx8nOwp7lCjb25soMKGif6J7XicA6c1uzyb25q1s4RA08a/7Aso2uGjAoPxK7O+rGMLqzjIUW0xvCz9oZt9j4ChP9/9t4zSpLsvA68EekzK8t739Xed8/0eACDgZshMCQIgiDoRCsupSVXq4V09pCSlqQsj3QoamUXJCVSBC1IgCSIAYGGHYCDse1dtauuqi7vbfrMiP3x4nsRmRneZGOgvufMqZ6qzMqozBfP3O9+9+5gLdSFC/J+pnb2A8q5XZ55A9urjPA1EoyR0rp5XlGPDj7GAh29QBTVc83yDW4P0uuCtB5qT2KfoMx7nR5zm6AKvkY7nc3Dw1obFT/Qrnha7y4BReaVrVq5VL//gqB2nvP5dlPphGsd8ed69KCQ0h27bC/4i+/Zh6PiFABAah/zPk5qoFqD1HAgdpTWmk7RIYXn84u0Jr7waljZG9F5yQiVEkSFF3lVOoZpHy1u5xTB7UBrQjm7Coyr2NLJIZMkldDWktbcnsPDdfWy+fawMI2D3UmIov0i+ohRAShgP2vgbUZaA9owxpoP65yisj72A8wiwAMmNb6YLSZG/6qHdAC+ocqH/g7xGvbbUAHxa+pL4ynxOqJSjtkkuFWNaZArVnhFx6jaq4eDvcokHSBxcun+JgDg1FArO4BFlevrO8ltHu4GGUaW7mOVbamM3RXWejdqg7SeICKd1Jzd7ohPLeY2c9hGCstxpQXGzCeJIMu8/e8N6bC+sb5HrPW9AwDwbOiKYcCJIDDLhY+GlMXk5I+4VyJpUpNrYWUNQtdCBzrD0C1uJXDCdVo4BStG+44A7/tV9s2z/wy4+hkguwbEWoDhJwGooRaNII2v65AGYw8ijDEUBvYoXtUTX4csyzg3tc68bmdeB0oZlrztsy8bOg8wIji3wTakCtQQxmoyZaQjhXiE+ffrtmstX2cKgnirL1ZNtzU+s0LP0eqOBOVgeaCnCYIArO4WsLrrn0pAi7sOCD9BEDDW6dMYah9jxYxKoW4TrBLpxvc33ftVbfE+dE6sZGW8rnhz9qy85uzJNFcPPqYfUGYTc5s53JWZcg9rd137rQLgPsfZeC+KiHCFiC/oOwEJInqETbynr7p75ARXWm8BaxOMaBNCwGF/rUHmNnIolCVEw6ItD3JBENSis4+F8Km1DC5LzB4Ea3fYvGMTVEw8mVXG24EXnK2bVBheuFwXBjS3mcNuoYxISDAtxGvxnkNEWq9A1hZ2bYZv3VthnXPJaMj2axpBbZs32NNo1dY2/KCpVdlJSBuB7EHkbYW8afLuZw2wMbyGZpSjLQBk66ArHsB1kothbnsMFs0VK7hWZCT8qGTfk10Q1HE1uZJR1f4+nFn0QCrro/3NOK146F+zE8ZohOGnGMFe3FGteWxiO1/i8/vxgRZNoW7T/Ilkn9FzzBmxf/B7WP5OboPZaGpAFiknNPsaCmPMTL7J1LZNPb7sXWpBBN6B7rRKwhtZhCzfQHx7CgU5gs/uHPEeIFqD2Y0cZuVuzMfGWDGLuh3NcP0vAAC32t8LGaJ/1hMtA8DAoxAg473CObQmI+gxCEAk1ePQprKH8EvlqLHJW+RKa+dz33B7EvtE/5TWxP2YCQr14IulghaJNtWWcHMai9t5bOVKCIkC79bVYr82yFmWG6O0Vs5G++UpREMifvDRQTwRZ+ToSsp7AaEWtLevE6eRp/WWYk+lByKSR57h3UwzPonoiNPb7DzN+JrN++a+1nPngeIudsRm3JCHfSt0yLLMbfb6WxOMqyS/bD219cIlILvKuh6GnuTfNhTvOkH7GIpiEnGhhHe02d9zAioPW/W+bM8Dq7cACIrFcTB4+5HWnToMf3ZdVeN4DGAE7FmDAOrhd2m7gPVM0fPrVmHoCRQQRY+wiRNx++qFQ73N+ICo+Kce+qAvLYh3ljWJuDa8tQmkaLu3uutrO60WpLQ+OdTKSK59iqXA8FOqB2WQRJ8Y4j5DtAiNmZHWiqczJx95CKP3BYSUQBsdp9g37FiErN4BMisoCVFclccCUVrfjJ9CUQ5hRFhCeNNYgfhk2zaeEG9ChqCqWNyApybfqCNtqMBk5d/ENxhGBQ9uJXDK9WXSGNjX1QQ88feBkXcwIvZz/7tyEe9jvqMI0E9VB0RaH+lXlR2NJM2roPG1/sz5WfzgJ1/Dv//ybbVlau97bQd82UYkDrQrJJLii14dwtha9fCQKKjKZr2uG+5n/agv8zFXE9PG+Ll/woghgJPWyWgYIwppE5RFCBXejLonauGFjK2CIOhahBTKFb6J0zs0EEg1en89i2xRIep86JyY2cjiFYkVzEKTLzt8sqK0Hnrc0/s0u5HDnNyJkhBlpL6mddUxFK/GYgtTAtUF2nlBNIXZyCgA4Nl0tcKEyJM7yzsoXlYIlrF3M69PH0EFybHOFEI2VSZBzMP317JYRzNWIopi1sBfWg+9zXFEwwLeIyqEn1M7uI597EBUytbZS1BRZ29XEyI2gwcf29OGZDSElZ0CZm4oRHrPUduF3Ws8S6HZ9mdiBJW0NjhwOlRbe1Fa9ypkTyijFEF9UFqXKhIWt/MABEgdSoedlUWIRmlN+/PptWx16KlDjC9u447ECmWp7buOnjumzTogP+uAQxhPDrbiYG8aYVHARrbkXqghisDxj7F/X/kzR08lD+mB1gTaUlF2foFKrBvCrZJNDAGP/1327zd+W7eD7Jimg4wyBga2L7FvDD/ly96lFjQHH+hpUgUK975ZdX0cN/4aAPBtnMCOnPBdYEOWo/e73s2+cesL5k8oZoFbXwIArI4yZbSfHTg4/L0AgBfEN3G41ziUdqQjCQESjhcvsW/4pXIkdezyDWWece5pDQDDbQlVae1yb0XYyZe4CMOp0tpzeJ0eyCJkfZLbA+7tSumGVR7gdqm7rHu0oIgdW4b8u55adB+BDAG9wgYe75EQC4fwrmZmv3Oh6P/r0vlwrPZMkGgDmhWeRM8ipFxQuw1H38FFBOuZIjI+WGKSD3p/V6daFDWzCFHm2cn0GcgQcd+n+3ozW0JOsYDiXQtmFiF0zh17d1XOjprvl3PPrYkipqOsEPlo1FkA+Ijy+jMbWbW7mDqf+0+zzzsgvP1Ia6601gyiy38KlPOMrPIh5fOeRQgjIRUL84nwppmfqQvk5AjelBiRuT9T791phEO9Kbw/xDZ/hb0OvA1NoFWnOklzH2hNIBUNoVSRvXnvGGC3UOYqkdPKpg8f+k3go/8DOPHD/IAZpKc2AL7odEsriIZEVkEzAFdak9qQk9beQhgB9VBV6lda52ZsKK2ViXu55YSipvOftL69AZxXxrJhqB6A95eYH9L1+GmmNHCLjr1AOMEO4uvVJLkaKGruzX7AqnV24RL76kEVxFupupvYAej7/xsjECRlkT6g3r97G6q0VomD2tdvqNIa4D7BmH0TF++whfXL1xc1ftY+W4MQasIYZzdy2MyWEAkJONBbvy4cVohQXV9rfhj37mcNqIUUriZuGQA+8knWAqyxgyJyNgiLEEmSeXHXbuYCeRC68WquAyetVS/eqdUsD4bsNgmG7GiKobMpClnWFKV86JyYWc/hW5Ki+p9+1X7LeLmovv7g455CK2c3cpAhYiOptJza8bk1gqK0FjpYAcdPexBJkvFWcRQAcFSuJrp6muPoaY5BkoHyFaZk89saBHBmb0MIgrSmQstqs9IhNGt/vyeKAt7TPI9eYQOVcJL5fjuBGFI7C2p8rW8tKipIm6nyABALh/DMvk4AwMod5RDqKITRH2sQwMLTmkBq66t/bqq2rkgyH/9uPK37lQNqLK9Ytvngzb60nYckA9GQiEiPsr8yU4zvLLLOIUEEeo6iOx1Dczys2OS4X9dvzG+r3R1kd2cTvPtmVau0Doi05gKXFsQjIT6uHYcxakHiirtfYdYdNkF7LPLW5qT1rMW1EGlNBK8TnP47bF+8dJX7VEuSzO85re1ZWyqKfd1NeFxUzicBWINIkqxmUPSkGTEeigLbs3ztqcKNzwEALqSYis83xawCOj/tjiok0t2vm9vt3DnLRCatI2gaY2cu3zytAeAQI62fEm/gZJfxw4bakzgiTKMVu5CjTf4VfTQ2eV48rfcmMmgWsqhAZEVSD6D3t7MphnTcuAteD9wHeC3jvruiFhpfa7IGMQrMJCHF7aUdVWWd6vLdm70KsSZsxtnc/O4WVjDdV2H31hdWuvTtDD3A0B4EUNXWyzoWIXPnGYeX6gI6D6A5HkGr4nLgRxhjVd4Y7ZHMwhgVAna9h817ftmDEL/S2RRTPb8PKPPNvZcZea8F2YRqrEEAoDsdQzwioiLJnoSGV8rsnLBfcpZ/09ccRzQsolRR90U8UyVAaxDgbUlak9JaGUSyrFqDnPlpX6rBPJ3WQmkNqIrNcZ+JgYmVXbxSYYeY1KyF/44GXZtX0SVsYVtO4HbCn2RYUg868bMGqm0WPPvm6eDK7CZkmR0IuqkCnOpkASmiqGnHCZjoUzypBoUVDHckTRVCKvm3C6lSUUOAur2T1jQhxvcogSlzFxghYgbFGmS3l226glBaT6zsqmQOkY21kGUcXGLKhs+UPbaWiCH1/VxSg60qkurRaWVzs5+TEzr3tVRRFUtu/W9lmRMfXKnaNgK88Ovs30KoipClTUDQpHWmUOZkma49iB+EoxO0jTLVs1RGfJ6N1ezanOJXLqiktt/gYYxMFUBqpEO9zboqCtqo6lpFzSlKax/8rAG1nbsq6+DoR4AP/juuzAegUX/7b1/FAhUlREMihmy2y/tqMaOjtCbV1r5u62DIg7VhjF5DGMHIsQm5H5vhLqZypgArKyxdZY9PtAEdezmRs7CVV5XgNkFqsXyLcjj0RFqzgl+ih5HWG9mS4+sxwp3lXZwvM0/Iju36Q8yJwVbsF2aR3LwFiBHg0Iu+vK4WbkjrukwKH0B72XyPMvZovrCJ74lcYk/reIp1iTgFdQvVKLxJae103/ec4msdIisKB/ZNeqpPt9CqtXaMwri1autX/l/D37W4nUdZkhEJCehOO3+P+xQhQ7qkBFT6QFrTXq2vNQ6B7FfM7vcF5fPo2A9EUxAEQVOcd39+uT6vKq2xdrcuZ8AMdMZaXZpViRwP3WtGKFUk3kF2UrEfIpLWk6919yE2vqUyt4qwg9p8DLIHmVzNYCtr8P6tTzIfXLdBe8l24MQPsX+/8UkAjMDb1YQwavH4cBqPisp4CiCEcW4zh1ypgmhIZGK0aBIYVAQ3tRYhq3eAlXFADGOu+90AgGmfhVB0P6VGH2PWmqWMsb82AFxTPu+jH8FwB4X0+kiIdu7DTHgEEaGCd0M/KBcA4pEQPphk4ortnier9oCeQHvg9UlsbrHx6sbTelixDJqRulCEt2uj88cehyprQO282cmXsWF0jzlFm+JrvTHFBSvGpDWbaydXMyitkZ91gNYgCu4I7BpPx2aBwg6SO2xf91p2ABfuO7OFMEO+VOGFn716eyozX2vysx55hnN4Q0pH030flPGqD7qWtDbg1Qq73K5PVoqDfqnzieAd0Aa1954AmnrZfKNVf2fX1S7dfdWktSgKnAuddFm8yxbLeD3H1u3O3ZuOniuKQrVHvCyrBdWgzuT02oH+9gCgyuKzzNNq6hXmAxhtUhdkj6BwHjObB8KhgIiBO8s7vNVYmPq27Y2goCQZvyydwviyP16mt5aUydhCnaqHgzyM0X+1H1mDnBpu1f05HUbnNnO+HbZ1oSw8g8IKn0iMMNSeRDQkolCWsDh9CyjnWAgfVWxdIlssc4uartEjQKKdESF0cNSDLPNJUhh5BgACUVpXkdZTf6tPpN9/DfHdGezKcXx696Txxt0udHyt769nkStVEAuLlp/TPoUQvL+erU90X7sLFHeBSNK2V2ctVnYL2MmXIQpq2xoApoZ54d8CP/DbVd78RJZMrWZRDshqB2CkqyyzSm6XRq26V1HJLm0XsOtDu5YjKBYhY1vM7uZdIWVM959iRaogUKO0NvKzJvDiZa09SH5LJRJ8UFoXyhW+gdpv4tusvaYg5l4i/PZ0pnh4kxV8swcBVDXeyk2gwP4+rtqyQUIeqF2XfCCtmQ2BgPkOxXuOkrStQNYgg48BgoDWJAuCBZwrt/j8TXZTK15Ia6a+iHfvR1oJrZ33ydf6/PQGLkusNVGcv1DXDn5ysAUvhhTSf9/7gESrL6+rhRNPdgI9dmI5A8knldK0Et4VHX2CfWP2nH57vAEeL7J58UrSJbFEyry5WqW16p3vBO8+2AVAxlBRUdDbVFpLmhDG2rBbN2iOR/h9NGPkaw0ATyghdXTo0sGsorTqb024si3pSccgCkA3FJLAD9KaH4AT6j7ElLRWrUEI+7Ut6y5xY34L8+hAOZxk5G1Nd5sZqPumaU1DqMe9f/a1uLW4g0JZQjoe5nu/Y4NEWns8u538YfbVgUUIkdZHlXHemozyfeCVuU39J9H4HHwciDnz8+WgsT7+ErA1y6/jSH9z3Tr+XOsymoQ8MkJKJZx8BM0vY12aPQT5Wt+rIa0VlTX2PIvubnbv+BmOJkkyZpX7abA9yTzAAWOLkMKOqoI89gMYak9AEIBMsYI1n6xCZVnGFyusc/zw5sumj31niBGB91u9d5pzNHUDyU4AMnqLjGTtdWEP0pph88EdecBztxbth9zkHcQjIa4U983GpUppbW492d8SR1MsjLIkY3NeWRuDDGEEG0Pn8oyY3FO6x8/Dm+EurKEFZ6/Zt561AlOws4D4jlS0/gE9yplcj7SeVlTPmk4xP32tJ7VK66EnmShs875aKK26llfZOtY6gq5hto/2HB6vgJPWWqGPIDArUKDaImTi6wBkoPuobuc5t7txeZ66s7SL69IoACC6cs3RnhPQhkFm2Dlsd4l18lDhMSC87UjrrnQMyWgIkqyoikhlffxjviShyrLMB3idL48ODvcF04J9e2kXN+QRZEPNzGtzzrjSWoWbbJH9cuUMbvhEpLtVWgOalpgAWtSp3e8UWYPUoKMphvYUawMP1NagldmDDAirlhXgkKiGz6xNKYeIzgNMHewBpBJojofRkowCQ8oB2MzXenMa2J4DxDBaDjDlxuJ23ndSdGIlgxvyMErxTkb26l3TpT8GALwcfho5xL0TbeRrvaSS1rcUFeyBnrTlwbOrKYbWZASSrKNupsC23hOuP7eJZTYeh9qTapsQwBawJ/8e6xbQYKA1gXhERLEi+RZOoQc1hLG6QNWSjPCNyGTDLUIYaf002MH2WVG5b4KyBgE0pPVNQJJ4C7ERmXJIUVfMbeawldMUXGjebh3xhWCfWmUeYulY2DCYh1+TJnAwsDbAbvuHBz7vZYrei1LpHsWWSeb3I5GQVmQ+oCH0F3d86ZwA1DbGzKDSKXLPJmnNQxjVzZ4bgl+W1VbBRJ+iklp11q5fBWrRbtuDPkUZ4pdFyIX7G7glD6EsRFlhp6Yd/MRAC14UX2f/E4A1iLbTxc54IYx0JBEWBeRKFcz74PFdLEv8M+va9yhTlWdX2dpsB1uz6MvdhiQL+CZctoXTmF+6xgvK5YrE76eDDknr/tYE3tFdQIewA0kIqYo9C0yvZ7nqU7e92AUsfa0BtQC2PQtkVnUfovpZO7cGAYBwSER3Oo5ugUhr757WNG6qSes7LFRWD4tEWqvK9wPalnUXKFck5ewjoNKuXMOKfdXWHoVAHs4p81RAftZUdD452ApR2fvRWu4pjBEAjn2UWa7Mvqlva1GDXU03m3Y/QQpwQ19rt37WWvQcBUbfCcgV4K3/gauzxvuaR8A+x7cqB1AIQCehdoxp5pcxxfZk8lvV43ic+VnjyPfxEDI/vYlXdwsoliWIgqImPqRkA9z6ov79dOtLzM6gfS/QewKxcAj9im+9X4Toyk4Bn8uz+6F57lvMQ1sPpRwOlxgReCnic4ipsg8+KMwiHQ8jFXNunSYohbQJecAz+UeWMKMuQ3p997VWSGtpY5Lf00cMlNaCIPCi9+6SMk8ErLRe3M7jfIERnq07t7iIrdjFilBnbyz61hlAZ9q9Rp2OtA9YulFNkJaLwH2FE9CS1m027L1sYCtb4oWkPZ0pVvCjdYYU3lpQl8fYu/l4Me3WcgAqNPfXBpruf5591Ya/cmsQ/XOuGizq7v25tbSDO/IgSoiwPbjdPacCdR7OqGvTiMtuPwd425HWgiDwN2tubhoY/zz7gQ8BjABTEmaLFYQ08nczkNL69tKOr2TfnaVdyBCx3KmotkxUIByrd4C1O5CEMF6WTuq3qjvEyk4Ba5kiBMG54gZQiW6vCeV64ErrIWPT933axN6goFFa77GRaExET35eCSTo9hZOAaiHqgEKCRoiX2sT0lqxBkH/I+hqa0M0xDySKHTDD2zlSljZKUCGqLaNTNRYhBSzwPW/AgBc62KevJ7Hro7S+uai/eKLIAgai5Ba0tq7KlP1/rKnmBFFgXvsTwRod3PdxFP0gVmEjL4DkhDGmLiIfaFlvFNkli/y3vcG95rtY8xfsZSBvHW/rp23Fi2JCCMPUNN1Q/YVPvlZE7mwv8faAmOkI4V4RES+JPmmFCBwawWb4xdgGRBEtLttaasCV4iy9/jukn3lLLdOWdxh62YpA0RSQOd+15dDas7IPmWeW7wK7C5bP5ECaDT2MW78v1d2CygoB+/WYeWAsHLbsYICAGtNzG+yf7fv4TkNfoUxXri/gTLCyLQrxaGaovypyAz2igvIyxFsDflfnFreUTtdnAQ6RUIiPzD74Ws9t5mDJAOJSAhdbS1Ar1JspbZQK9xmQWDn5f24vqmjbrKDtj1AvBWoFLnf5PQ662RMREKuiNqP9DLydyE6avsQQ3Ps4b561adb2PK1jjerXqtUkK4B7a+GXIQwEkZagBZBuQ4fldb9rQlmLSZGWI7Hzrz+E3SU1jwczOVYvreaQaEsIRUNIdqn3MsOfK2pGH5CVEicgP2sT2g6pQ4pYYzrmSLmtzzsedO9KpFsQ219Y551s/W1xNGpCbYnX+tLMzp2JZJURaZ4wuP/G/t64fdxc5Z5rOvZ8bSvsTnojcpBb77fBiB1f5XN2cCjbB3Oravet+uTbOwKInDoxWqFn08gIUhfS4KFzo6+E4immXpwXkcwRlYwx36A2xnQOuKXr/X44g6uyyNYELohlHP15yZ+8W8gIhexKLfhYq7bl9fmUIjGg+KMKz9rALxwflfu97wP5datFp2yRhhpV32tfQFXWk9DliW0p6JVHaq1oLEubTTGHuT63DbGJabmFldv831F29gZxMIiZtZz9d2hLnHP6kzbuZ+tUcWdaoXz/EXWcZ7sqArqtLV22wCdNXqaY2rRxczXmhcHn0U6HkG7Itby4wxFnYp1uWdj72a2T+sTwNoEm+8phJGCGmug9Wh3g1uLOyghjJUk63jk9mE2oc7DWX8KqjbxtiOtAfXNil/9E0AqsbZajXrAC+jGG2pLIBq2fnuG25NIRkMolCXXFQ89EMlaGaV2qZetn6SorDP9T2MXSdxc3PFcRSPycE9HComoc1UpqXSmVjP1NgsesLCVw9J2ASFRMG0lNSQe/YSy8PQLaxjtsF7YaVIPrymbe2rl9gDyMuUHTK60ftOYtCD/pJGnIYoCV9P56WtN91NvcxyRA4ovU62v9c0vsIWsdRii4tXnuXOB2hm3ZxkBA7VjwKh9qxb7jfweOWl9yvXlTbhoTSefsCB9ra8vVAcEacFJ80YrrePNmE8zMudfdpxFq5DBtpzEdMKegs8VQhGuXluZuIytXAnRkGhauNPtuiHS2ic/6zsO1KEhUeCP89u+ivuxOxi/gFZB7GcY43mUK5KjYEhaF1Z3C9iZpMA4950TpYrECd3+gWGVfKxtc67FzpKygReqChtqgcj+vUbzdk9zHJHuA+yQX9iyR5zXYkNp8W/qBaIpvsme88EeZCNT5J1PsWHlvqghBponmLLu69JpXFn1b99AoPE70pHS9ag3AxVq/NhT0IFjpCPJilA0T2i82k1x64sAgK9VHsH99ay7/Z4gqAVYZW27za1Bmrgy1QmeTMwBAM4XBm13eVy3KAy6gS2lNaCGUS5c1P3xTO3+ygUOJdnvKItxIObcaq8Wc9pW41CEFVoBfYuQ7LpKFGg8xqkTcnrN3f6cAgUP9zVD4JZEzvwx93QkcUKcYP8TkNL68uwmAJUYBphdwH4/whgBNZDxyqcti4T0WrXCgFNDLfxa6+7jxStAboMRqV7fo4MfZF1K2TWMLLCi14la2zNZhjDN7JnelA7irSn/vG8JagFes5cJRVS/blo7SZg28gyQ6sSIsoeYWc/61kFG5yfeth+OqQrHmzUWIblNlVA6qnYBVSkPfQDbswm40aqoz+l9qIXCDXxbOob7ZjZIbsCV1jPorVWH2oViUXZXGvBEQMqyjEnl7GMnb0wPI50+K61bBgEhBLFSQDc2cbgvbSomof14bHeWfSNge5BrinVTVkwzywtlLEcGT+FdB1i659nr/liE0Ll0zOizCUVUUlprEULe0ho/a0BDWnsMYpzkPuia6zLytd5dVruzFT9rvofwYcxUFZq1iDeruQF3vsL2Ydk1tk8gLqcGXrsGaP7NdyhcCRW1bYLmu9nVLZX8f0ha62O0MwUREvbNfIZ9wyeVNaAeEu1YgwBMAXmQ+5n6QwzkSxW+yW49plRZZt9kBvFmUCak+PHvQ0gUsJktYWnbm6/1LQfqVD10pWNoSRjYLHjApfub7Lp60qZkOldaBxAESSilelGWRcSEMvYlrCcQIq1bdpSNepf3EEbux0abrv7TrHK3s6Dv2wRUhx8AXCk66yNpTQTn3u4Ut3nA4pVqIuUyswbByR/BwT62eb7llbSOt6hVbGWBJMuRQza92ffrjR2povqEe1BaqyGM9jdfQYcxlioSbi+y322qtA44DFIP58OnAABPbrND1t9Kx/DqpP/qnyooG/a1yUsAGCltVsikccXXAVlWFZM+Ka0pGHR/j731iQo0fttXOe0UIHAFsa9hjBdwfz2LUkVGIhLi85gZUrGwGsxDpLWH+3lhMw9JBmJhkSltaK6zsggha5DuI2zjqsCNPUiVhUFYk5PgxiKEfGnbWYgPvad+2INcnGEEyFhXCvFRpSNIq7SWZa5k+3zlKd7a7yfqQnAdYJ+PxUPyI+S5BgMKaW1HaV3Y5UFhX5EexW5BzbVwjBpf65su/awJfVk25i6URnhHnBWuzRsXTN3CNmnNwygv6f5YFQW4V1qPxdl7uh3p8CUwng7AgzTfUZfI6p36By8qgdStI1X+8KY2aDZwQ2snRoSEw/nmdEsGXcI2KkJILfb5iGyxzA/oZMFBOK6MNc+k9aEXWcbJ+j3LgpOR1diRvhaERAErO4X6bkcSLY2+w3vQXigMPPazAIAfkf8G8YhQ3zG1egfIrqIsxnBVHsM5n0nriqTaM9XNMdwihEhrsgb5MAAmgImGRJQqsm92VXO15ycAOMi6PqHkRHHc+hvWldJ1COhRhRMj7RrloQ+gfeTmMFkHfEk/D0gZG69Ujvnq8w2Ai38OiLPoc+FnjfwWsMtI0QmPSuuNbAnbeZanQ4ppp1AtFXwS3oQi3B50WFi2PFuyfbuMtqJCFAestL42xwofWy1KQbGk/N29J/D8Udbt4x9prZz1zfZUemGMJJ7TWIMA4AHvM+s5T+JLOmtUdcEPPaH4Wk9X8yMUvNp7nNs58sBBX5TWGkuvWhxQ7vM7Z4G7irf12LOG8/0eTfHOjcsD7fEiA6fYN8zyz3RA91Lb+hVm+5rsUG1ZA8Tbk7TuSOJd4hW0FRcYOXX0I779blIA2QlhJPAwRh/sOAB2oJJloC0ZQfvgAbbRlMqqnYMedpZ4m3HkyIf49Y97vKbxBWdEXy0EQeBqaz+JYzoInTTwsyZQZTNIpfXMZhGLYKF5XRXrBWBvVxMESOgvK5Nltw+k9UbNZBhNqm2gM2/WP2F7nqnpBBEYfqLquX6GMVaRW01dqsqHQsq259UN+ckf5uPslg9dAlpf61yxwjcqdgswumNn9TZrwY02qS3FLnDPzgJfg70+Kvz0cGdpF8WKhOZ4WFdRRoW8hiutAXwxy+4RAWxMfFM6idfurQX7osp9WVlkGyyjEEYCpYaPE0G8NQtkltnmyGYYmRVuO7DAANSx7rkIpMHabgEb2RIEwTnpR+uSEwWxIfpOsflrexbT06y9fG93yrYylA7LoYVL7BueQxjZoVcQBGCMrJC+bq68o7l5qDpAiTakU06U1rUb4k5SPnohrZl6k1qD/SAJLkxvAgAeGW5T7QAWLgMVJeB17jyweR+lUALfkE7hiqKS9BPcz9pm8UcLeo4f8zAdhEi1wpXWC5f1CQotJr7OyJO2Pcg0jVX9PsfgSutLAFQVjluxgrjESNJr0ihevmWt9JdlWTlg6xdM3WK4w2aLMVda66uNuD1Iu3ul9VCU/X3rYofr30GQZbk+1MksjFHHGgRg+/MD3e7355SBcaS/We0YXL3Divs28WiUzTXz0TEg4v79NcK1uW1IMmsP762xOCDi+KpX0jrWBBxSSM4rnzZ9KLcaG6w+UyWiIX5WqvO19rv9+pGfREWM4Zg4hY90zNbb8dxnZ81c9ykUEcGF+xu+ed8C7H4slCXEwmK9DaeicMT0q8DGtGqfdehFAKyDjO5DvxSzasFXcy3738f2bis3Wcs+4ZpiDaJRWQMapbVPxDGRSi37nwFS3YwArlWGZtf5nP1t6RhWdgrI+BmUrhSi+oR1DKdcFEQVlXU+0Y0dJD2R1lTA72+Ju+r6BgLwtAa4OGBYWOZnACMc6EmjGRmkoLy+QngHBeqEEbTFwHgr0DqM9x7qRkgUcHNxx7OKWJZla3sQQC3ykPVPpaTrZw2wdU0QgFzJW7gpF6JqOb1YWt3zaH2taZ6lOQj+jZlCuYLlHSYi7W/VKQCRDcjUK8CNv67+ng560nHEwiLKkuyYs1nPFLGiXEvnfkU44tAepL81jrAo4HElawp7ngXE4CnltylpncKPhZT2nFM/5utGh1qM7SqtAU1buE/eQHe4mk5pNaGNiplFyO0vApDZIbC5nweDeb2mW0tsU+r28AKohzzP4XoaXFQ2daetSGtqf1zPolD2v80YYIvprMxabYTNGcvHj3WlMCSsII4i5JBGEecBupsuszBGKoD0Hudp7XT48dMeZKJW0bZP8SEmf7YrnwZkibXGtI9hrCuFSEjAbqHsXfGt8bW+vbQDWQY6LDzHtCD/sSlt6yxZg3gIYcwWy3yRcUL6qQq/jK8HCAJtcI70N+u2uJHSe3J1F5LPwX5myBbL+MpWPzZlddPxrcoJvDaxGsj7wKH4+TVtM+WaVdv6oT4iiLdZ2yoprnqO+rJGFcsSJzHtKiD9LqgCKlk30JpwfHhwoyA2RKyJH6qyilraSajeod40QqigbVtpZ/chhJFaGjH8FBCOs04XM9KYFLWD1aQ1qRg2siVs2Nyw16lBzZSXVtCEMALQeFp7twc5P81Ue4+OtLHCX6yZeRqSrYBCCmwNvQ95xAJRWtMey4knO8HP4iEdhLjSun0MSLQBlUJViLAuFD9rHPwghjWqG1eg4sHyDaCY5Xs1V/u+zCqwPQcZAsblEXzDBmk9u5GzZcHkFESIzW7kzK0EyF5wawbIVBdDyxWJj3svSus+YRMAsCR5J+XXMkXkSxIEgfnwAjAnrUlFpWOjuN9lGKMsy5rg5hYmrgnHWUCdg1CnA2U2P10X9jp6fbugoteJGpU1ABz1K4wRUC1Crn2WkTE6yBbLXMhxTKc4o+trXcoD95lVh2+kdbIdV9oZIfJj+GL9zxVrkOS+dyIWFrGeKfpTaFZAY21vV1N9KHrPMSDRztR7X/vn7HtDTwDNanjpKCeI/bkm3aDVRBswyrpQudo6u652T9UEBI9y6wnv11QsS3x9OTzYpgZD3nyp+oGT3wIgA12HUEwyP2tf80vizVgNsd97ULA+29ZB6bqgkNb7ay4trKDuGd2GMAJqYWE9U8S2D8F6ACC3jgIAhsUlS+vJvpY4DsaYXWU50RVIkY6wulvg61brmGZv23cCEAS0paJ4fJSJ7byqrZe2C8jYyYKrVVrPX2Lq70RbXcd5LBxCr6Lu96PYsad23NT6Wsuypjj4HH+Y2q3l7b5e2mIkcTwicp/sKnQeYMp7TbYI9hnnuYiiwPeNTrs7SMQ01J5AYugEAIF1RDiwEgyHRAy2JfCMqOxTG2ANArxNSeu90Q28R2TkUfH0T/n6u++tGAxwExzShjr5AFI8kD2BLdL6prKoKoub2hbunqwoVySu7CNi3g3o4HPHJ9K6XJF46vWp4VbTx3anY0jHwqhIsm8BGbWYXM1gTmatJNgysOLQIBUL44kUmxxyLWOuyU8t5vQ2XWZhjERaK9YgQAOU1gBA4XkTX2dhA5f/lP3/yR8BwIKu6LHefa0V0nrpKp+kDzkYx13pGJrjYUiyhmSj9mEPBBfNMe2pKNr0Fi8D7OlMQRBYuKWXyrMRqg6gOhhqTyIsCsiXJCz4GNZphTtLu6jIIt4S2YFb6jqMzUgXVneLrgOkbEFRWveX7iOECo4PtJo+fFQTfDi9lgHmiJD0x896ei2DsiQjFQ3ZDsWhuXd6PYts0R8Fjq02QAOMdamktS8FB8XWILrIbA2ceMQf7E1jnzCHqFxgXqHt7kkTIgt5UFskrvHmNLAIqZTUItjg41U/Smg+Y7uhlXMbNcpLrnx0obQmT+sae5C5TW+tmuWKxP1lHxluY8oMUn/OnWdrwvW/BACkznwcgsCI8uUdf+ebu0rSvZPxQtjb1QRBYAWFtV1v9mtEcPB2Z0Go8mo3hFTRkNYv8NZ012qg5n6m5pMrKMxd4sWxg24IZEXVW2kbQwYJXJvbxrLFekGWCQd7zS2YnKKvJYGwKKBYkbBkdg3xFvX+r/G1XtjKoyLJiIZEdDXZK3jroV1mZMVM2TtpTfd6dzqmvl+ctNYpUnGl9am6Hx3g2R3O1tL5rTy2ciWERYER32II6FAKZQ66O3p3WSD5a/mRQIrQamB7a93PjvQ1IyQKWN0teg8gH3sOSHUxL9KJr+s+ZHyBqb670zF069gtnFS6uaqU1rNvskJAU68v+TeEP8b3AACObH0T2Jqr/qGitA6NPs0tVc77aBFi2skhisCed7J/X/ss+3r4+6oeovpH+6W0NvCsJ4sQOl+Pf551PfccrwttJnJrM1vCZtbb/nxiZRdlSUY6HkZ/Sxw4/L3K679U3cWgIdn8fk/4SwjMwmKoPOX8yUohOtbL9tI7hTK2cu7I4ikj8tEBmmJhHn7qh0cxAOwkBwAAI8KyZeeWIAh4tIWN/Z14vy+vbwQ6z411phAfPKX+QJNp8PzRHgDeSWs654+0J83XbzqTr90FSjlgWiGMR57RVenSftptMV6WZZW0rrXiHFXmGLqG9XusaC1GgJGn+MP8uq9mN9nz+1sT+r7nggDsf179/57jbF9mAm5347CgeEvhBQ/2NANRTQC9Q7X1wTbgtHCX/c9D0toYHbf/FCFBxquVI5gVB3z7vYVyhS9eTrxmaeGd28y5npC1uM1TlZUFnVoVlq8zG5BaFHbVxUtpofJD/T21xhLkk9GQp9R0+jv8UlrfXtpFrlRBUyxsSZwIgoB9ykJC6iq/MbWmKq0N/aNrcCbJPseV+Jjn18+XKlhVDs9Vmy4iQpau1fuhc9L6af4trrT2ibQuVSQ+0e/tVu6noSeYtUZmBbj4KbapCceBo9/Pn0ctVre8qkNJab18E7cW2Ib7YI99mxtBEDQHOmXs+BnC6JD0i0dC/POdCICsrfKn1EEkJPJ260b6WlPB4Xw7O0CIj/4kHlMUAq/eXQ3uhVuGIUWSiKKM/WHrDWlIVK2Qxhd2VJ9en/ysuTVIj3nYixZd6Rg6m6KQZf/smUgB5IbwG2pPIiQKyBYrnvMWAPD3tnP7muNrOtibxnGRkbNy/0lPrW0zColUpTLRWoToYfEqUxjHW3Wthrgq3aYdT51ajNuD6CgvrUBKa8UepKc5DkFgCjAvBbNbSzvIFitIx8JqUZ78lOcvADOvAzvzQKwFiUMf4HPklRn/1NZb2RJfL50GiQKsoEAkvhe1dUWSMaMEZ3GlNWDP13r2HCPH4i3A8FPeW1gFgX8O63fegCQDrcmI7a6kKigEabj/JCfhXr69YvoUskzw088aYHMy3Q9ufa1nNcUgN6GUhOYyU3BPFZo9B8jVWQEBQKcyh+wsAHnN3qmwqxLZOjZV+13ujyk4c193kxpm6jSMUZKQWGUH5TcLo4EU46lToy5sEEoYozIHXPXa0REKA8d+UHlRfYsQeg2jri1SWl+d21K72bTWID54oQOAJMn44kon3pAOQZQrwLnfVX+4NcvOMUIIGHocj462AQDemlr35bUBdS9juKfStOcDAI5Uk9akanZK1uhBlmVV9NNac84lhfPM66wDQ8lawLF6S9JkNIyeZjZfeiW4SGx2uFfpehx9FxBrYXZzZJcCVI2NEZ8UobW4XhkEAHTn7jl/srL3CPcc5GuJW9WsoWLWIVR1qj/v07TMiN/90TVboc5Hk5sAgGVFwR4UqHP26EAL60gUw+wHmjXgA4qv9fn7G9wuwg3u8RBGi/1UUw/zPpYltkaQyrnGGoQw2O4tZ2t5p4CsogCv47CGFV/rjSlgc0b10B96nBG5Cmi8zG/mUCw7944mzCsh5qaZO1o7EAqCNQF1HTgdy7eU+Zd3BlAhg6wSbeJd0VsICxI2YoNAW7ChooS3H2ldKUG48AcAgD+qvM8/Q32wypsks2qck816SyLCB+JNH8IY79SGbaU61EFFRvFaTHyNtZO27eHt0qT+nljZdW2LQQvngZ60p806EX8z6zlf/LYu83a/lvrWMh3QpjQoL2BmD6IorW2S1odCTNkwJQ56fn06wDTFwmhJaEz7WwZYSrgsVau2MmvAyjj797BKWtOGbW4z54v9w/31LMqSjGRUbfNBOArseRf795f/H/b10Ie4RQmgFoE8K61bRxlBXilge5apeazat2pR5V9aKauBRh6U1twypdv55ou3pvtMGkuSjBsL1p6iY0qYxb0G+lrTOCiMvgf4p0vAE38PT+1lvqCB+lqLIraaGBHwXPsaIrW+jzrgnuwL62qBg0goj6B14YBDos1vX2sae25I60hI9bAkKy5PUN7bvcXbECCpRKgN7OlM4aRCWu+0HfN0GVxprfW83auQ1lPf1vcn1lqD6BDmTqxUZFnW8bRW1BM789UklhWKGWBXKY4rSutoWFWZevG1vqBYg5wablX3FGRNMXdB9Qs9/CIQjvGW/itePWc1uLvC7oO+ljiaYmFXv4PGvpd5eHE7j2JFQiQkVKfJU2fGnAlpTe3q+94PhCIYVhQ3nggLZU0r3Wd7hQMOimNV0Pgnv/sgO5h/46Z52+k1pWB6zMKCyQ2G7IYxcl/rS1XfNlRhOkQ8x96DBanVc+cAkWxV4ybewtS4ALCmUVsvXQcgs5811RMltD+/v55Frmj/rKDbmUVhjHYLZWt3IRR2kEcUt+VBf2yjNFjPFPnnfsKgU+qYxiLEM078EPt68wu6cy6N86MG43x/dxMSkRB2C2V1ffTbzxqse2e3UMYfyy+wb5z/n8yGBODWIOg7AcTSeEwhrcnWyQ+QCOSAkZ2X9m/tP10XWOenqnhlt4BCWYIooM7zHK3DTPEoS8ClP1TP3zV+1vy62v0J+uNZUtQZGo6qQW3jn2dfN6aUXKIQMPqMa6sAM+RLFVwuMLVnesuFzRh1eXUdtB+KawC/SWu/FOk3ckxAMwQdQaEOxsJK8bLiPdvADNeVjIhj/c0smHvf+1nhgxTGYOvHicEWyDLw1XF7168HtfvS4rMRBG67iIUrwP3X2b81Hd9a0Jhxq7Smc+pQW6JeAa71tZ7+tuE8252OIR4RIcneBH20b+5vMdlHjL4DCCs/N/GzJvB73qXS+gDxIVTIcBjGeKp0CQBwNeaeD3GKtx9pfeuLwO4itkJt+LJ0BpM+Wj7QjTfWlXK8WefKZo/EQL5U4ZN6lT+nmUXIzS+wr4c+xCvxfS1xNMfDKEsyJpbdLaDcUsGDnzXAbBCoJccP4vjS/U0A+u1+eqADZlBWAlOrWY3S2p7v12CZef5dLXpvEdKGMNaNW24RogljVFr/0HWYFUQU9LaoarrVjHcVJJGzdffT3vewrwVlU3/yR6ued8gv0loUuYdWZEUhrR3a3OzThhSt3mLKSI9WAl7sFUh56PaeNsL0eha7hTJiYdF040E/a6jSWvHVP9SbZrYLgoCn97Ii0ev31j2r1swwHWKHpceT9trnaB3Yvn9NCexM17WRusUdl+Fx1F3gNZSXUOdT7xC++lp3H4YUjqNZyOJAaNncT68GkZCIM1E2D9+PH/B0GXV+0gDQfZRZLpQyrMW77knK92r8rAlO3qeNbAlZhXDiRFailSlbgGoSywoUwphoY/8poN9LihE3uKCs3Y8Mq7+XdyIs3+DWIEQKnBxi5I6fYYx3HIaZ6mGfD77W06t0oEpWF9+JxF+7C+QMiCJuDcJa/Ie92oNoXje5ygqzrqxBAI1/8kk8d4iRpH97ZxUlg4R7WZa5alfP59crbB98eRhldRjjjF5eiAsIu2wNWUabp3sI0Cita4l0PR97gxBG/pSmGNpTrBvHyXhWi9wadbxTpfU860a6H92HCkK2u0rsguaNPZ0ptCQjuo/xLYwRYGOoYz+z8yByUQMixo2U1uGQyH92aWaL3f9U/B57Vvc5bkCK77ne9wLNA0B2VZ176XygCFporr63mvFshwQwiygikwz969vHgGZF0FNjDQJAtUNaz3gW2ND5qbc5rm9tQGrrb/w6I6/7T/Nibt11KSSSV+uJceXeqgr24xYhn6/23x18DIilVVLYR9J6aTuP2zL7HEKr4+ah0rUo5ViQJgB0eiOtZVnmhQAvntaAe0sFI5zbYvdrc2WjvqNZBz0yK16O59osHukN1+ape0mZa37oU8Anrld5wwPA84ra2otFSJ0NqBnIIuTynzDf+nir+r0akDo6sEIHedZPfkstSNWQ1oIgaPZX7scMJ63NlNbRJPDR3wE+8K9YLo4F9rgo3smyzDtdOLdHWRcO7UFGtljXx7cq3kQ/TvD2I63P/Q8AwPWeD6OEsC+hBwSqbLup5PkVeHV3eReyDLQlI+hs0vjdaklr7cJRKQG3zyoX8SH+bUEQ1DBGl9fEq70eSWsAONjrXxijmUedHoj8v+tTe7wW+VIFc5s5VWm9NWO9sEsVtGYZMfDGrvcWIVMlkF4Yo441CMDUdD1ppjTwI4zRkJylMEaAqX9IkaiA7qXJVU0Aolsoi+Fw6R6zjHIQ1AaoYYy3l3fUw0OfNysBRwt8DfbyMEZ/xzK1kh3qTdcnyWtAnsR+hvJYgYpnB3vVDfyx/mY0xcLYypX4Bj8IXFGKSvtgrxhFc258iVTWp33xrAfU+cvpGFYDIr3PvdoQUbekn1PbC1OEIthuZYWp9zbPmI7dOlRK2CexltdLFfc2TZlCGau7TEk9pCXNRVFdt/UsQqiQOKRPWju517Qet/GIZryRz60Ti5CaEEbCACetPSit7zMS9pERzYGtZZB5wUplRp4k2jlBQwTOlVkfgtIUeLG3IezzoXtrqjaEkZDqUN97PV/r9XuMFBTDPKiHSJzlnYIjtWwVFNK2PT+NJmRVFY4T5LfU8dN3EicGWtCRimK3UMY5A0/cha081jJFhEXBU+C3EWyTJTyM8X5VGKNfSmvsKKS13IqFLW/7K5qDB2sPwHphjItEWteHMBKoQ8VJGCPZiR2pIq1JaX3LHsGlWGittbA53O99xWXFVuikjjUI4Rgnrbe9zzGCoAYy1liE5EsVXng2C3UmG5Mrs5usfV6W2Odq4W/qBETQHx1sBx77WfbNNz7JPjNSWiu+rq3JKB8ffqitp9ezKFYkJDR2d3UQBOC9vwIceAF49KfqfjzQlkBIyVdZ9mBrAGjzgAyKUgcV0rqs3LMGKmtA267v1R5E5+y9773MSnFzmlk+1ihD6bX9CqcEgMWtPCbkflQgQshv8jnMFtbuApCVDpBuvjdyo5o1tXlwCL+V1hdXZDUk3kYAbUthAQBweafFk92EGbZyJf738aJiOMrUxTUgX+tX765hx2U4JRWhbHUPUxgjhcuOPG14nuZjZsMtaU2cnsFej1Tn1z7LCoTRtCoa0GC4nTrZ3I8Zw0JzLQ5/L/D0/2HLCmqEArg3sigbiAL0rmO3UEYkJKhcJzk5bEyyPZwdbC+gafsuJFnAF3b2BZJHoYe3F2m9NqFM1ALWDv4wAJ/UWgroED1mNMBNQMTAuAcPaUA9BO3vrmnNHH4KCEWB7Vn2PhCmXwXym8wniAhKBYc9KlZJ4agli9yCiJbbHomT3UKZEYiwDmEk0AFzcjVj+8a2C1oYMrEeyILIFBZWCayb0whVCijIEby+2ex54aoL4NKClNazb7KQK4C1wgB1pLX2d/jha21IzraPqYfyEz9UR+r1NMfQmoygIsnelfmKr/VhYRqjHSkkos4IRBq302tZVGYVj2IPftYVSeYHM1ekdVdQpDUdQM2VbuRZ1ih7kNXdAlZ3ixAEtYAAMEXSE3sUX+uJYHytJUnG326xolJ33p6f32FlrhzJK0ozn/ysSxWJF1WdKq0P+WgPog0R1U3AtgFfldYA7icYUfJ4bMrZE5fHEZFL2JaTeHPTvcKTlFrN8Rp7JkAtyE3UhDHuLisHHMFwjNBGe2rVWk1GxFrdGuAmjJGHMFYT+f2trKDplrRe3S1gei0LQagpOAtC9UHhyPcBIfY+Hu5rRlgUsJ4puvY1rIUXexsCPddLtgCRC9TqXgWyCJnVIa1vKSrrkaeZmh7MfzodZ1Ynbg94aOoCWoYgQsYxccqd0npRSZJvGQKS7RBFAc8eZF1oL9/S3xeR+nR/T7q64OITbJPWBmGMNO6GHHRx1KGY4Z1lS3IbFrwqrY32fHqktYXSGtCEMdr0td7IFPkesYq0bt/DiimlDPNGtoKitC71nAKgkgx+gZTWJ00ELkf6miEKbH7yJWfhuOJrPfktYHuef3t8YRsVSUZnU5R7H+uBrvXyzGYg1iCASlofH2wFHvkpIBRjtji3z2qsA1Wl3xkfLULuKIWRfd1N5raTJz8O/OingWR73Y8iIZET3l6Fa3VZELXoO6mqvgHgaL2fNUElRN1f0+puASs7BWXPq5mDoylepMSNzwH3FA9eZWxQ4XJuw5v3rhaL23kUEMViWMkOW75h/8kUxtp5ENCoVd0Qf7TvHNSzeXAIUlr7Qe7nSxXcW9nFfVkRn21MmT9BlhHeZvPitNTpq72tFlRQHGhNoDVpvk/f153GWFcKxYqEb9wyz57Qg1bIYos76zlS/f8GftaAarc3v5l3xd0YhjDyF1B8rct59VpC9ZZxfhQ65rjSuj6A1y36lO6QUkW23b1F58C9XU2q5WWyne3ZANUC1QrK2nRN3oOFYoILd4JGQ0jr//pf/ytGR0cRj8fxxBNP4M03dVpl7eD877Gv+9+P7iG2OfMzKZfIpDEHIYwE7mW6uOOpXYmUDnXERDSpktL3NAdg8jY88D115B+p/twoEXcLZR4S5I/SmjbF3jalV2Y3IctsMu5O27v5B1oTSERCKFYkT5UyPdAme6izBUJaUUJY+VovM0JrEv0oSYLn4AzTTVfPMSCSZNWz1dvsK01KOj5SpKbzR2ltoih+zz9jQWVP/u91PxIENdDOM9HWcxwAcFi872oc9zTHkI6HUZFkFGeItHbv30QbylhYtK646oDsOeY2c+4VdTq4bhHCSBjrVF8/W/TuT28F+vyH25NIRqs3E9zXeiIYX+vJtQwuFVgrXXRrUvV8NEFLMoL+ljhOiUqisk9+1tNrWZQqzB/e1BNNB6wACqxlip7CVgCNStWlNQigjiG/SOurMvMdP1h2GDiodE5ckfZ4WpdUP2sdUovIhvmLQFYTZEVhSl2Hqvz8tRhsSyAsCsiVKliy8MDlystaFZIXpXUNad2njLt5lypR8rPe391UT+4PaEhrjZItHglxQcAVr0FpCvwYw0Raz2/lXed0TK8aKK0Bdd7Q87XW7vkUCILgy8Gq1HsKAHBCmHBHWhNB2quqep8jX2sj0pr8rC3WHrdwpPDTCWOcsyK17EBRKBbFOHaRcH0P8Wvi/vW193uNPUi5ACwrJKQpaa1Y6NnsRqQzxXB7Es1xzb0ciqihsisWhbJKie9F46NMXOGnCEmWZU3+Tavh4xLREBcn+GIR0r4HGHoSgAxc/Qz/9jUeNtpiaj9JBb0bC9uQJl5m3/SRtJYk1Y7n+EAL6+w4/jH2w5f+IfvaeRBIdfLnPDrCiGM/whgtQxhtwi9fa8tOCkHgNkwYfBxoHTL8Xdx6wsM13VSEbyPtSaRqMxfIIuSN3wZy6yyzRylwdqVjSERCnr13tVjYYvuOlYSyF6C5xA6ocNbF9iBeSGsid736WQPqZ7S0XfB8hrmztAtJBhZDiuWGFWmd24BQZJ/vnNzpqLPFCa5zaxB7a6oXixCtkKXNjpCl6zAAzfxn4GcNAD3pOKIhERVJ5mPR0bURp2c0buLN1SI0g3nW695KlmUu9jANYnQIURR4scpuAYTcDuqsmWh/YNciRCGtL0UZH+Kn64UZAietP/3pT+MTn/gEfvVXfxUXLlzAyZMn8fzzz2N52UKNWotSHrj4R+zfZ36GT16zG1nfqopqAqrziXG0I4lYWERO40ntBrSg63p91fpayzJwUznAkO+WBl68gYks6mmO2ZuILMCVHB5JSLIGIa9LOxBFgbet+O1rTZ7qo50pdTOzZUFaK35/S3GmNr7r0Z9Y10+VEIqoSr6ZN4D7b7B2w/axOm8rwD+ltSzL5oGDx38Q+Im/0r0GQDt2PVo/9ByBBAHdwiZOtTtvfRIEAfu7mxBGGdHV6+ybHkhrCgHb05myFSJai46mGNqSEWZp55MqSZZl3KCkaQvioD0VRaviDel3aJIeaO7SI1DI1/rNyXVDv1QvuDa3hRW0YkdIQ5ClavWaCU71RHBAUFRmPimt7y7bVCfpIBEN8Y261/uJF6JchIgSSPVwfz3ry+f2tznmO96dvc1IGrtQAteuymOYWNl1fS2kbNX1027uVzbpcnWIsoU1CFAdWmllpaLNNaiCnvLSCuRpXePb6dXTWtfPmkC+3k09dcobNYxx09XrapEtlvl7td+tZzNYyzzZt7ntepleNyGtudL6XLXNQm5Dtfc6+ELVUygEzMvhYanpMADg8di0oQewKXRUve/a3wVRYHvbWR0VuJbMCwLDyvu7ulu0LjDUhDGWKhK38vCDtM7HuwEInpTWu4UytnJsL1On2qL7fW2CBUcvjzPbnXirqqTSAd0LdkkU3pnVp7NfsNvdsXyDKdxiLejbw9R3U2tZ3zIq5rfyWN1ltjNW+5pjfvpaA2og45U/49+6atO3fbAtgfZUFF2VFYjrdwFBNFUjOsW91QwyxQriEU1+yRP/G/u6w6wLyBqEQGGM1+a2PVv23TYiTRxitMMZWWOEWbNOVcI7/iHz1n7+X5v+LnWuKbguZtIe7bDevXXgedbJUFDG6eg7eFeSX967WiwqROFuizKvuFVaQ90fzW/mHe+16KwxqteV5BAtyQgvmnsVsVHxLpdS5lYr0loRtG2H21FAlPM9fuM6LwTbW1OJtH755rLj+1vtHLb52USTQIfS0RRrAXqPGz5UFAW+7jq1lSlXJO7vblrs0M6tBqS1WnBxd19tZEvIl9iYrwt79YgRXiizSVpzu82a+ZeEBnbCGDWe+rOtjyuv768g1AjuotMd4Dd/8zfxcz/3c/jpn/5pAMAnP/lJfOELX8Dv/u7v4pd+6Zds/Y6vfumvkMreB3ZGgNBx4M4ucOclPB2eRrEs43N/sY1mNxtsDQqVCvZmmW/p9IUMZi845/PfE5/Hym4RX/7Cmv5BxAbkezM4gwrke+s4u16jstlNAOXTwPVl4KU/B3ZXgbUOQOgG7m4Ddz9T9fAKJDyG+5B3gM/91RriYfutlzcXd3AGaxgUYjj70qarv0WLklzBGcwA28Dn/3oVUZc+r5PXl3AGOezZWcLZlyZtP+94aRlxZHH5m4vApH+HiI5HwwAA2gxJREFUo7m7yziDLHrWFnC2vIdtwl85D0yZbLwn7gDl05gKD+IMxnHlW4sQptxfU9PCNM5Axvr1TZyd1mk7zBwGyrvAt18HxCQbQzgMvPSZuofGF9jnnhmfxNmQzTAdHeRKFRzIz0AAcO9cFtNwRrS1r7DrWLlyD2dlBxslHWQq70RK3kF88mWcfclGy2oNjpZW0I15fLV4FBAiwGsXAFy0fJ4ersxu4wzWMVZJ4uxLDgt3Ct4RmscCivjWl9cw2+nN3w0AcsUKRndnMAZg4UoWy1fMP6t3hOax6OPrm2FSub9Gtxdw9qWpqp/JAN4RmUa+KONPPr1m2nLrBpcm13AGO/iS9BSaKyvA2b8GOq3Jv4ObN/DVyikUhSSi3/q2P9cyvYkz2MSBYhJnX3KuhnhMXkIHcnjja0vI3nKvaFwaZ/Nvx/ISzr7kUNmswdOhaRQrMv7qs1ue1+6dpWn8Nc4ghgrwmf+uhg9a4cocUD6NGfTgZOUG/vIzW2hJOb+W6XtsnAxszOmvSfITQDkOfO0sMKkcZK+yNQBrnbrzMOF0eRHtyOOtbyxj56bxIX93fBFnkEdidhlnX7qm/qCYZa+zJAAvfRqAjXV3Euw542vArHpta7tFnME8kosCzr60YP17ajB3eR5nUET/1or+2On/B8yi4m/+surbvetsLVi8eA9nK9cdv64Wq8rfkIgIeOsb3gqzT4vzmEcRf/uVVSy4EDq0rbB1e+3KNs7erR13FaDyKLAjAZ/5PSCh3LNrd4DSCSDeBrx6AcAF/oz+9Q2cwRbuX5zB2a1OuMHsFDBYPo2cIOKsybg0xPgqGzuz4apx/aGmeSzsFPGZzyzzsFpCeYK9D8LUZv2e1ye8IzKNfEnG5/9qy1yEsSUrY38beOkz2M2V8Ig8h3BIwMVvehgv63eB8mkUij04g3GI0xOO9q9abGZKOIM5xMICXv1q7TXJQOUMUK4Af/EpYOc++3uEQeALnzX8nYWKsj/fAL7w+VWEBfPzz71bbF3es71Q/3es9rHXfOsasGYyhpavssclBiG/9iU8IU6jUpbxV5/dRFNtJ4YLTK1lcQbL6ExE8M2z5gfqwXW2L5s7P4mzBWdhVLqoxIDKI8CcDHz6t4BUB7ZvzOEMSmheWsPZl8xbsJ8NLyAp3sLZ8mkg1QN89Sver0nBxAp7X/riUXztixqiI/Y9QEbZW6x11a1L74pMI1uS8ek/W/NEvBRvs/chMrOOsy+520cDQPMC+8xWr03jbPma9RMMEJ2exRmUkb21jrNLJsR16sPA5fvsPxO8MzKNXEnGX//lFtqbnAu+7t1m99bwls69BQCxDwI7SsZK5mDV53SiuIQ0crj0zWUU7njvkM7cZPu9W9kkMuXTwI1VIGxzXbizy+7vyRKw8RnIAJ4ITaNSkfHXf7Hl6B5fV879rYvLOPuSA7W3AZ4JzWNZOcNMe7B9uq7s/RZKbexevb4MSCbvz+YkUD6NnNyFMxjH4uUpf+abGuwqc01yYR1nX7ps+XgZyv1dlPFHn16pz0owwbUpdjY5VEjh7Evz1k8AgNIpoJwG0qN1+71anCgtoB0FnPum+R64Ftu5Ek5Jc4iEBFx5JQfDGXezhY3TUBPw5jUA9XPJdo6tuZFVAWdfcm6hQvvOZETAy1/ypwuCMLLNxuC9t2Zxdq3D8vH5W2xsxOfWcfalS+oPNsPsfbixCkQs7vHsBrDZBwgDyFcqOINx3H5tAWcXWnUfnsn6R2gLcoDu2cViEclkEp/5zGfw/d///fz7P/mTP4nNzU187nOfq3p8oVBAoaAqpba3tzE0NISuz/8txJS3VqKHeIiHeIiHeIiHeIiHeIiHeIiHeIiHeIiHeIiHeIiHCAZSZhcr3/tObG1tobnZmw1coPYgq6urqFQq6OmpVj719PRgcbFeLfbrv/7raGlp4f8NDRm3sz3EQzzEQzzEQzzEQzzEQzzEQzzEQzzEQzzEQzzEQzzEdx8Ctwdxgl/+5V/GJz7xCf7/pLT+5I3fR2r7Dkv21oTPnJvawMXZLRzpb8IzY+7aIf38XQtbObx0dQnNsRA+/phzwn1iJYuv31pGXzqKF0/26z9o8Qow9S3mUZffBCAAj/0MENJv57gwvYnzM5s40J3Eswe6bV/Lp16fRqEs46On+l21OOnhrcl1XJrbxpH+NJ4Zs25jqMX0WhZfHl9GZzKCjzwy4Oi529kSPn1hDtGQgJ94asShWYU+SnIF//PbrE3rp54ZQmR7Hhj/HPtsTv24/pNy28DlTwGCiK2jP4s/u7SAWFjATzw54uoa5jdz+MK1JbTFIvjBx0zek0t/qIwXAKFm4LGf0H1YBRJ+9xXW/vaTTw+5tnF57d4ars3v4MRAGk/scf5ZA8Cn35rBdqGCF4/38BAwp5hay+L2zSv4QOgckOgETv6w49+RLZSRufgn6MIW5P0vQKCgIRf4gzdYm/IPnOpHh8v7anYzhy9eW0JHIoIfeNTZfaCHr95YwuR6Dk+OtuP4oHUVdHo9iy/fWEZXUxTff8pgnvIB27kSPn1+DqGQgJ9+ahiCzl1L93U4JOAnnhpGyJc7u2a+OBKBMP4XQLgZOKN/31ThrU8BlW18ofIE3vnIKTR7bHWWIeP3Xr2PiiTjRx4dcNU6Te9lOCTgp1zOf37OoeenNnFhdtPz2n19dhuvTq3jTHoLp3N/az73arF8Fbj3TaB5CDean8O3p9Yx2pbA+4/atBbR4PdfZzZlH39kwNjq5NpfArtzwOi72P9PfQtoHgaOfJ/p76Y9RWssjI89Nqj7mKJUwe+/ytahn3nHMEK1WoR7X2PetoNPqN7RRticBm5+Hkh0ASc/XvfjP39rFpuFsuM5mT6noZYYXjiun2NghpcuM3uJ5w52YZ8LKw6C07nODLT2mn02RriznMHLt1cw0BLDB43ej7VbwJ2vMHuA4x8DtuaA8b9ke73HfgaouQN38yX8ybk5hEQBP/20u/vzj96YxlPSeYwJ8/bGixZLV4HJbxqO6z9+4z4yJQnfc6yHtx9fnN7EORf7U6dwtL+/82Vg7TYw+CTOl/f5Mk9h4qssy2TwGfzuTDsqFfdz+bnJDVyc2zLeR8+8Bcy9AXQfZgGscgU4+eNAotX093773ipuzO/i1EAzHtvTbvi45d0CPndpAYmIgB9/Qm/fWgFe/yQAGXj0Z4GI3jxRAl7/bfaY0z8DxJKav8v7eU4GO8MUyzJ+8HS/rVyez56bw3q+VDU+vaECvPHfAbmEmZ4P4EtzUQy2xPA9Nua/4tYyouN/hgpESGd+DpGwd7sUoPp9+aHTA47ssJZ3Cvjc5QXElc/dzfwytZbFV8aX0d0UxYc97h0rkPF7r0xDBvATTw4h5sD+kkBjORUR8aNPDHu6HsL56U1cmNnE4b4U3rG3y9FztzIl/NnFOUSVM6Hhe1zJAWIIEKrH9dxmDn9j5zxoAzJk/O4r05AA/PgTQ0iMfwbIrgCHXgRaR82fbLCP+Pa9NdyY38HpgWacMZljtNjNlfAn572ta7Wwtf5aIFso44/emoUoCPipZwYReuOTzOtXmc90cesl5nu951l8arIZBRf3oRXG53fwyr01DLfE8LyDv02CjD96/T7yZRnfe7zXlgWQDOD3X5tGqSLj448OeD7r6IHmjN50FN9rxIvpgNaz04MtODOqk6PiEH9zdQFzWwW8+0AX9jvM9Hljcg1X5nZwciCNx13yIUZwsu+7t5rF126avJc3PgdszwBjzwLdRl7jFRYEK1eAYz+ELaENf3ZxzpTHymSz+Kjjv0wfgZLWnZ2dCIVCWFpaqvr+0tISent76x4fi8UQi9X7kr5v9wtoDmeAD/8qsPc5/v3Nt+7jd2avIpHqxL948QlP1/oXf3AO52aX8MFHjuD5d+yxfoIO1jNF/NrVrwAF4D+9/3k01Sb/WuDffekmzt2awI8dGcbzLxoMmNVTwH/5j0AZ7NMbfhr48N8x/qXXF/Fbf3AeuXAz/s2L77R1HfObOXz7la8jLAr4ox983tVmQA8752fx3//8MiLxDvyLF590/Pxf/5txnBu/hx89bvL+GKBckfBPL59FsSLh3z79HE+U94Jrc1s49+1X0NkUxYvf+34WfnPn1wAkgA/9JUudrsXNvwGuXwR6jqPwgz+MX75yFpWyjN9813vR0+zcJ+5P37yPc9eu4t2jXXj+xceNH1h5Gbj4Dfbv4z8EvPiDhg/9pxe+jI1sCf/izDtxxCLAxgh//Ltv4hxW8NEnjuP5x91tBj+7fg7nbizhgyPu78nf/PItvDYu49+HfweohIEX/ggIO/M+lssFlK78AqJCGZNP/xfs2X/M1bWsZ4r4+VeYN+Ef/MALSETd3VfTaxn8y2svI1YS8d8++IKrQEct/tWNr2MGOXzi/U/wcEMz3Fnawb+58S2kiiH8tw89D0FvnPuAL11bxLnz53G0pxkvGMxdsizj3935GlZ2Cvg/Dz+Jp/b6syH4y4uzOHfhMs4MtuGFjxwC7vw/7Afv+0OWOG2EnUXg3M+iEhbxf5X/D7xw6GlHG0c93FvZxRuvfBPxiIiPfPQFx0GMACBJMn7lylnkShX86yeexd4u53ZbX76+iHMXzD8Pu8hcmMVv/5n7tYDwrb+8inNT9/HsiXY8f/4/sXXxfb8PxC0yAv76m8D9i8Cj70Hz6IfxH3/ndSwJCfzGi+9x9Prae/oPP/IC4hGDe7p5Cvj6S0DTABBNAbMXgcdfAN5tPA8DLAjp165+DaGSgP/ve15AJFTfHHd9fgvnXn0FHakoPvji++t/yd/eB772x0DXftN5HwAjm+5eBPZ9r+5jP7X4Os7dXcOPHj6J5x+xT9S+9CcXcQ7zePbxA3j+vfttP49flnwdn//2FI51jOIXXjzq+PmEf3frZUwgg3/wnsfxrgPOCIVaLGzl8M+vfR2hkoD/9sILiIbtNy5e//ItnLt9F/sPmuxl1iaA//zvgHIUeOFTwFd/DQhfBE79GPDix+oeXpFk/NLFL6JUkfHvn3lOP5jZBKu7Bfz8K1/F/vA9/P3wF4CWfuDFf2v/F3z+b4GZi8AjzwHvqx87Lxev4E/enMGx5lH8rPIZfvZT53AOS/iex92v8Xaw/iY7K6TTFvskAPj2HPCVTwNtQ/gSfhjnZufwvpOH8Pyze91fwP/8n8DGReDpX8C/OduJ6bUs/u8zT+Fxm8SNFi/9yUWcm5vH86cO4/l3jdU/4JoIfOaTQP4+EFoDIingoz8DiObjc+X1aXzqr66hqakL/8zkPfrjN+7j3KWreOdoJ543Onfd+7fA+gRwcpgdgGtx/3Xg3AWWP/ADfwcQBGy9NYPf+ewVX85zd5Z28Oor30IiEsKf/uAHENaZN2txNnsJX74wh3f07sfz7zvg6fU5pFeAC5/CW1I//hV+DI+f3ovnXzhk/bzX/htw5yJerpxE9Nj78fQ+byQ+4e6y+r589GPPO9o7FssS/tX1syiUJPza4+/Cvm7nnsn/4Su3cW78Dj52cBDPv3jS+gkW+NfXvo65zRx++fTTeHTEOTH10pV5nLt0EY8NtOH5F5/2fD0AkL04i9/+9GVEEh34lw73Np+7NIdzFy/h0YE2vODieu6vZfEvrn0DsYqI3/qgu70iYWErhzcVHuDPPvw9EPEN4PKXgaEPAc9a7CNe+Q9sH3Hwo1X7iNlXJvGp+Rvobu/FP33RXkj5t26v4Nz5N7G/swkvvKgzl7hA5/Q6fuP2a1gQEviPDvd8hK/fXMK5t87hQHcTPvjis8DErwMbk8CjY8bBqff/PbBzA3j2n+GThQQu3t/ETx0+jedP+Cf++dpnLuPcvVk89eg+PP+Bg46e+5XMJfzFxTmcbNmDn3zxiOXjZzeyeO2VbyASEvCnP/CCrXnWKa7ObuHXx19BpxzDf3nxfbaf9wf//Q2cwyo+/uQJPH/Gu2vDy8UrOPfmDJ4edP6+fu6PzuPc3CI+9MgRPP+Mv/uciiTjly9+CcWKhN+w4LZ+4+wtnLt5Fz9yeAjPv3ii/gGxq8C3/xroPm18Vrj/OnDuHJBoB37o7yFfkfF/X/wSUAb+y3Pv1y0Qb29vu/3z6hCoPUg0GsWjjz6Kr33ta/x7kiTha1/7Gp566imTZ9agtMtU1nuqJyxKkp32IbWS0mnHPKh42lNRHgZGCZ1OcGeZJcnu7zYhFDr2As2aw+KhD5n+zsO9jGC5s7SLss3EXrr2sa6Ub4Q1ABzoYX+X3YTyWlyc2QQAnBpqdfzccEjkCbJ3l/1J7L1Xm2jconwu5RyQXdN/0ooSbth9CLFwiKfSTri8prlNJfnaShkypDkEjJhvhihFm363G0yssL/HDTlGOKSk27q5lwjjizuYRwcK4TQgldVEawcQlscRFcrYlFO4nnVfsaX3ZKA14ZqwBoDBtiSiIRGFsoR5D58RAGzlSphZZ7/jaJ+9MNDhjiREAcgUK1jeKVg/wSUMU441EAQBTytE9WsTq7699pVZls5+fLAFSLYDaYV4tho/c+cBAMuxUWQRx80F74s1rQv7uptcH0JEUeDzr9v7aWKFUsK950vQXEzrrlvQezM4OAS0KlX+eRvhTvOX2Nf+03x8zaznkCmUHb0+JZr3NMeMCWsA2KscjCa/Bdx/jf3bhoq1pzmGRCSEiiQbpqfPbShrQJvBGtCpEDCrNua+9Xvsa7sOGQagX1FXO513LkxvAAAecUEsAMDJwVYAwJXZTVfPB4BSReJ7xX1meyyb6G2OoykWRkWSMW0zuZ1ASeujZoHd7WPsYFApAovXgFtfZN8/+D26Dw+JAieq77vYE9O+bLnpMPvG/AWTR+tgQQl86tMno959kCmpv35zGRSlc22OzbPHXBbH7YL2WdMG91AV+k6xrwuXMLvBHj9odG/ZxY5ih5juQZ+iYFvYcrd2zynXZHm/0x6097glYQ0AB3rYPHhnyXwven2efWamgoYuhZg1Wi/nlLHV/wgXd+zp8mdNAIBLylnh+ECLbSLl+ADb/9CY9AUnmMr0yOY3EEMRxwZsBq7fexkA8Ip0DJc8zHm1uDqnfnZOxQ7RsMjPXuemNly9/p1lNsfQWPOKEWX+dDr/EmY3bJ6fHF0T8RLOr+mmsjc7ZLLnNUN/axxhUUChLGFpJ+/qdxAWttjze5rjbN/ZrawLyzesn7yiBC13VpN7NA/ftzMPK5hS3sfRTvfcTC2G29nvmt/KoVCuuPod4wvsszrcp8yDbaPs68aU/hNkGdhUQjxbR3BAKfrctphvneL6PDtzHO23Oddo8IGjTEx69voi7MTd3VtROZAgCGsAGGpn9+bqbgG5ov3Pyg9OTwsaM7b2EDWY22T3Ur+P8wwhJAoYVubBKYs559aSxfzbpxDZtJfTg7I2YexZQBQRj4TQqwgurV7fDwRKWgPAJz7xCfzO7/wOfv/3fx/j4+P4+3//7yOTyeCnf/qnnf2iMz9dt/Gig+/sRhbFsj1CVg8VSeaHCK8HcprAxl2QFXesBhTANnhj71b//9AHTX/nYFsCqWgIxYpkezM4vsiu/VCvvweJfd1NEARgLVPE6q4zsqtckXBVIZJOuyCtAWCfQtrQxskrppT3k8YhwjGV3Nqc1n8SkdbKpn6vMqESoekUtOmyVFVpSWujKrAC2sDR4cgp8qUKJ7z3elgwDirj7+aie+KPEXQC8h1K1XjJRcq4QoJdlfbgzrL7SZkKE3s9EiYhUVALMC7HDeGGssEZbEugxcjaoAaxcIhXc90WW+zg1hLNQ+YbeE5a3zMoFLkAzTV0kLW9YZ89BwDY6WDEzY0F73MNrQv7XSibtCBy1i2RfldDnnvFWCf7HYvbecdEsd417e9OAwOKckcpHBiilFc/x75TaE9F0ZVmxWanBVU6fA1Zzb99J4FEG1DYVg8uA9ZKI0EQLAl+dQ0w2BB3KQfH1TuAZLFPItK6TV8NQptu2oTbwdJ2HnObOYgCcNLl2n18kN2H1+e3UbJZfK/F9FoGZUlGKhrixKEXCILA17c7DudBOviMmJHWgqCOkUt/xFRcoSgw9pzhUxyRszW4rRAmcu9JQBCBnQVge8HekyslYOk6+3efjoIHwDv2dSISEnB/PYvJ1QzWdguYV4iRo3bJPJeg92V2PQdJsjiME+m+eR9ba8sAbOyvrMBJ6z5N4ccdqWQpVOjYiyrrGIMiQi2oqDm3mcOuyZxsixTpUohz2u/WggoiA4/wb9E8N7eZQ77kjkgiUNH5xKD9cUWE8lU/SevhpyE3DyAlZ/Fe8YK6nzBDpQRMvQIA+LZ0DJcVAt4PXJ1ln52t69ABtdmfm3ZHWhNBt7/H+x4CUAniKZfCNbUo5b3zlkAipvmtvONxTLzBoT53Z+9wSOTFLK9ivkVlbuY2Ed3KGcoOaU0F8q7qjgVOWju4NiJG9/hIWnc2RZGKhiDL4KIdp+CfFfEk7cqeyYi0zm0ARWWf0DLE74E7LkV8eiiUK3wPe2zA+Rh69kAX4hERsxs53LBxTvBDnGaFlkQE6ThzLZixyUloOYg9nX7NNVQgc35fzdsVF7rEKCetza/NUghGBful60DFYA8woXTsa3hIL++NUwROWn/84x/Hb/zGb+BXfuVXcOrUKVy6dAlf+tKX6sIZTSFGgZM/WvftrnQMyWgIkmx/MOthbiOHYllCNCx6roQcckm05UsVftDYb1WF3qeotnqOGSqiCKIo8AE6blNhZ0fh6AbJaJgvWk7JgdtLu8iVKmiKhTHmcoLcpzzPL6U1kQhVFeBWxQqDiIlaLI+zr5y0ZtdEKkanmLMiLAid+5nX6+m/A1h4Mg+0ss/IrdJ6cjUDWQZakxG02/ASNMKhPrUSbXnQ1MFuocxJpdiAcpBedEFaL1wCAFyVxzyNHXWB97752qt4ankljUk1ddSh0o2PWx9UUUa4yech82t7SvG/vHh/E9miewKUUJFkfjDnh16+YR83f7JCmIaHzwDwVnAh8A4cjwc9dW1yt0mmAokfpHVLMoIOZW5wW51f2y1gPVMEoKgpOGltoRBdus66LpKdvDvmYA/NNc7eG9p3WNpNiaHqTrGuQ5YeswQrBaIlidW2BxAjQCkLbM+Zv9jGJPtqpLRuda4SJZX1wd5mx5ZphD0dKaRjYRTKkutOrbuaoqFflkZUgHS6LpAKj0gXQwyyeQQXfp993fMsEDO+/+jw4ETJRrilEEp7+rtVhZydrgWAKWorRSDWYljwSMXCPN/iG7dW+Bw71plyPS7soq8ljpAooFixoUBMtPK/oSfDSFdPSuvCLlBUxmy6F30u7iFCsSzx7ibDs0okoe5DAcMiQi1ak2rxzohIqUgyX9NM9wxOlNYKOlJRpONhyLK78avFZUWd7KRIdqSvGYIALG0XsOxRpcohiljd82EAwMeir9obR7PngFIGpXgHbspDuDzjH4l+dW4TgAfSeoTZ2Zx3QVoXyhUu9PHrbOmX0tpzJ4UGbUkNyeZwHN8k9a6H94fWFDfdNlosGJHWa3eBsonoTJYNldakmt3Ol7GVLdm6Dtof+klaC4LgSREPqPvow8o51VJpTUK2pl4gEuc8j9OCtxnuLO2iVJHRmoy4IkgT0RDetZ/Zpp29vmTxaPVM65eaWQ+CIHBRiN37icZMSyKCNptiLCu46RIA2Ly3YrVmewQVyqZMzuPZosqHHDTiGNv2ANE0UM4Da3fqf57fBmbfYv/WkNb89b8blNYA8Iu/+IuYnp5GoVDAG2+8gSeecOhXdvhFIFXvVerHxAMAE6vsxhvtSHr2h6UJ7KZDhd3Eyi4n+jqtAtqOfAT40L8HPvo/bP1uqtraVdjxhbPPX9IaUNWCtx0SJ9Tud2KwxfVnxCubPpPWY9rFtEXxTtqcqX+CVAFWlcVcUW7Sode90tqiVZQgCMD3/1fgw/9F32tbA6/2INrqqxdyYLQjhVhYRK5UcXWIIXKjKx1DfFBRGy1ddX4hGqW1W8IE8Fepuq/L27gh3HDZSkZj/p7H1zdCvqQecKyU1kPtCQy0JlCWZLzlsm1Vi4kVViBLRUNqlZ4O4WYqE0niY6Xr0DMA2KFoO29vc24Eatf2qrTmdjsuxrAsy7i37K+qYg8fQ+7WbrqfBtsSSEbD9pXWpPLrP83nQq5Cd7gukUpnyM6hlyxCAEcBd2OWSmsLtVgorKgvYW4RIlWADeVgZUhaO7cHuXBfsQYZbrX9nFqIosDV1tQF4RR0H/kx/xL2uSCtt7IlbCoHdlOlNQAMKKS1pBTjDr5g+nA3SjbCLYWMPNCTVhWwdklraiftPW66v3j3QXYgfvnWMle0Bq2yBhQFojJ2bb03/acBAMeESSQiIV5gc4Vd5fAfSQGxNA8wdaO0XtzKQ5aBWFg0Pyd0ahSONpXWgKq2NrIImVzdRb4kIREJqbZ4euDdHTrzTW6T+V0D/H0G2HluzOOaADCSgFSQZCtkB6lYmK9tflqEvNX8AQDAO3ERQnbd+glK+7Ww510QBBGL23ksbXsn0bXF+OMOFOhaPDLMlNaTqxnH3bKTq6zTJR0L83Zyrxj1qPCbs9up6gCCIGhIHPvXtZEpYlH5nL2Q+iO828YbgURjro8+q+Z+VpSUyoy4NsLOAivSCaK671CQjIbR2cQKY3bPdJO1Npw+YbTTnjpVD/lShZ996uxB1if1n8StQVhBkebaqdWMJ6cALWjeOtrf7Prs/bxiEfLl64uWj73no2WgGajYYZe0ntSo8/0SKNBebT1TxI6DMx11LCQiId8I9Lpr67TmQanLpbMpho4mg1wvUQR6lcyuhSv1P59+lQUwtu1RxzuAkc7vIqW1Lzj9E4Y/okVrctX9m0UDfMyHNgKtms2OJxCBNokHutPWN5koAo/9XaDbRqAH1KqtnQN5sSxxIsxK4egGB3sVX2uHxPGlGXbwdeNnTSDS5+7SrqPPxgiOldab06yCFYrxG54rVl0Q6aWKxDc5fioFVHsQl6T1Mi1k3jYZIVHgVjluFKu3tP5wNBEvXmNKALsoF4AlRlRelccwuZpx3Z7upycwL3Z4sCsBtK2+zu516nbwcrg0w93lXUhKEa87bR6cqfW1ftUHX2sixY72awpkdpTWa3eY/UMkifTQcfQrChUvnuwVSebzsWnWgQ3QQWh6LevYkmN5p4CdQhmioG72vcKrr3VdBkTfCUAIKbYG88ZP1PhZEw669M+ftau0BqpCpJ2Q1nRYs1Jam64BnUr4ISmg9LA1C0glZkHRrB8M1K9ZG+yuoRfubwJQCQ+3OKEQUJddktZ+dgoQ3HRvEZnAOgUtFMYa+wQAwAF9P2sCF3E4JCxkWeaHmoO9afXesOtrvagccCwI0ucOMV/rN+6t481JRuAdd9HG7AaOlFL9pwAAx8R7GGxLeDv47igWK2lGBrjpViDMbioihVaLayLSOhRVC642wEUlBoVN2i8c7kubi0fo9TMrQKbGtosKIa0jdWIkXshcdV8MH1/YQakioy0Z4WSHXZACmWw0/MC3tztxTRpFGBXgxl9aP0EhrcP7nuP7Xz8sQiZXd5EtVpCIhFzvQVuSEU62OfW11lqD+EckuRetybIciNIaAPeYdXJddD4fak8gHXdPbo3YtAqwQp3SWtD6Wpvsg6m7om2Pbuj9sHJP2pmHi2WJf0Z+q3lVRbrzsXNniZ1P2lNR9XxiqbSuJq17m+NIx8IoS7IvPv4AcG2eMiLcF4Lfe7gbIVHAzcUdy/HLBWo+7qn0QGv3jE1O4p6eoNAj0nG1c9wJOUs8Sn9r3Ld5rxYqD2pCWtv1y+818bXmftbvrnn97zKltWcMGns/jtqoMFiBNkh+TIpjXSlEQgJ2C2U+2doBbRL98vrSwonSemJll1XD42FOuvgJ2oS5VVp7Ia1HO5mSfqdQ9hwgt5EpYivHqm1VFWAz0npZ8ffrPMDaxaESu/Nbzr1dF7fykBTVTZdR5cwFBn1UWnuFWwUkoI73Q71poOswI7Ry6+oh0g6WrgNSCXKiHRuRHpQl2bQFxwj5UoVbCfhCWvugtM6XKpzIcay07vJ+uDQDtwbpsVHEA/D0Pnb4fX3Cu681KQCr1EikHMssAxkDYpwUvn2ngFCYz7tu8g0IM+tZFMoSYmHRHjFqgo6mmGvvZiLlRjr8C+f1GrxV17kQTanFBTO1tWL3U0Vau7UHWXdAWrcOM+VsOM5CTGzC6n2atQpiBNQ23VUT0pr7WY/y9akW5MebKVawnbderwrlCi8CPeoyhJFAVj1uwxj5ePFRFURj796qfQsrWyGMhGQ7CyEH2GGiZcD04dzTei3rqDA/v5XHbqGMSEhRCfZrlNZ2fo9FCCNhrDOF4fYkihUJ37y9AsDbAdsJ6B61pdZSvB2PC5M+hjCyvBNSWhMp5ASWoasE8pLtPgKE7BNgfH9uUISx3ZkVTal74Vq1tY6fNYE6myY9FMOvaKxBnJIEQfhaX5vbwl9WnlEu7s/MH1zTfn2SF+o2PV/HlVlVhemlo/jMKFmE2FCNa2Ars8khiKDdyJZs200Q1jNF5EoVCAK4ZY9fcKMAv+lTlpSXbhstFpWiWq+WByDSmvIL9EB7DNozG12fjXl4ZiOLiiQjGQ1Zilecwq4PsB7GNWdLPscQaZ1dBQo6+8ga0loQBJ6z5aWDV4trc8r87KF7qTUZxRN72D3+ZROLkJ18CUvbjEcJ0h4EUNdup+p8Py1lAHcWIcSjBGUNAqgc1Mx6DhWDfSidqS3nX9rDLeoorQ1Ia25L91BprcBk42GnwmAFUgu69UrWIhISsa/bOdFWpxrzEUT8zW/lLRd2rTo1iKrQAQ05YPdQtVso8/fnlIcW41g4xFunrBLSrUCVvL6WOBJRzQG/VbEH2dKxB6FQGo1CvjUZ5W2eTscwkaCWqhuHoEPa6m7RVSCOn6T1IR4e54K01noiR+Kq2tCJr7WiChL6T2FfD9tMurGXmVpjPt/N8bC1/Y8N0CZhLVPEhuLr6xQ3F3dQkWR0pKLoaXa2IaTXn93wHpqkh1uLmoKDDZCv9dW5LV5Mcgs69Fb5PsaamDIMMFaZKCGMdBgne6VxD2GMdzSWHF6tqwCNRYjDIhD3A/Zxc8pbwT2S1lW2KUSEGJHWxaz6+WlIa6b+YnOe3bbniiTzDantgsKPfwb4hTerWuusQO/Twla+zrN9t1DmVhOmHoZdTkhrfU9igHkeUoujHaXo9fltFCsS2lNRaysMCxBpfWtxx/GcI2k6FvxUWg+3JxENiciXJNtFXlJ2URq9JUYVwuvw99m6HgDYyavjwg5IRDDW2YRoWAR6jgJiGMiuGedzECQJWFRstyz8kwVBwHsUtTXBacHULRwdOJWD27C4ggPNHnMSOGmtKK0V0no943x/ZelfTzj2UeDkjwDv+1VHv/+ARTgYKa2P2OnMokJZbRgj+VnrBNF6LWQCWivBVsfPpTXfL3uQUkXC+OIOPl95GrIgAjNvqPOsHqrar0dwYogKdd6vh4j4Yx7teM6MuAtjVIVZ/pHWyWiYk5lOu0uo2NudjvlWiCeMuFAeEhHqxc8a8EfIB6hFtarQ4p6j7KsdpbXWokgDJ/PwlMYaxG8+gtZfN+/T+CJ1nGjmwXgLkGBkL7dZ06KGtAZYZz3gj2VpuSLxwscxh52ztSCLkLMmFiE0R3elY2j20BlgB049rTlp7bs633kximzAggphBBghHgmxzA6jffmtJZtnatrDLV6pFixsLwAr4wAEYM+7qp5C891apujZDtMKbw/S2gSjvD3IPcPvdzqtSlbYV9gFUYUmNMdVU34rm4Vxn6q9RhjrSiEkCtjOl3mVzgpXZjchy+ym7057q4jTgfXOsrfK5pSRzxYRW5v36xVKtHmvadccc6mataWwc4GWRAQphYh3qraWJFn1ufKBHKBx6NSHV5Zl/hw+SfcoFiFOfK01qkwqKLkpeHDLFJ9CwJLRML+n3aqtKYTxiAv/s66mGNIxFpoUhI+V3RBGQm9LHGOdKUgyeNu5G5QrEk/NrvN9tLIIIaJUCU+jsetFaU3zlF8dOAd7nBdUgWDaAFVVnTu7Jnpv9mnfGyJCqIBQi6VrjBRo6gGa+/i3q0KCbb43i9t5lCoyIiHBvkdnog1oG7H3WAWtySgniqdqbNBIeckS1k0ODnSANApGAyxDGAlOfK0phPGRYeeqx1oMtCbQkYqiLMm2ku21mNvMIV+SEA2J/HP2A+GQyO1y7FqEOFJaA8B7fw343v8EPPMPLB+a0CjSnKiBaK08QGtlJK4SFFa+1uv3gOIuEE4AHfstX4t8rQFGYLQE5PNYC0ekdaIVKxFmkXMiZOBRahc19iDNiTCSyv7KqdqaK62tDsCxNPCRT1b76NsAEYoLW/m6w6csy86Cm6lQVmtJRONJE8JIsPLvtwMieE8NOSdnmRcsm9tXPHZjAmyvWCxLyMW71CDeK39u/IQaJRtXWs9sugoj14KI+BMu/awJFMZ4bW7LUdGFW2D63E3s1g5jNgA/a4IbXkIN9vNHab2dL2Mz607QIkkylpXzeW+LZq7h9iAm2S4WSmsnHS9BKWYB1eZudiPn2PJRq7SugplFiA5pvd+iSOgE91YzyJckpKIWeQM28IGjPQCA8/c3DOdBVZwWrMoaUD2tZ23a0gU1bkb4HsL++jTfAKV1SBT4fVV7RiDcWlTmXyvSuusQsxXLb6nhoQAw+U32te8k6wDUoCmm8aoPWG39tietaVDObmRdmdlnCmXuC+zXzXeY+1rbO1jlNUFz+wKwBwE0AZEWB/JbnCzynzwHmNqZPjO7RKQf1iAEWiSceFDqwbCS1zLIvhZ3gVyNEoHIrhrSeq8LX0xAGyLi72QoCIIaxujQ13phO49cqYJISLAXTmYBGodTa5k6laEZlrYL2MyWEBIFVVmn9bW2C37AOs0327ddFDzu+hxiB6hqZ/ektbsQRkAJTSKLkADCGN3MQ0/54Gt9d4UFTTXFwthTu/GjDfuKDmldyjFCFODEKR0+bi3uuD5wqiGMPpHW3G7HGekXhLXCSEcSgsAOV+sOuwW2Na2JVcpZIq3nL7FgwVpo7udaOCX06dDV35rwRQVvBiP/bzWE0WKupS6T7CpgFAa27oy0nrMRJMdDGD1agwBszjnhMoyRxu+ezhTCIX+3vU7DGGlTP2yXtE51AI/+pK4/qB64GsgBaX2b2zFp7iVuEWLha02F3Z6jLPTTAk+OdSAeYZ/BsQb5WQNa0trenua2yGxZ9pZNQsfsoEZpLQgCb7l36mvNldY+7/kILYkI77qqLc4vbuexoeypbIlraJ+rVVrvLALbcyykTcdKhua5tUzRsd0DwFrWaT/kRmmdioU5ce6H2roqGO3Ex9k3r3za2HKnhrQ+2JtGLCxiO1/25BdaFcLoUWk91J5AVzqGUkW2rQDPlyr8+v0WZnFfa4eFDttrpwtQQXJu0x4hWpFktcvZI2kdj4T4PezW13o9W0SxIjEba60tBwk3NqeBgsF6R4VxH+xBgiSte9JxxMIiypLsKFhalmXjAgMnrSdrn6Sqr1tV0cJ+l7Z0elDnmhaIHvejfS0JnBxsgSwDX7mhbxFCQiw/HAqsQIUlbWehETazRX6e8Du8c9hFMWp+K3jSGgA/r+qtE2u7BazuFiAINoqGoYh61tX6WtPapM3m0UC12wnW1/ptT1qzMJsQJFm1S3ACmhTbU1G0Jr237QPAISKIbbaFT6yooWN+ehNXXZNN1R9dM5HcQcCqBbEWl5QgJz9Ia1Vp7ZG0Vm7MOmIrkgBSSvurtq1WqqgVaJoQFOx1ST4GqRTgYYwOldYUKDna4Q850JWOobMpCll2pnAmUm5PZwrxiNL613OcfV2ySVqX8mqhoe9UVZCnUwTRmq76WrtbJNyGMNa/vr+k9UamyD3nnZDWT+9lFiGvefC11vo+1m38zJTWi1dZqnqqG2hhFkGjHUnEwiJypYojAkkLVWntz3ysJdKdqJvr/KN9QDwS4q3yTpV1dD09zTWtiV2HgEiSpdev3ql/ohlp3evsAEGktZ/KXSNwVXqNh7xtu4Boio9LQ4sQTlob24NoX8ue0noTgPcQRsIJlx6vQYxfgtMwRtrU+32gIlDbs5OAqVt6nX48jNFCaW0zhJEQj4Twjn1srj7pglh0C7pPV3cLtgrgF0qjAIDezE3zB1phVzn0p9XODpr3FmwUfrRohGqLxkDt/vy64pe6r6tJ3VOZgZPWmu4OsgbpPMgst2qQioU54Tbp4vB7dW6Ld2V2ujxLHffR15qC0Y4PtACHX2TdCOsT6vugxc5iXft1JCTy/ZkXX+t7KyyEMRkNeSaZBEHgFiFvTdnraru3koEks6JIUN7ETvdYtgKMXaIrHUMiEmIWYjaEP1NrGRTKEhKRkC/7iREP1hcAy0sCgM6mGCLac1yyHWhixbc62x+ACbUyy+zfRvYgGkK/bEHo075wNADSWhQFVyr9xe18vSCKYKS0zq4DJeWzIGEbVC5kas2d6FIL8rO2Zd1kAx+wsAihPCM/hVhGiEfsd5DRmOltjiMVsy6iOwH3bnbhaR2kPQigsSTSOUvR/m64PWkd/g2oe7kFZW8ny4Z+1rWvH0TntRZve9JaEARPCcJBpIwSQTy5lkGuaN0+xdumuoPxkQZUsmLcREW2mS1y1XkQNiUE+t12fFVlWeZK65N+KK2JePRIWk+ZVYD1whg3p4FyHgjF6vxMqeWeKpd2MbcZnFLArdLaTz9rwkEXPry6Sl1SWq/dZcpYKyxdZ0RkshNoGeQq/Xuru47byYJ4X/Y6VPhpUa5IPKjSLWmtKq39raySimGwLYEmB5uOJ8fa+fPXbPoS18K0hVbbGllL+HI/60d5BkM4JKrKZhcWIZIka3yb/Rk3+7qbIAosuMhu+/N2vsSLCH6nhLsdQ3e5Ar1mnQqFeYiarq+1DdLattI6wKJhLdTg01qltYNrMLMIkWXVa9VCaU0elwsWpPX8Zg6L23mERMFzSzpBDWN0p7QOIuWez8M2infZohoCHRxp7cx3sSLJvIhftV6SP/z8JeZbbQQewmjuZ63Fr37vUXzi/Qfwd55yZpXjBS3JCJrjbD2ZsVBb50sVvJZnRZ70uknomB2QPUhTD/9WnwultSTJDfHHVHNnqsez4yI3hUHuzLNWY8A0hJGgdpU439dcnmGvc9KFNQjBzzDGKh/pWBo49CH2gyufrn/wPf32azr30N/m5Tq8hjASKFT3vE1f69u8KOaPPZ4Wbs//QYp+GC9hX3lIYrIDvWlfPh+vwWi6ftYEM4sQsgJqHmDjXQc96TiiIREVSba0RzI9Z/sAGjtOCrwk7Nvblaov3lHBv5a0JpuFpl5mvaWgtzmOdCyMiiR7skQC1AKZV896AvlavzqxqutTzC0vG2APAmhsZSzEqUGq88keZH4zZ6vIIMuqij9o0npPp3EBhvgQ27xer8bXGmBnhp0FFiI/9KTuU7jS2uM4tsLbnrQGtGGMzidoam33c4Br1aF2VFu63pw+g9Tft5XwNT1oySJTf0yPOOigJWZhK4/lnQJCouC5rQ1gpKEgsBAct8SWLMvmFWA90npZqUp3HgDE6oWOlFqTqxnDz0YPs3b9DV1goFWthjuB6n3rfxFo3IGlAY3lQ9pJuqmHEdCyZO7JRqADVv9pQBDQ35JAMhpCqSI7qiZW+Xz7uMC7VegDjPwqlL35n3Evdp8XKachjISOphh/zuv33PlaExmmu/Hr3A8IIXYAJyKCQARpTbgUXY8bX2sWcumvD288EuJzll1ylronugMIXHEbxnjXrHPBKIyxsKuqjInY1uCQRmltx86FlNbktxckjOxB5pzkGhBprae03lkEyjnWtk+KbAOontbmB04iNI70NdtTd9gAKa0nVnaxW7BvF8X3WEEorTXFQ6vuBVLotCQigXk5O7UHmV7LoKio/Ia0BE7XIXZIKWwbh8fJsoa0tqe0BtgB9B+8d79v48Iuhm0qpeY3c7gmjQIAQlvTxpY6dsDtQVSldR/dQw48rVd3CyhWJIgCuL1IEOCdkDU2aDcW1AwMW4i3qH8zkVlzmj2VAdSsA+f7isskcPGg4PcrjLFckfi6z/cTJ3+Yfb32WaBSQwSRZ2iNko06TL0orf0KYSQ8NspI9fPTG7bWyiBCGAmjvC3eqad1cKIfwFloG+9w9smW041FlBYkXuvRy+ow6zhcNQ9hBJjCeVDZM5nNw7lihc+PQZHWoy6U1je4n7XOPGiktNbxswZYcYPEUF4sQiRJxo15mmv8UVrv627C3q4UShUZ37i5XPWziiTzTphGKK0BcLtRq4JzUCGMgNpBIcn2uBEWtsxsdnpagnFRIJgV727X5ntZoVZpTSrr4Serii5Vr9/5UGltG17ScmmA++3Lc8iBr/VtrrQO7uYf7UjxVnWjhYJ7agXkZ02gjcud5V3LDQ+prA/2pJGIek94TkRDnOR1q7Ze2SkgW6xAFAxaw1uVQ7+WtCYf3O5DdQ/vb00gFhZRrEh8I2WFckXiVepA7EHcKq2X/V/I3Citb+r5wwmCM1/r+Uvsa/8pAGyzpRIU9q+lyufbRysBupaZ9ayjQBygOoTRrf+Z1tPaTZCeEaiVyY2vvhdf65LmkKnrhxmOAR372L9rix5zitJ6sJq0ttPhYgQiDca6/PXhdZq5EKS1gltVHbWu65PWymdQS1ovXmUFq+YBIN1T97SRjhSiIRHZYsXWhpST1g1QWnv2tAZU5aMeaU0ejC1DQNjcJq3fpnUU97MebrW+NpvoSsfQ3xKHLNsnlWTZ/44FLagQvpUrYXXX3JudQnJshzC6ABGzdoKuAK0Kp6l6LQhFVMWNka/11ixrCRfDKpnxHQy7fqozGzlsownzotIKr/V2dILCDss3AarmnH6b3QpazCqP7W2OV7fs+wwjn1VSWjtqPydf29VbrMBhQ2nttpAJsNB2wJ2fNeHoQAsEgQlmVl0KWwBm21aXjzH2HBNOZFeBiW+oDzZpvyYC/vr8tmsLAcoA8EP4A7AxkIiEsJUr2RJNBHnGpfluZaeAjM1CpizLgSqtAS2Zbj2OaS/mNYSRMOyh+xwAFpUOEOdKa3M/a359NubhaSXsriUR4UHUfsPN+2QamMlJ6+nqTBUD0hpQuwW9hDHeX89it1BGLCz6mjtDausvX6/2tZ7bYErjWFgM3KuZYHftDsI9gSAIgqaTzXrMkLCjqymGWNg7f2UGHv66nq3j1W46VVr3HAUgALuLwM6SZm3S97Nmr//Q09o2VKW1C3uQFSKt/R3g5Ak9bsPX+o6en6DPCImCZav6zUWTCqKPGO1I2iYHeAijjwff/R59rWlSHGxLIhrWuYVoYdqaUb/HF/N60jokCpyUsKuaXdopoCLJiIQE3z3iAA+e1gHYYKgkmz0f3lJF4urQugJMj0Ja2/G1poApjSqINhi1rbNmIMJkpCPl62GzqymGdDwMSXZe3SR/SjchjITRjhQEAdjJly3JGie4ya1dnM9D3Nf6nnNf6ztLuyiUJaTjYd4GVge+YdeoTDJrqrKiv/owbjdLQPd6iGjzeV1waoMxsRKcomKPpsvECe6YkZBEWi9dY770BLIG0VFZA8w/lKwe7Lw31KboZyHKCLQh3cyWsKEJrXTkl9epHCT17EFsWoNoX2txO2/aGXRByaLwI4RRCyKkrthUHq7sFrCdL0MUglFsxSMhXjSwKoRT6vxwQNYggNrCuridt1XM1PWzJlj5WhOZ23XYdlDkgwRvMbY4+FIxaC6uFHpoL+AUO8phP5quapcnpbVVe7wWjroqPIDm1KXtArZyTA28lS1xku9on4M9gzaMcWNKKXBE1H2YDowKdFZY3sljfisPQQCOe7AjaoqF+TV4sQih51YJA0Jh4PgPsn9rLULW7rKAylCMqdk0GOlIoiURQbEsORJuELQhjH7ZNEVCIrdgOWfDIoQK8EGccbWkpt198Ea2hKxi3alLzPoAJx6vxBP4JRgbcWgRVQual3Q7OnqU4uSSDmlNBXETpTVgj4Cc0nQzB2Wb6kZpTXv5Q3q5X80DbH6TSsD2vPp9Iq3b6u2w9vd44yMA1RrkUF+zrwIXIq1fvrVctZeY0DgUBB1CThhUxoyVsI86dAKzX3Pgaz3XgAwKQn9rHJGQgGJZwsK2uq+QJJkHbdueX6Ip9R6evwBMvcL+beBnDag++ss79jJD3OK7hLR2J0uXZZnbg/jty2OXrMhrlM9B2oOwa1KIdIONz009H+AAENaQA1YtMZy09sHPmkAkkFultaXPFqUDV9mDKCSXDmkNqIpBu77Ws8qY6W9NeE4K1gMdwhe385ZhGQSt962fRaD9PcyHdz1TxIoN5cvkagbFCrO+qCNzepUwRiuldTGrfmZa0trFBoMIdD8r4ACr+u51GAJGcKWaqkE8or6/93wKY3S1wGrw+J52iAIrRi46IAQA4OrcJgDgmFn6tl5rJCl6O/YDidaqh1PxcnYjp+sLZwbeUuuzOslp50KQSmtSQ0ytZW1bI2WLZb4Z1L2m1mGmZpPKTF1NMPGzJhy02aqZL1WwtM3mokYEMSaiIa7OpKJprljhxSJbam9SP23er/f0txnCCDC1c1gUUJFkQ1/0fKmC6wpp41cII4EIqcs2fa1p/A61J+0FyLkAD2O0mAfpcByk0ro9FUVTLAxZtj7gAepY1933casdA6W1wxDGBw27ai0iaDdbj7JvUNeVU5CNVE1nB93LdsJMCY0KdErHI/z6SFBDLfGDbQlntjZdmkIZqax7j5kWOKite3I146iD64ri+byvq8lRFoYeSJF83QNpTZ0gx2qFASd+iH29+QWmxAdq2q+rP19BUDMB3FiE3FvZRa7EQhjJesUPnBlhFiFWYYy5onrGDcIeBHDua03zYnc6FtiaoNqDmF/TVq7E722/BGPEibglkBbNPK3pDJtZBjI1HY0+Kq2DVMwSRrmntb39Z75U4WedI3pKazGkita0FiEmSusDDuxSjUAhjMd8CmEkHB9oQW9zHJlipap7NQhxmhVoj2tWcNZatwZhDwI4Kwg1ys8aYLwavUdaX+m5zRwyRdbp7SjQlDJKzv0eC7ZPtKmddzpoSUbQ6rB46AbfFaQ1kYezG84SWJd3CsgoNg9+q6WoCmelDp1Y2YUkA63JCLpcpl3bviZSrOoQ6ZIkcxLjsF4F0WeQb94tk4m6XJF4W9tpH0nrfS6JPoKl0X+tp7VUUSvQpNSsAU3+dpXWamtbMJNhV1OMh2Usbtsj/6hroTsd89UTPR5RfZftEG3a4ksd+ciV1tfrw/S0WLoGyBUg1V3lRcn9Hh1sMILw+SY4HTcAW9jJHsRtCCOBbJXctPLqQbvAulFFtiQi3LfxtXvOLEKumoUwEvRaIw38rAGgNRnlG3+nKimanw74XMykYsCd5V1bBakJM/9oj+hvTSAaElEsS7YJnHsrGcgyI+c69NZMQdC3CLFDWmu6OsxAh8xUNBRY62ottGSO9hqaYmE0J2yQNMkOtvGEzJR9WjhQWodEgXtdGnXiXJ3bQlmS0ZWO+b5GnXSotA6qaKiFWnS2UForm/kgCx2CoNpQ2Tk8mIb00L2ycBmo6JAfLkIYHyScktalHuXvcq20rvezBlSl9Xa+bNvSgJTWjVBt7a8JY+R2Yk7tC3h3x02Nn7WxNQjAyImQKCBbVAuDdkCErh+B7cd9CGMk0vr4YM171v8Isxkr54Dxz7PvGViDELivtSLicQLK6fArhJHw6Ki9MMaJlV3IMtCWjKCzydx6yi1GHXo4B31+AlTSemY9Z0qI0vzb3xL3LeegJRlBS4L9LjuK0FqYelpHU6oNhla8UcyqZ95Oc9LaTscLV1oH2JXU16KoUyuSrXPunSXG17QlI8Ydznq+1jZI66m1LAplZzaPBPU8508nBUEUBXzgKCu4nr2mWoRMBJDRZAVSOM9tGt9PS9sF5EoVhEQhMNu+YQde9Zy0Drg7ijDKRUDqeZzml71dTc46vYmgvnOWfd3zLCCaP99tKK4TfFeQ1l3pGJJRZo5ulSyqBZFsQ+1J3/1m9nU3ISQK2MqVTCdDrddiUC0wBC2RXovZjRyyxQqiYTHQRYLAq4sm5MDtJaYQSMfCvlb09hkEzdiFJWndMsi+FraB3CZLDi7nWesfLWg1IOW5XfIxaNWNKAroa1WICZu+1nRgD6L6yseuDbsdKsro2kt0HmDtW4WtaiV8LbifNQthJJA9yL2VjG0FepBV6X0Oxw1Aqt8yIiGB/z1uwf0nfVJa33S7wGrAfa3vOrMI4b6PpqQ1Ka1vApLy+XM/6zO6TyHvOyNbJj1IkqxROPtbRBxqSyIZDaFYliz9xwplVSUVxPgNiQI/3NltB7cVqldLWue3gbU77N+KR70eDnEVuvlndZ+HMCYDX7cJtf7fWj9rW9cgCMYWIeRp3WattAbUdceo0HBhWvWz9vv9oftzZj2HdY1VihHuBNgpQNCGMZqB7jdHihcXsKsGypcqXP2tq7Tu2A9EmxjBtnqr/ucLb0+l9YyO76MWRKbEhhTSnqwtnGKXSOveqm83xcJIx1mhaWHL3v6qkQfgAzUdJxTy5ZgUIVXm5gww/W32b53irhbRsMhDt+45yDqgzouTPlhg0N9JCkan0Fpy1PlICwJw4uPs31c+zYpBk3/L/n/sWd3fR4U6N0prIt6PD7Q6fq4ZHhlugyCwOcao4waoDmEMaq10SpbMBexnDQB9LUpBviKZ3uN++1kTnARBaiHLskZpbTDXdCsdKFrSeu0OAJkVxlOdpq9hp3gYtGIWqFan2hk745rPynAs15LWsqwhrevtQXqaY0jHwizc0IX4R5ZltavDpxBGLcgi5KvjS5wsVoVYjVNasywHAaWKsZCO1ouhtoS+dasPUMeu9WfF7UECDE7WQu+ev+U0hJFQK0QwsQYhuLHbcYrvCtJaEARXDD8N8CDaT2LhEK9CmRFtQaYq14KU1mTarwVNxvu7m3z1RDLCgRolhx7IGuTEkEm7vgvs03j2OW3ZB9TF1PDgGU2x9nSALVbLN9m/Ow+w9iEd0FiZsJmYrhIWwW26nPpaB6koPthjTwEJWASKhqPqQcrM19pAlTnQmkAiEkKxItlWMATpCayOG/uHOzpMHehJe17YadNyz+a4tcIt7qvvfj5042tdLEvcV9A0rKh9Dys+lXPA5hTbkHKltb6CjP6WGzYKLoS5TVZEjIRUUtcviKLA51+r+2laaZtsioXR0xxMJ5BTD1NbdiW1pDXZGLQMmx6oDvSqRSmzri2yZwpy/q0FtXbXKq0dqcX0whhlGVizr7QGmH8eYExan+ektb/WIADrpnDiORukvQ3BDmmt7Sbw+56uxYhN38V7KxlUJBktCQPlmCiqHvC1FiG7y8DOPADB1KP4Own9rQmIAlAoS6ZWY6TE7OnpV0kIN2GMpLRuqg9+7W+hwo+9TrZG2YMA2rD0ansQx51ZqQ5lLyyreyqTEEaC0zVBlmXeeeGH0vqoQv7MbdorjNVictXCkuP4x9jXe98Ebn+RiSjiLYZ5CycU/+g7y7t1ZzcrGCq+PaIlEcFBZZycnza2CKEz3sEAz7ijndQWb1dp7SDA2CVCooChdvb7zYhj7mftc4czJ9ccEkjb+TL3++7VU1oDmo7D6+r3VsjP+mCV0EcPpLTezJa4b34tJpXPck/AIjon5D7ZvZoWGDhprQgBsutASZnHSNCmgSAIqu2kg6wkwvxWHhvZEsKafb2feHxPO1oSEaxlijinWAGRSGnMR7shK4REgXcZGSn0LQWFPoC4xvvrWUv7qvkGeloDaleCdt3knXROz9S1ViB7jUMYCQ+V1g6ghjE6V1qPBdQ2yn2tTVRbQaYq16I9FeXEQ22r+q0G+VkTaANzd8W4Rf3SDDv4nvSQBK6H5niEvw9OLUIqksxb0EwXU61FyIpSje7W97MG1Ml/PVO0tUl2RVg4BCet7SqtA1QUq10C1sqXm1aeyL3KAdvM15qT1qeqvi2KAico7IQxbuVKXIXid9groFHoL2dMlWNa3PDJGgQA9joMELWClxBGwpmRNoRFAbMbOcvALcLtpR0UKxKa42Hz1n0xpPr1LY8zW4XcBhCKAj3HdZ/CldY2xi6B5qWxTveKczMcsulrTdexN8BOIG4xY3MM0cbe1OubiJH1CXZoMLifa9HfEkc6FkZZkk1VfjPKnEiH0kZA7Wpg+xYi1hyRWHpK69wGI00Aw06gWpgFycmyzEMYH/U5hJFAFj5XbLTLN4S07mL30+J2HjsGhfDZjSwkGUhGQ4FbwdkNC+J+1mYqyAGDMEZSWXfsA2KNO7x6QSQk8gOk0XuTL1WwqhDaQ21JlUh042vNPa376n5EIWd2sxfmGmBpQNCKSvKlCu9WcJWBoc1xiaQsQ9oAdU2YtFkMv7+exWa2hGhI9MUXuDnurDBWCx7C2GdgydG+Bxh6EoAMfPGX2Pf2vMtQ1NKdjqO/JQ5ZVkloOzBVfPsAmt/PTRl3IdzmQa/BzRHOPa2DV1oDKolk1tHGg/188rOufe1pG4pQLZYUFWtrMoJE1KADXS+QnDpxuqzv76ZYGB0pZhWjt0ffyZf4HEwFiaAwYuMzIty0E5hZq7TenGZf032GXv403zqxnSTQfLC/Jx2IP3skJOK9h7sBAGevL2ErW+JZKkGcac1g5WtN64Wf3v21GFAK3/mSxDO8jDCnFKQbRlp31s+Dt90qrZPtTOQDsDFt42zAldYOeFin+O4hrXU+LCsEXZUhsmLcRGHH7UEaoLQGNL7WNQQKb1HyeeE0wmCbolgtS4Y+ZEGEMBLIFuGuw8rm/GYOxbKESEgwb9NsHWJft2Y04RTGpHVCExpohwB0RVg4BP199pXWwSmK7frwbudthJpwX+ur+j8vZtQNmI7yZT9X1VlvMOiz7G2O++rzTRhuTyIsCsiVKlWJwWa47rbVVwd0uJzZyDnKEzCCqUreJlKxMJ8ztOEhZuAttIMt1uQstwi5oaoPe08wFb8OKCPg1uKO7cICt8AI6KDHQ3kt1N+ctA5wc8rJWIdKa1Nrm2S7qhqev2ibtBYEgSsSzAh92jQ3IoSRsEfjVydJsrsWZyKMVu+o36MQxnQfELX3u/pNunBmN3JY3S0gEhK4v7zfIALGKoxxK6eGAwfZytqSjKBLUSobdUtNa/ysg7aUoSR3q/3wLbMQRgJ1G83XKK0Xyc/67WENQrBSIJIKM01e8TRnuPG13lF8QGvsQQBNt4INe5CtXAk7isK2IZ7Wyr2yslPAm5PrqEgy2pIR/WA2K2hD2fpPGRKzWjhVWtNZ4XB/s29t4TR3OSGJCVdnt6t+hy4okHF7ln21aL8+6cLXekIJYUz5HMJIOKP4Wr9l4mvdiG5iskOa38ojX7L2Bebnp4ALQLx4aDDXBJkl5cR7VwsqRBuqrIHqQHJSm9I518LPmmDma02kV2eTv/lIeuBKawuiTZblKnsQQ1CYdS1preNnTXAihKoFnef8DmHUgixCzl5f5GHTfS1xpDwG3joFHzMGQjoqPARpKRMNq4Vvs3tLW/xuRHcUoPH2X2P2Z6WKxDkIVyp8sgixYQ0CVKvQg8J3D2nt0BcT0LQ4BDTAVR9efYVdvlThh4r9AVah9a+p+kB+s8FKa1HUtsTUkwM7+RJXd5wabvX99Xk7r0OFKE2Kw+1J81ATrdKaqtEGIYyEvTbDnCRJ5m0ngwGSJk7sQUoViY/lIMgBuz685JHeZxZqYqW0XrwKyBIjcZrrFVK1IUVm4D7fAVimAKwKTgU7q3FDuOaj0rqnOYZUNISKJNvy+DJDoVzh87fXeehp8rWesGcR4sj3UasysfCzBpjiJRYWkS1WbC/mttTEHkBK9ltL5urvIEMYCbUBg2YolCu8yGl5Tdwi5IKtEEbCQTuktUJsBRX2oofBtgTCooB8ScLSTp6Ta44O3qSCWrvLAoIBRyGMhAETexCyBjnS3xKI8gdQCRyrMEYqcPQ0x9Ac8OHXKuCZ1segrUEAjXfzRs60UHbbTusoBectXgPKGmXR2yyEkWDlpzqjIbQEQQhMaU1+sQs27EGoQNWeiiIZDZ4oSMXCfP/3V5fmALAit6tiSxVpbT3/Amoh0+55jsIGT/ngZ004rliEXLUojOmB9limpPXRj7B8FcKYefs1J60d+Fpf5SGMLb6GMBLOjLQDAK7PbSFXrCeLM4UyJ4iDsC4gtKeiSCsEmlV3nSzLDbEHAayV1vfXs8iVKogFkCVlN9egFotKEa3XrEDVsU/JBtoGttn8wC3HuuyR1mbzMHW57QlYZQ3YU8MDrItqM1tCSNNtqwvyrc6usSwVkxBGwoEaOyYnuD5nY67xiHft70I8ImJuM4eXrswDaLzKGlA7G43ucRK+BGH5q4VqKWM8ZqiDKhEJobVBYe0DreyMUCizYNF7KxmUKsze0RVx/tQvAqPvZF9tgHjY+a2creKhG3wXkdakLLE3QRfLEt+cBqEMBVTV8r3VjO4HOLHCkmhbk5HA20Vrr0mrtM6XKjyp129fLTPQRH1rsf6Qd3V2C7LMbsLutP8m9rToOG3HmeLqfIsxQwvXxpRmMTdWWgP2/YmXdwooVWSERAE9RgnGPoArrW3Yg8ysZ1GqyEhEQugzq9C7hF0fXlvFF7Jx2JgECjq/iwguA39BIhLv2CCJg1SfE5z4Wq/uFrC0XYAg+BP8IggCJx3t+rEb4d5KBmVJRjoedqfo0uBJhbR+bWLN0ncMUA93J+wcerUqE+5nbRwuFQ6JfOyO2wxjvG1HTewBpLSeWc+ZemRya4UAxy+p6uY2rTc6U6vMYzttx2ObPpOJr6vErME9rYUd6xRSTg01UGkdDolcPTW5ktG0ODvYjLYMA+E4UCmoSiCHIYyAqvbUI60v3Gek9aMB+FkTjvY3QxTYWrhk0mEyEfB9pIWVrzWF0zQi6Lq/NY6wKKCoHF6MwNdLM0KpbZSFa0klYEnjYfo2C2EkmCn8AFWFye9t+vs2Jp2FMcqy6mmto7SmNc6O0rqRftYEsnM4e439Da6sQYBqEsuGnzWgFjLvr2dRshF4TerjEz5aCRIJ5NQeRJJkHlxpasmRbAf2f4D9u2XIsmhIe5PLM/av52rAhNZgWwLd6RjKkqxLptNc2NkURXtKvxPNDwiCgJFOeyTtZraEjEKwB30/Wfkl0zn8QE/a9yypUc2+ys49RFjgIYwme/BwFOjcz/69dIOFia5NsP+3Yf8DmJPWpLQO0puYoM1/MDsrkNBvb1fKvBgfbwaS7PyBjSlHpPXUWhaFsjOyz08RkhES0RCePdAFAPjTN2cABHumNYKZPUi5IvF9edDjZrjdWlGsDU5uVFh7OCTyfcvUWoZ30h3ocWnvOPIU8FMvqfe6Bah4KMtqx5rf+K4hrWmQzm5kbbWp319nATSpaEg/gMYH9DTH0JqMoCLJugcZtc05OL/QWmiV1jRB31li5Hl7Ktow8hxQD0q3daqLl5QNUBDWIIAz4lGLe5y0tiArWhR7kOlXgXKeEQUWnkC0CFiRfzQZ9LXEAw3NHGxlf+PcZs6S+Jvg/vApX0MztbBDJtEm0JS0TnWoyqelG/U/J0WVgSqINhgTK7s8TdkIQfp8E9RxYz2WqZVsT0fKt9Yu8mP3GsaotQbxOh8+MtyGaFjE8k7B8n4qlCt83NjyfSSl9eptlbgxIa0BtfVz3EaQqCzLuBuwD2RbKsrXPaP7SZLkhqSEd6SiSMfZRsdKia61TbEcI/SZ3H+VfW0bZUSBBXgx1aCguZUrYTvPiP5G+MtqQTkKNxd3uO2Fo4O3KAIdygaUiqlcae2ctN7IluoUdkRaPzLSav+6HCIZDfPPyaxd/m4DOgUIVqQ1je3hBiitwyGRF52N7qkdjZWW6TwjCBqLEKWgm9tUix21gT3f4bBSWtepMJPtqgjBSRhjYUcN4NILYjTxha+FGujkvyDACHR/EcHnmhTRijX67ZHWPek4EpEQypLMiwhGKFckTtz4EcJIIKJ3bjOHDQdhjJNrGewWyohHRGtbrSd+HhBCwMkftgyvOz7QAkFg17Ni4aVKINLaVjHeBQRBwGOjbE09r2MRwq1BGlA0tOtNTHNeVzoWWCcQQavi1TtDjdvxSHaJ7nQM8YiIiqYz1w6oCNzbbLGv4B2HN9haIJWASFI9+1rAbB6eVJTWow0grQfbkhAFIFusmIbz3nDiPa71tbZBWvc0x5COh1GRZEduAcs7eV9FSGYgi5CcIix5IKQ17yCrHzOzGzmUJRnxiGhubeMD7IR3zjY4hJEwovGVvsX5kMbY/mqLh0H5Wn/XkNZd6RiS0RAkWX9A14IIlj1dqcAIY0EQ+GKkpw5thNdXLViwl4AdTdsW+TT5QRY5AdmD3NZ5by4pQU6BkdbKez63mUO2aD+N277SWlmg8pvsa+d+Sy8/u+SjK4WdC/S2xCEIQKEs8eAFIzSCnLXjw8v94awmaTNfawsrgYG2BOIREcWyZEmycXuQBpDWdkJFryuHO9eqKR1Qm5jdID0j+GlRFI+EcEYJCXrNwtf69uIuShUZrcmIvXuqZRCIpgGpzBSriTZLlRQP5bWhtF7YyiNTrCAsCvwgFgQOKZtcI9KatXgx//6RABXFgiDUhQwawZHyu/c4IGoKMzZb06mYOruhr0InlUdHKtpwTz8qzn/7LhvTiUjIuYKNLELqSGv79iDN8QialL9dqxTNFst8fn4kQKU1oAljNGnf1waJBg0irY3WbyJTGqG0Bqy9m+9orFNakxZjiMhG8rVeVNbNlmFbhaDvJFiS1us6XvHka+3EImRX8bOONesGVZKSccGGKEBVWjeus6P2XOKatE73As/8Q9ZibDPoVRQFTlhNmgTiAhQWKSEdC/vaFt4cj/B2Zydqa/LAPtzXbC0qGXsW+KX7wLv/ieXvTccjfN2zskUCGJlPiu8grQPUMMb1up/RHBNkCCNh1KaHc6OsQQB2Vggpll56oW08hDEAwlEQBD7XTTmwCOGe1i0WAjatTR75WXfsY4VxGzDreJlUrjdomweAeRRTgdds7Nzk3uN2SGuNr7UN0loQBC6kc+JrTSKksU7/REhGeM+h7iqLoQdhD0LjeWm7UNeVSWT/aEdwwjnCiMUeAtAorRtYaAa0rhMZ7mIQRFHMCE6CTd0gENJ6amoKP/uzP4s9e/YgkUhg7969+NVf/VUUi/ar1U4hCIKjBGHV+ybYxZQHH+qQFUH7luohGhaxr7uaSL/VYD9rAr3e5GqmSh0vy7IawhiAnzXAVOXtqShk2ZlClE+MVkrr1ppqs4U1CKD6Hs+sZ03b5Bt1gImGRfQo1ixWvtaNIGetfHhlWbZPfBr5Whd2VDLHILQtJAr87zSzl9GGjAap9ONe6DbGsZ8hjAQKY7QbpGcEv6vCdn2tr8xtAiAlk43NjiBU+9MPPGqpkqKNbm0Arh6omDnamfItVEoPaueC/jUR4TfakQq0owOwH7x1hwcX27ifIgmg56j6/zZJa60K/bbO/U2H3kZagxCobf71e2xMu2o9pKCkFSKtFcWsA6U1oAmS06wNl2e2UJFk9LXEA1eYHFesAMw8XrkyvwGqIJrjp9cydS2+FUnmZGgjPK21rzNtkDXA/aztiCa40voS+7pI1iBvL5U1oB58l3cKuj68/P7WklpkK+QkjJH8rHVU1oDqaZ0pVnjnhhHmGhQcp4WWaIxHRG9Bfu//58Dz/9pyndTCbiGTCNzjgy2+kxVuLEKu8XwMm3usWJNtoo/sT+yEMU6sZHgIY5DkH4Uxnp/eqPPPp/XT1DPfJ9glS2bdBBi7RCQk8k4oPUL0ZkAhjARuY+CAQFrkpLWV0lrZWy1fV4PrbfpZA+r6NLuRQ7lSff6fXGmc0hrQKOJN9p9qgcHGZ8WV1pMa0nrE9Cm0Dt91YFnaCD9rQmsyiifH1AL1g1BatyUjSEWZ+K+2A4dzeg0g04c77JPW/Vb3kc/Q5vsRVxJknoDR6zv10reLQE6iN2/ehCRJ+K3f+i1cv34d/+E//Ad88pOfxD/5J9bVZC9QPyw7SutgQxgJRxSyYlyHGFCr0I0liw+T+luZhIlIsVSn+oze5jjS8TDKNS0xC1t5LO8UEBIFHPORXKsF97W2GX5Qqqg+6JbFjliaKTAJNkjrribWIiTJFm0nDVYKaF/TCKqNQIDKUAsf3vmtPHbyZYQ1pLIhuNK6hrRevApABpoHgKZuw6erwRnGVXGtBZGl/64HUAvqyk4BW7mS6WNV5Y1/9/pen5TWWnsQP/CUQlq/fm/NNIyM/KxtHzIBoFtzP1tYgwDqoWRmPYedvPlndLdB6qSDFh7xXNXcgKIqFT6sVHV3eaHX5hjRfjY2/KwJZmGMM+s1nrcNBJH71LLvag3gSutbrEiXWWb/78DTGtD3tebWIAGrrAHg5KBKKOkpVfOlCj/YNGIMd6djSMfY+l3bGrmwlUOxwroW+hp0iBm2COIi+xtTP2sCeREvjwPFrCaE8e3lZw2wDBkKbdPb1+iSWm6U1iZ+1gDzCW1TApoWLHytZx+Ap7X2njnU2xxIkJ8ZaK6zKoZT0cpPP2sC7QmuOSKtlT1WAGeXU0OKr7WNcEgi2o8O+E/ma3G4rxmJSAjb+XLdfthRYcwj7AYP0v3dqHuJt+vXEMe7hTInvWxZTriAGwLJlqc1oAo3Vm6zdQFQC+I20NMcRzQkoizJVRZJG1nVfq3RXUlG71O+VOHnmyO2lNaj7OvsOaCUBSCwDk0TUGeLG6V1kDyJFmQRkoyGArfg0IMgCIYWIZM8vLMRPujsNdYzRcPz3LwSsNxwexDl77+xsM3PKo0UpL4tldYvvPACfu/3fg8f+MAHMDY2hu/7vu/DP/7H/xh/8Rd/EcTLcVBVzpbSmuxBAh7gVJUb13hIA2wSpOu0pRoL4JoetNJaEARd/1BSWR/qTSMRDc5zbL+FB2UtZtZZCFgiYpOE1LYDaZWZBhAEwbLFGGicPQigbuzMwhhlWW5I4GBbKsrfdz0yiRSje7uarBWqvUoY49INQNJ44FtYgxDsBHneXVbek4A969PxCH9fzIjj3UKZF4f8VFrTHLqRLWHdgfejFlu5EuaVjatfB5wTg61IRkPYyJZMwztd+T5SGCMADJyxfHhrMsoPAWae7IDagbMvYB9I7TqgR/o14p4m7LGhqitXJD5+bZOQVaS1fYLtYI8xaU0HzaEG+1kD9cVSVwdvCkpava2qrBPtQKLV0a9RSWv1wHlB8TU9HVCHlBaHepsRDYnYzJb45lyLiZVdyErQdWdTcCFgBEEQeNdL7Z5iWhPc2SjyzyosyJEKMt3HFMNyhRV236YhjED1wbf2vckUylhT1rABPaX1xiTz87YDTlr3GT6EChgLm+a+1nMN3PMRktEwhtrZ6wUZ8mUE3n1jobS+pAQTEqHrJ447VFrLssz9tYNQP5Jn9+XZTUtLGceKb5eIhERu43huWrUI2clr9nQN8LS2GzzYSNEPUN2urwXtLXqaY4GFVKrdNvZI61yxwoUvvVakdesIEEkxi7y7X2Xfo4K4DYREgX8GWosQIh/7W+KBe44TRi2INsr9aktG7GWgEWlNNlrpPiBs/jwSqehlfBmBhzD6KEIyw4sn+rGvuwkffWQwcAsOI9DaPbteS1rbtG71AU2xMDqUe9ao0DH/gDytKfeGeKKudHDzix7U+e5tpLTWw9bWFtrbzb3vCoUCtre3q/5zAq0s3gr0mKAP5Pu70xAFVpHRmvzfW8lAkoGWRKSh4YeAxl91cRsrOwWs7hYhCI1XfAPqa2p9rYm09jNURQ88jNFmZZN7Unba9EHXktY2lNaAxtfahEhvZKsovYaZPch6poitXAmC0IAiUK+xzQJ5qdoqvrTvZeGYpYwaKAWopLWFKvOAjap4I3y+CXZ8ram9ra8l7usiloyG0a9scN2qrYlA6W+JoyUR8eW6IiERj+9ha86rBr7W+VKFHx4cHTKr7EHshUupnuzm6xptWoO2jdrX3YSQKGArV8LSdr3f4kQDldZ27EHur2dRrEiIR0T7ZO3oO4FwAhh6whEpa6q0foD2ID3NMSQ0BzlXLc4d+wBBBPJbwMwb7HsO/KwJdM/T5lyWZVxU1m7yOQ0S0bDIOxj0LEK0/ueNyuowCmOkDXyQ3vC1sAoLIr9DW0prQVB9rae/rbaDvw1Ja8DY15r2Oc3xcPU6lGxX93N2wxg5aa1vDwJoLHZMlNb5UgWruy5CV33ASUW93Ij7uRZkhWS2JuSKFb53CEJpfVTZE8xu2AtjnF7LYidfRjQsBiJG0hbqrPJUuG1KA6wDHiOLkCk1jJFU193pGFqS/uzpzKANHjQT2TRS9ANoldbVn9e4k2A/lxh2YJkKAItKCGMyGuLdKIYQRbXjMKd87g6U1gB0i4fUKb+ngZ7JVmsldcof7mu2t5fgVmtKYcnEz5pA3YPTa9k6ezE9bGmK9Uf7GqO0bk9F8dVPPIt/+f3HGvJ6ehhq01+7JxskRCWYWYTIssz3Eo0OaycffUIj/awBre1Ptsr21y80hLS+e/cu/vN//s/4+Z//edPH/fqv/zpaWlr4f0ND9lJoCVbVMsJWtsTVFEEP8EQ0xCvANzUBcmRJcaCncQcqAinsplYznCAe7UgFqmo2wkGqLmqV1gGHMBJIyWhXaa2q820ePFuUhSoctx1AYxXGKEkybxUdaoAnmx2lNSkyB9sSgVfGD5mQSY46BkJhlXikajigtv9aKK33axTxFQPrCdXnO/hFVB03xnMf+Z8FoZrivtYO/OG18DOEUQvytX7NwNf61uIOypKM9lTUGSEw8Ci7p/e9H0h12nrKIW4VZaykkGWZW2AEXUSMhUN8/dMrAt1daTxpvZYpYiur3253R0Oi21Z5tI0Av/AG8KOfdnQ9h7h/fr0KfYYrrRtPWguCULVncVW4DMfU9ej2l9hXN6Q1Ka0Vwm1qLYv1TBHRsOhrJ4cZjvMwxs26nzXS3obASeuVWtKazYtBBqvWgojZrVyp7p5a2y1gdbcAQXDQ6Udr4sU/BGQJSHUbWl98p8PowGnqV+/U15o8rT0qral1PhEJobUB5J8Wv/LiEfzHHz6F7z810NDXBVRP68XtPDI6dnAAC5auSDK60jFrOwMXaElE+CGcVI1moMcc7k0jEkAORDQs4rCyf7tk4mtdrki4oZCix510kLnEo6NMHHBuWkNaUydHg8RQgiBYcgCyLGu6FhqVLaBPHNOey5ZHstvX1pDCVsp8QLUp6m2J2+MltOINIeR4H6FXPCSldaOsQQBVpT+1ltF9nxwXGNJ9QEgjDLJBWvc0M3vQSo1dqhGuK3PNUHuiIUWh7xRQ94+2uy5XrPCujkaEdwLmdkRrmSIKZQmCwGxwGolISKzqAm20GJWKh5JsnYXmBo5W1V/6pV+CIAim/928ebPqOXNzc3jhhRfwsY99DD/3cz9n+vt/+Zd/GVtbW/y/mZkZR38MHebmNnKmDP+EMin2NscDT1wFVK9oLTFAJG1tQncj0NUUQ0cqCkkGPn95HkDjqzEEVbHK3o9yReKteKeDVlorh7UpneAkPXCltd3FlBaqzv2AaI/MJYLTiHxczRRQLEsQBRvtWz7AjtK6kYpiIjX17B5uOQ01qfW1zm8Da3fYvw1CGAlD7UnEwiIKZcnQ73uigaSfHVsZ8j87EgChRNkAExaexEbwO4SR8NQYI5TfnFyvCnshXNEEmTgqHsbSwP95Gfjxz9h+CoUxmimtl7YL2CmUERIF67BXH2CkKF7PFLnVSyOCTVKxMLe4mTQ4cGqVs47QNlKdL2AD+7qbICgdUqu7qsJOlmWu1Bp+AEproFqB5FrFQYqoyW+xrw5DGIF6e5DzCmFxfKAl0ABRLXgwmY7H6wMhrQ06XrjSukEhjAC7pzqVLr5acpY6hIbbk0hGbe6BqaNkfYJ9fZuqrAGVlJ6pI61N1FFOfa0tPK0BdQ+n9XSthbazrtECl+7mOD58auCBtIK3JqO8I8yIhKT7/uSgw/XbAZyEMV5tQDDaKV6oM76eiZUM8iUJTbEwbxcPEqeHWyEIbJ5ZVtS6NMc00v7SSjG7nStjRymANM4eRL0mLSFKIjZbHskuQarLfEnC8k59N10tFu36WRO0NnntY0DYWQenHmlNeRCNUsxqr2MnX8amjmiCPivbZ0sxVE1U2yCttXapdnytuQ1RgwQC3ykgsYjW05rWh9ZkBG0NssKgLob7OkHX1H3YnY41bC+shVYc8SBsf+0KiN3A0bv5j/7RP8L4+Ljpf2NjaqVtfn4ezz33HJ5++mn89m//tuXvj8ViaG5urvrPCbrSMSSjIUhyvUm7Fo3ysyYc4sGHGqU1D5RqfAKrIAi8uvvlG2xj3eiBTSA/xen1rNLqt4tcqYJ0LBw4CdqdVoMPa4OT9KB6JtkcN/veywL9Tv6I7WvaqyEf9Sq+dKjqbY4HouSoxaAdpfVy40hrbg+ysF31/hTLEidsbROf5Gu9qJDW1PbbMmSpng1pwh71NhiN8vkmWCn0AZW0DkRpbcOT2Ax+hzASjvQ3ozkexk6hjGvz9WTxVQpxakAL7WENQWwUDEnFu5GOJGLh4DtfDhsUgWgcDbQm7JNaHqFahOiPYSICG1HoTURDfOOlJfRXdgooKEXDvtbGB9EA1UoS96T1fva1ohDyLpTWA5ogRlmWeQhjI60EyL7g2txWXcfLg1Ra13bgOC54+4RhRZU0XXOwoiKhIxVObfdR3wlP1/YgYWQPQiS2rgrTqdJ619rTmuxBzIIY5zbZNTXaGuQ7AVa2UZfJSjAAaxAC2Wtcn7O2q2yEjzT3tTZRWlPnyZH+5oYUHJrjEW4zRGrr2w1WWgPW3dbEC3Q2xRrmlzzUnoQgMEJ0QyFEZVnme64g7UEiIdVKzY7HLNmD2FaHapXWXc6sQQD94qHjc7YPiEdCnKivHTuyLFfZg9iGtrvaBmkNaC1LrX2teeBrA84u30mgLim9MdPI/ZVZeOeD8rMmjGrEEQ9CkMqLhzY6BpzCEevV1dWFQ4cOmf4XjbIqx9zcHN797nfj0Ucfxe/93u9BFIMn2ARBMGzF0YIOxY1QkAHqRHdDo7CjVucH4SMNqAtlviRV/X+j0amovmWZHfSo5e3EULCJ10B18OEdG+EHjivAnfuBT9wAnvoF29c03J5EWBSQLVZ01TeNbm0jpfVOocwDOmrRUO/m7hRCooDtfJlvsOgaypKMdFz1V7ZErdKaDqMWKmsCKUj0xs7yTgG7imJ2uAHqur3daviBXpdJsSzx6wzWHsS50lq7gfe7eBYSBTw5xixC9Hytr841roV2T2cK0bCIbLFi6EdJ60KjipkHeRdQ9Rgmwm9vAwk/ClAxCt6i8dsoEpKCcbQhwfS59bUkGlI01AOtP9GwiM6UyzyM2gNmm3OldU9zHIIAFMoS1jNFHsL4SANCGAn7upuQjIaQLVaq5p5yReKHz0aS1kPtSUTDIoqaDhxZlvm4aaTSmr2efijOrSUHftaEVKdqeQa8rZXWwwZt8yQK0A1ZJdJ+/R7zgzeDLKtK6yZjT2tuD2JTaf2/GqzCGImcDTL/hpSMVkprWZYbQiTR33ptfsswcJDI80YU4wlnFF/rc4qv9Z0G2ZxpMWyhtJ59APdSPBJCX3M1ITq7kcNuoYxISAich1A9ta0JJOdK66Pqvyng2QFqi4eyLPPrbCRprb2W2rGztF3AZraEkCg420u4Ia2Ve8VOzhbZgzyIkNwHCRJqbOfL3PaMSOtGWYMA5l0dc0r34QMjrZX3QRBUr/SGvj4vHvofxhjIqYsI6+HhYfzGb/wGVlZWsLi4iMXFxSBergpqGKO10nqsASQboHpWTazsoliWkC9VOKn+IJTWQH315UHZgwAq+XdrcQeXZtiGJ2g/a/7aNsMY86UKt8gIcjGNhEQ+GeqpZhu96UpGw2hT/LKM1Naqojj4BSMWDvHX0XYuaJW6tttEe5QN19YMCxKhEEYLP2vCAZMNBqnPh9sbo5jtbY4jFQ2hIsm67Uq3l3ZQqshoSUQCUWwRuXl/Patrw2GGha08dvJlhDXqdT9h5GudL6khTo0IKwqHRE6E6nlIA8Dd5caqk2jen1jerToA322gHzuBq/V1qvOSJGNiubEkJBH6tzSflRrC+OAIJPKLPtDjwNu7FrWBSS6U1tGwyEOkby3tcHL/keHGKa1DosBJJa1FyPR6FqWKjEQkhP6Wxn1WIVHg45juoZXdArLFCkShccVmAicFag4PXAXpdN+nLei+jUnrgdYEBIGJNrQB6bNmogAnYYyFbaCkvOcm9iA0NqlbQQ+UYfJQaV2NzWyRH4pPBFh0PjbA1oH761nDvAWAjZ2tXAnRkBjo+r2nI4V0LIx8SarKAtKCCPZGFOMJjym+1uen17GVK3FRSSPtQUYtRGtUSGx0OFqtmI4s4vZ1B+N9roXRGqAHKp712l0zm7qBBPvc3Smt2etsZEvYzpewvMPWypAoNDzo2kilT5/VWGfKmTpfKwSwSVrT+eC2hYguUyjzfXKj8kO+U5CMhtHZxMSxtB9vtHsCoHpaL2zV2xHPP+A1m7jNPQ8oq86OeNgtApktv/KVr+Du3bv42te+hsHBQfT19fH/ggZVGMzeLJW0bswAH2hNIB0Lo1SRcW91F/dWMpBkFvLRlXaplPIIbZtLIhJ6YP6cgKr2ub28w5XWp4Yac/ClKlRtcFItqJqWjoe5z15Q4FYPOgGRD2LTZeZrnS9V+MLRKFWmnjp0nHsiOzgsJFpV5djSdZW0pjZgC5ip9O+uNJb0EwSBv/93l+vnvhsaa5AgvB/7muOIR0SUKjJmTKxk9EAFh7GuVCD+X0/tZVYvb02tV20ubixsoyLJ6GyKBhLipAfKN7ixoL8pJauZRhGzA60JNMXCKFakKmKgkX7sBFqP9Sxm5jZzyJUqiIQEvlkMGnqhrxT+8iBCGAkHe9P4o7/7BP6/H3vU/S8hexAAiKZth4nWgpQkX7y6CFlm61J3g4Nn9MIYqZC4tzvVcD9edR5m10B7h/7WRMP9DUd0AgdlWcZt6mxxSq6Rr3W8BWgd8eUaHwSiYZETxto2Y76/MipK0d7Aytd6Z4l9jbUAUeM9QE8L2/8XyhK3DqjFnJnP9nc5zAqZ5Ok82pFEazK4PXlrMsrJNbMwRiKKD/amA73PRVHAiSFjX2ttCGMjrQPIFur6/Dafi/ta4miONy4kboRbB+R0A9JNPesbcF3UrXvTaf6OB3Ai36C7TwuutLa7hgsCcOyjQLID2PMux9eWjkf4eXpmPcv3fkNtje9kG+nUV866sgYBNEprAWgZtPUUKnZNr2VNc7bGF7Yhyyy88UFxSA8SVFSmtZvcE/Y0UGTTlY4hEWF2xLW5Vrw76gGR1s/s7cAvPrcP/+LDxx7I63MffxtzjlMEMiv81E/9FGRZ1v0vaKhKa33SuiLJPOipUa0EWg/pmws7nOQ60NPU8GAVwr7uJtBZ7kBv+oEErRCoJebC9AZvjz851JjNFpEydy2U1tr2k6A/M9XXWp+8ARo7GQ5wX+v6CYilLbMCTEeDAhC4R7xGAXnLrT9crzKpT7/K2n4B20rr/RpyotajeOIB2CuY+VoH3UomigK3d9ArtphBtQYJ5toO9DShIxVFviTxohhQ7T/ZqHn4UJ/qyV4LWZa5l12jWrpEUdCov1Vy1nXooQdoVXW1ewW6nrHOJoQbdJjRhuLQ/U2b5EargGrxzL5Ob9eQaFVtC9pH2eHTBWht+OK1BQCNVVkTSGWpVVrzoksDxy+hNozxQYQwEvS8mxe28tgpsM4Wx8qkfe8HxDCw/3nXY+Y7BURE0nuzW1A9Zw0V8aQ0t/K13mH3A9LG1iAA6xyjsMx5g7DrB7Hn+07BHl7IrM94IU/nEwH6WROO2whjvMZDGINv1ycPbz1f67sruw0NYSQMtCbQ2xxHWZLx5+dmATQmf0KLvpYEoiERxYqk6xNP91KjO15GeGgbkdYKEdoAW07VMsWGPcg2Ka0dFJ4/9BvAP74LNPe7uj6trzXPfmiwNQhgrNIfVwQmh5wWGHqUkMrO/UDYHrFMOVsVSTbNB+Jzzf9iKmsC7WtIMPcgfNAFQVAtZWrI2fmtB+tpHQ6J+MfPH8Q79rsTo3jFiPI5zKxndYuHXvBgTBkDhFUQw/wmk/JHQ2JDFy4i08YXt7kKaN8D8JohxCMh3kJw+AFagwCqOvatqQ3IMtv8dKcbo9Yi0vre6q6prQE3+m/ApGhGPpq2rwaEgVb2WnpKa2rZ39sVPJlP0FNAug7yI1/ri3/IvraOsDZgGxhW/EvzJYl/LoRGhjASSNWtRxpf40rr4DY5XClrEKRnBLJfCMqiSBAEPLW33teaVEqNsAYhkLJmXMceZGWngO18GaLQuC4goN4GI1dUrZAaWXQZak8iJArIlSpY2q5Oun8QoXqjHez+zmm6Sejrg+xM8g3kQenCGoRAHQqruyzQsZF+1gQicMYXtnknRSNDO2vBC+ErRFqztWCkwSGMgEpYzG/luHKLbFxcdbb0HgP+r+vA9/0nX6/zQWCkXSGS1thcR2qptmQETTGD8FmutL5o/svJz9rEGoSghjHW+1pXJJmrHx/UAfhBgs5z2/ky1jPFqp9RkSpIP2vCMRuk9VVOWge/nyCi/pIOaX11VhUoNFKMJAgCHlV8rb90jY3/Aw22v2S2EsbBgw9KaT1a4yt90y0R6gJm3rtaFMsSVhWrJEekNQB4yCzTFlYfBPlIMHqfSGDiWGndPgb8xF8DP/wntp8iCIJqO2ki/uHnuf/FQhgJ2oLzRqbIi80ND7qmTraaMaMGMT6YsPYHjb7mOKJh1nltVIx3i+860pomu7mNep8ZQG0zG+lgB+RGgRan8YUdTaryg/GzJjyqqKIehDpKiwM15H2j/KwBRpAnIiGUKrJhOBoATDVwMeXkYw1pLcvyd5w9SCNDGAlU5CCP+K1siR/4HHt0ktJ6c5p9tRnCCLBqJnVr1FqEPIj3xajYUZFk7ssWZGjH3k5jewcz3HTbqu4ATysWIa9qfK250roBSi0CKWtm1nPYyVe3g9MmdaTDoXeeR9QWge6t/v/t3XucFNWd//93dfd0z32GGWAuwgyDgHgBRFBEk9UokbCu3xhdY1zjF9Rks9mRFdncSKLEXCSXzW5idDUmWc1u1mhMvpo1v68a4wXXfPGGSxSjCIiiAsN17sytu35/zFRNNwy3qTNdZ2Zez8eDh073cPpD9alTVZ869Tmtcl2pND97T09IvfX8vQuYA298ZHsRRql3//ZmzXrbxi8PEmJNa2O8G3YH1rc+Bgcm0k6rzf65RG15vkryctTV01/jtb8mewgzrdOewHFdt3+mdQg3OsYVJpQfj8p1+xM2bwZ9sqWoUsoZ/v2/5oDSKe/tPYoJAUe7GKM/0/rIJRG9Gz8DzQzd2dKhnpSrWMRRRZbL7tggNyfqzzBPf3rWdV39yVuEMQt1m70b2+sPkbTuXYQxe7MfvWukjTtb1d7Vk/Heq+9n/2a8Z27f+N/VNwEom4sweg43cc27fhpwodUhlL4gbntXj/+09zE/GToI3jlV0/7uw9Zk39nSIdeV4tGIyoaw3M6BatISkOEmrXs/c09bl5r7zs07upN+zuikY01aS9Lkc6SxU47pr3h5oY2HqFcvpc+0Hl2LMHom+uVB9vv7UmVxrgoOdbN5iNQO8CRbR3fSn8QxGp+Oknqf4D3UwqaB2zbamgXGFfWepKfc/llR6bwV5rM5k03qv0v3xvZmPzkRxgE93Yq/nK47rjxNfz3n6OotDZWS/BxVFPc/PpPNpHUk4uj48V7i8dB3NrN5MPVmNzY0d2Yktva2damju/dksCqLd/D6y4McJmmdxWTSgTXivUftjivNO/b6eV7ixnOUpUE86SUEPK2dPX4SPZsL2aWXlUl/lPbtPW1q70oqNycypIvPem0fS9K6O5ny+9Ax1SM/Rt5M63VbG7W/K6n9XdldhNEzpiCuyr7kQ/qTAlL/4mjZTMxK/Ulr7zHI9NIg2S5fdaiFtzb6M2fD2TYbdrSoO+2R4zBrWhvzgWXS+TdJ8z4z6CbSk9a5OZFjn41kgOM4aSVCGpVKuaHMzPfUjS1QxJFaOnq0q6Uz1JnW6Y+werOBvJnWJ4Q8aSJs6Y+lS/3XC4edEJBf1r8OxuEWY2ztq2l9FDOtq/zFGA+eae2dc1WW5GZ1ko1N6gaoa72juUO7WjoVjThZWYjMS0S/s6ddTfsPTvxta+rQvvZuxSLOkJ7HeCpLclVRnFAy5eq1bZlPbYWxCKNnbm3mU4rZPl5L6eUwMq//m/Z3q6WjN8HvPT2aLd4s3r1tXXqp74nisYXZqUecH49pfN/nvDPAIu0e74mOipJEVmfo98+03t//RHMIx8rCRP8Cf96xctPOViVTrsbk5/jbcKh5pQEPtchqR3fSP7/JZs16m0xMKw+yJYRFGD0Dzc73rv3z41GV5GWvnr9tDny6xJQRl7R2HOewK1f2rzKa3YOpN4twZ0un/yVODeGCKl1pflyLZlSFWs/ak57APzXLjxj7izEeLmm9J3sDY3Fu/wEyPQHozZSqKE4oEcveTMwJls20dpz+C4MNO1r8i/BBlZcYUyfF02I/xqT11AEWY/RujI0tjA/pAkEH8p4eae3s0c6W/vIK3kXN9MriIb3wHUx5kC2729SddFWYiA3p0wOTyvNVVZKrrmRKa9/Zpz9vb1LK7b3JmX7DLBv8p24OSFr338zMdmK2N9H4fmPv7O/NIc5S9ZPWuzJn1YWVhPTHmYYWbWvcr5QrJWKRkbH4TVGl9MF/HPQijFLmTJKZE0qzvniSx7vx9Mq7TdrW1LtoZyzihFJHOjcn6l9UbdrZ6tc7nDQ2nBsdB9a17n/SL9xJE2E7cLscdemA6lm9/z3cYozeTOvCYykPcugavKN1xpbUf16RfiPTq+U8raJIefGhPxceUxD3+8VrA8y29kpyTKsoytpTUgPVte5Jpvyn6sKYaX1iVZHy076PMMozHao2sTfLurwgnpU+k64gEfNr1z/2Wm/plGwswujxF4I8zKxHv551lp/o8I6Vb+9u85PFYSQgpf4by16O5s9ppUGyNYFjqj/TeuDrqDcbWtTTl0jP1gLytvGO3e/t3e/nILK5CKMfh1+rvn+s6S8NkhfamnU2OFweNogRl7SW0hdjPHiA9hfUy3IHL0jE/AOHt3DdiLjwNcRL6kcjTtYXF0h/nHcgLR3d2tWXBMzWAhEDlXoIaxER72R9d2uXOrr7VzROpVy/pnW296f0cjveDNFBzXCJRKTxJ/X/XDXrmP66d4KR3nc2+09zZDfBlohF/YN5ejzeIoxDvUCQd6K5u7VrwNlIA/FKgwz1orQH1rX2LjJnZnERRo83G/X1AxZj9BaDzdYijJ6S/Bz/QuXNhha/HntYs1SlzATFzpZOtfTV+s72xcy0tJtj7+7tT2qN5pPRdOlP/IRZZsyr8fqn9xr9sa9ubEFoSXSvrMzad/apse+R7LDqoKc/pplMuf7FcDZmhNrM2y47mjvU0Z3sLx1wpO/Jq2t9uMUYj6GmdWXfTOuBalp7ifTjslzOwCYD3cj06lmfmqUF26XDL8aYrXOsdF4t7/S61umLMIYxWzUWjWh236Sj40rzDl0bfggdqjZxWPWsPV5e4vd/7n0KY6jWcBlIjV+//8gzrb3xKFvSbx52JXvXGwurfv+Bfcc7R89GGRePdzP57T1t/joU6da/3xvTKSFcu9iiqu/Jo65kSi9s2StJfqnObEovD+I93ew9HTWabzRL6TOtKQ9yRF5iceCZ1t4ssux38PSD1FAnaYYb7wLqxKrszJxIN2WA2bLpvAPY2ML4sZefGCSvZEl60tq7qMr2YFiSl6OCvu8kfbb1juYOf0Zbti/I0xeP8xfyG+yj6V5d6zF1Ut6xJV68mSQbG1qV6lsl10vkh5H0G6ge+p+zsAijJBXl9pf5eWuARUQH4n13g66vegzS61q/ksVFkw7kHQfeSEtau66rN0Oo2+zHlHYTKMzSCpMHSFp7SbZJ5QVZfcJE6v+u3trd5j9BMCIWYTSkvCDuL+Y3J4R61p5ZfcmrjTtb/XqPYfRfj/fZT7yxU5I0viih/Hj2EzhS/4X41r1temdPmzp7UsrNiYyMEjcBpC+4+N6+/ccw0/rU3v8edqa1l7Q+ck3r6sPUtPYnKoziC+CBbmR6s4tnZnE9isMtxhhGHWlvprW3oHT6/59yXHYXYUw3p69ESFg3xdJrWqeXyQtjEft03sxDbwJUNhOhk45iMUbvplm2Z+9WleQpltZXs73eWLoDZ+l7C2Zmc1b8+KKEinNjSrkDl1pcv81baHV0lgaRem+Oef30f/qOBWHMzj9uTJ6iEUcd3Sn/6eb302Zaj2Y1zLQ+ev0zrTM3VntXj7b1DcyTs1weRMo8SE3J8mw62100q1pLzpqkGy886ci/bNjUtJnWXuIx3Vsh1NnyZ1rvPLg8SLZnCjiO078YY1pday8xWluen/UZbSem1eH1agMPeuZC7dm9/637i2P/q2X5ikcj2t+d9A9WYZRM8fT3m94YXLe/5uFQLsLo8cbVo61rHfi7OwbeTOtX32/S82/13p2fGULdR29Blw07WvzxZndrlxrbu+U44STbvAvMP29v9o+boSSt+/rv1r3t6u5bzGlTXzI/m3XzPZXFuSrKjSmZcvX0hl2SjmIm5ijiOI4+OqtaU8YX+vtXGCqLczWuqLfG63/9aZukcJPWXl/1FosLo0yJpyZtEbD00iA2lIULk+M4GXWtvdrWR0xqVXmLMW4eeDFG101LWlccMY6qvovbHU0dB51/buMC2D+n2LKnTamUq1TK9Z+UmpXFpPWhFmPMWIQxi0lrr2b11r3t2tvWlRFbGKVBPEvOmqS/njNB158/NZTPHyiRJPVfu4Q909qTzfUf/Drfe+0rDxKNOBnfSbaeZh5IehkV13X1+o7+8iDZ4jiOPxlqoLrWr72f/ac6bORNHkn2HTPDSFrnRCN+eS/vhtA2v6TX6Czd4km/UTZQXm2wRmjSeuDVg72L8dL8HI0pyF6tWU/63bps1y21XW5OVF/7Xydr3uTsX/jW9CUeO7pTA9ZtfttLWmdxUPSSj5vSy4OE+Kiovxhj2vYJs/at99j+juYOtXUllRN1Bn/QOuVS6aqHpAu+ecx/NRaN+KVRvJn6m0N8miN9MUapd/vsbetSNOJkpYbp5AFmeh+OVx4kG7NyjivNU215vpIp1+/HYVzc1Y0tUDwWUVtX0l/8y+s7NWX5WauJmc67afDE6w3qSqaUiIXziGZFcUJ5OVH1pFw/ieQvwhhCEtJxHH/bPLtpt6QRsgijQd+7bJb+sPycUB4F9ziOo5l9+7K3KK4NM629iX5hLMLoSX+E1SulNdrrWXtqynrHuPXvN6m5b5G2Iya1Csqlkom9/7/9lYPf72iSevrOk46ipnVFUUIRR+pOutrd1pnxXpjnfLY4bkyecqKOunpS2ta0X2/tblNLZ49ycyJZvY7yzhXe3tOu5rQF0huaO7W7tfccK5vJrZK8HP98y7s59mqIT5B5ygri+qfLZvnlS7ItJxrxr1fe3p0+6ecoFlodQrVp1yexiOM/TZuVzz6KWY/95UGyn2xLnwgQRpkHT/pM64bmTjW2dysacbJ+LjFtgLKTUu/C9d5aONkuo2qb9PPwaMQJbTJJbVnmvrWtiRvNUu/1diziqLMnpZ0tB5c+G6wRmbT2klfv79uvrp6U/7pfzzqkQTH9hCbbdUtxaLFoxO8zA9W19vpNNu/kecnHd/a0+TMOw3y8beCZ1r3bJYwZkMW5ORllUqaMLxr8bG/HkY7/kJQ7uAsOv7xMQ6t6kim/v4Q509rrx6/11T+bOr4wK8lQb6bs0cy0bu3s8ft0tur7nZU2G7SiOKHxWZ5VIvWON95JqZdE2tgQXmJW6n8KqKG5N2kyeVxhKI9oOo5z0OPgftI6pBu9XoLPO5eYWDa6T0ZtdWCpgDDGX8+BF7m1Ic7OP25MniKO1NmT0h/7brycQNJaUv9srTVv7ZHUW+7mqMq4eGtfDFTX2ptlnVsixY/8vceiEY0v6isR0th/cee6Lgsxqjch4SXdtuxu80uDnFJdolgWn/AbUxD3v4f02dZeojhb51jp0hdj7Emm/FJw2SybYqOB6lqHXh4k7Rhw/LjCrJY68z67obkzY12idGEmrdNLrtkw07qhuVMvb90nqTdflO392ssPHTjTevOuVnX1pFSUiI36MnXp5+E1Zdl/2tv/7PL+SQGStK3vGD7ak9axaMS/Qbh1z8GTQQdrRCatxxUllB+PKuXKn8km9SdSsr1AmmfimHxVleQqPx7VSVl4VB9Hb4q3Yu8Ada3DSFpXFecqLyeq7mTvjEPXdUOdKXBcae/AnDHTOsQyGFJmojObi5ocaJr/KFer3t23X91JV7k5kVAuNL3Z3TuaO9Ta2eOXBsnWeOPN/PHq/x6OVxqkojih0vzsPPkyv6+utSTNOK40K585EC9J7C30stGvZx1OPz5+XGFGXcEwZ6l6q4B74673RMeUceFsmwPHlrAuenF4M9MWZXOccJPWxbk5Gp+20HZtiBfiOWkLW63tuxCfNsoXYfR4F/4vvdO7XY763Kq6r0TIQHWtW4++nrXHW9A0va51Y3u32rt6E0yj/QI4/UbmK32zisOYyTtQiZAwZzfP6isR8sp7Tdq4s1WdfQmtMG+S2cCfMbv34JnWYT21kF5ecnoWayRLvU+XF+f23ozbOkCJkFTKVUNzODWtpcykdRhlHjyl+XGV5PWuW/XYa73jeDafnvBMS1srKZ23COOJ1eHVrLfFREv6TG3aQtfcaM7k3WzeutdcXesRmbR2HGfAx2G8xcEmh/DYviRFIo7u/9v5eqj+bJWFUJ4Eh5Ze1/pAXpmZbA6MkYiTVmqhTU37u9XWdwETxmB4uJrWYZTBkDJLSoS16IuU3nda/ATb5LGFoZxUlObHNbawd2x5a1erv6p9thbtOL6v/uTbe9r9WmOHssEvDZK9k8IzJ5f5/x9m3UfvRPiNvpp53slpWGWj4rFIxnExrH1a6n8S6q3dbdrT2qk9ffU6s/k4bboD+2dNiPWJcWjp9W0njMnL+oLOB0q/8RN2EsmbQeaVKwnzJq9NvAtf7ymKo74h5S3GeLiZ1oVHrmftN1fSe361LW2mtXfxO7YwEUrJKJv4x4RdbVrXV886jPUovDrSr77fv4iyX2M2hIlIXuL+T+82+nW+Tw5xEUZbpNcmlqSm/d1++Z+wkkkl+Tkqze9NiGZzEUbpwJzIwUnr3W2d6km5ijjSuMLEQe8PNVuS1lJ/Ld4nX+9dRDnbNxik/qcK397TljEz3q+dP8pLg0j2JK29vvvO3nbtbu1SV09KjhPOEwu28falrfuYaX1E/Ysxps20Drk8iNR7wUs9Qfv4JR4OSFrva+tdIE3K7kKMUtqierta/UfbwrqAObCmdUtHd0YpgTBMT7sDHuZF+NSK/r7j1SAPo2SKJ73fZHMRRqn35kY8FlFXTyrjBsdANvQlbLP53Y0vyvUXQpw7aUzWPvdA6QuJSul1m8PrxydkLBQc4kxrb1bdrjb/JuKEMXlH99j+EEgvpVCSl6Pi3JxQ4sDhlRXE/ZmyU0KcZe1J34eyfe5woJqy/s8vycucBT6aHfiI9YSjLf3jLca4Z5PU0Zz5Xsv23v8ey0zrkoNnWr9HPWufd0zYsKNFr/ed05wawkxr7zzqtQFmWs8IIYl+YlWxYhFHe9q69Mj63n432kuDSJm1iaX+yTZlBXEVhLj2gnfuObumNOuf7S/GOEBda680yLiiRFZL7ni8kiCFiVjoxyYvud/S2XuTI4yZ1uOLEirOjSnl9j9xKMmfhDTaF2GUMmtah1lSxi8PsqfNX4Sxoig3tHIlNvH2pfeYaX1kXif2BmjXdbUl5PIgsJeXLNrU0CrX7Z8huqWv/1SV5GZ95paffNzZmlaPLZwLGO9zdzR3qCeZ8kvtjCtK+I9TZVtmeZDwDuK15QXKiTpq70rqvzfukhTuTFUvYb72nX3+TYZslQeJRhz/huHmI5QI8RdhzPJNvNv+ZrZu+5vZOnvK2CP/8hDxbrhs3duurXvatTfk2cRS5v5kRdJ6d5t/EyjMeEryc1TZV/ucetZ282ZbT7VgYoDXZ0vzc1SSH+6Njtq0pwNOqCiS44zumZie48bkKX1THPVM6/TFGHccsBhjS0Pvf4uOvAijp6pvUsC2pv6Z1t4F8AQeM/aPCS+8vVddyZRK83NCqenqPZ311u42tXR0a2dzh3a2dCrihJPcys2J+p/79Ju9555hLsJoi0lj+xK0uzMf2Q/r+snzT5fN0s8Wz9W8urIj/7Jh6WUMDrTdr2cdzvaZXlmk68+fqlsumRH6sWnSAU/SnRjCtaXjOGllJ3uvk1Ip15+ExD4ujS2MK69vAl+YE1G9xOy+9m7/6eHqUmZZS2nj8F5mWh9R/0zr3uTartZOtXT2KOJknsADUu/OFY04auns0c6W/hXcvRsdYcyU8hJYvTOtw63HNq4woXg0omTK1Y7mjtBLg/R+dqHOnz5efzWzShXF4d2dz0lbyPO5t/b6sYXF++z/+2rvY8o1ZflZnR16/FEsxui6rjY0eOVBsptgmjyuUH81szqrn3mgsoK432cffmWbpN6EaFiziaX+pHXECXdm6OS+EjM7mjv8RbfCWqDS49UAnkg9a6tdd94U/eWMSi0+a1LYoei0mt4nOcIsQ+RJL08yrZJJG55ELKqqtMV4jymp5S3GeGBda3+m9dEnrau9mdZpa4Z4iTYugPsnGnklx2ZOKA0luVVemPC/q9e2NWt938zH48cVhnbsntVXy9+ba2PDeBO2CWPy5Ti9s2X3tnWFuh5QuurSPJ1/YkUofbe/zvfBSWtvpnVVCAuTS71J2hs+PE3/a1a45+VSfxJSksbk54R2bTn1gLrWW/a0qb0rqdycSKhJWls4jqO/mVejWRNL/XOtMBQmYirvK/frLeg82teg8Hj70rvMtD4yb4D26hF7CZQJY/KzumovhodELOrfzEhf/MCvZx1CctabqbV5V1voM60jEcdfLOj9fftDX4RR6p3V+7Mlp+u2vzkt9Lvz3gmGd1EVbtK6t696s3ezVRrE4y/GuOvQM613tnSqsb1b0YgT6izaMHkzpB7+U2/SOszSIFJvkq0kL0dnTi4PtYZqSX6OfxL45Bu9dQXD3jan9j36bcMMXhzaiVXF+tcr51ixCM4px5Xod0s/oB9dMTvsUDLqP2b7yRbbpW+bY7op5dW13vY/ma97Na0HMdN6R9pMa6+kgQ19OWxjC+MqSivrMCuEUhyeU9IWY3z1vd6Zj2EmitNr+Rflsgij1DsD3UvAvr2nPe36afRum8OWB2n2Zlpzgyx9UuP0yuLQri29iRreTGtvlvWJVcWhlHCx0Y1/dZJ+W3926OuXePvWms2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            "text/plain": [
              "<Figure size 1800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize= [18,6])\n",
        "plt.subplot(211)\n",
        "plt.plot(z_true)\n",
        "plt.xlabel('Time')\n",
        "plt.autoscale(enable=True, axis='x', tight=True)\n",
        "plt.subplot(2,1,2)\n",
        "plt.imshow(mean_true.transpose(), extent=[0, total_length, num_neurons, 0], aspect=\"auto\")\n",
        "plt.xlabel('Time')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "pqhsFCN6XxUg",
        "outputId": "978bbf70-556d-4be8-8521-3a0e7092ad7a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize= [18,6])\n",
        "plt.imshow(z_true.transpose(), extent=[0, total_length, num_neurons, 0], aspect=\"auto\")\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Neuron')\n",
        "plt.title('Raster plot of 20 Independent Poisson Neurons')\n",
        "plt.colorbar()\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d7-6OY3jqyth"
      },
      "source": [
        "Realistic neurons"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNo3Tgvy/YlcNnqxBMr2dZ1",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}