NASBench.ipynb

{
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  "metadata": {
    "colab": {
      "name": "NASBench.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SRxqMakh3PRY",
        "colab_type": "text"
      },
      "source": [
        "Copyright 2019 Google LLC\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "\n",
        "    https://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "47ieDn-jNLYd",
        "colab_type": "text"
      },
      "source": [
        "# NASBench-101\n",
        "\n",
        "This colab accompanies [**NAS-Bench-101: Towards Reproducible Neural Architecture Search**](https://arxiv.org/abs/1902.09635) and the rest of the code at https://github.com/google-research/nasbench.\n",
        "\n",
        "In this colab, we demonstrate how to use the dataset for simple benchmarking and analysis. The publicly available and free hosted colab instances are sufficient to run this colab."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lBNMsBUS3SAq",
        "colab_type": "text"
      },
      "source": [
        "## Load NASBench library and dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vl1oLYux3FhJ",
        "colab_type": "code",
        "outputId": "260484df-3a86-48ef-88a4-f8e441d524bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        }
      },
      "source": [
        "# This code was written in TF 1.12 but should be supported all the way through\n",
        "# TF 1.15. Untested in TF 2.0+.\n",
        "%tensorflow_version 1.x\n",
        "\n",
        "# Download the raw data (only 108 epoch data points, for full dataset,\n",
        "# uncomment the second line for nasbench_full.tfrecord).\n",
        "\n",
        "!curl -O https://storage.googleapis.com/nasbench/nasbench_only108.tfrecord\n",
        "# !curl -O https://storage.googleapis.com/nasbench/nasbench_full.tfrecord\n",
        "\n",
        "# Clone and install the code and dependencies.\n",
        "\n",
        "!git clone https://github.com/google-research/nasbench\n",
        "!pip install ./nasbench\n",
        "\n",
        "# Initialize the NASBench object which parses the raw data into memory (this\n",
        "# should only be run once as it takes up to a few minutes).\n",
        "from nasbench import api\n",
        "\n",
        "# Use nasbench_full.tfrecord for full dataset (run download command above).\n",
        "nasbench = api.NASBench('nasbench_only108.tfrecord')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100  498M  100  498M    0     0  54.1M      0  0:00:09  0:00:09 --:--:-- 67.7M\n",
            "Cloning into 'nasbench'...\n",
            "remote: Enumerating objects: 92, done.\u001b[K\n",
            "remote: Total 92 (delta 0), reused 0 (delta 0), pack-reused 92\u001b[K\n",
            "Unpacking objects: 100% (92/92), done.\n",
            "Processing ./nasbench\n",
            "Requirement already satisfied: tensorflow>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from nasbench==1.0) (1.15.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (3.1.0)\n",
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            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (1.1.0)\n",
            "Requirement already satisfied: tensorflow-estimator==1.15.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (1.15.1)\n",
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            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (3.10.0)\n",
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            "Requirement already satisfied: tensorboard<1.16.0,>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (1.15.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (1.15.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (0.33.6)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (1.11.2)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow>=1.12.0->nasbench==1.0) (2.8.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow>=1.12.0->nasbench==1.0) (41.4.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow>=1.12.0->nasbench==1.0) (0.16.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow>=1.12.0->nasbench==1.0) (3.1.1)\n",
            "Building wheels for collected packages: nasbench\n",
            "  Building wheel for nasbench (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for nasbench: filename=nasbench-1.0-cp36-none-any.whl size=46791 sha256=88bafd1b9d560fd0fe9b6f8ceac03001e0b42b4dc2718648550569f654f098b3\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-blg0rruf/wheels/4b/19/99/1d5fdfe30f8b16fab91e900808f4f7e5adc38e602c84970ad5\n",
            "Successfully built nasbench\n",
            "Installing collected packages: nasbench\n",
            "Successfully installed nasbench-1.0\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/nasbench/lib/training_time.py:130: The name tf.train.SessionRunHook is deprecated. Please use tf.estimator.SessionRunHook instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/nasbench/lib/training_time.py:174: The name tf.train.CheckpointSaverListener is deprecated. Please use tf.estimator.CheckpointSaverListener instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/nasbench/lib/evaluate.py:30: The name tf.train.NanLossDuringTrainingError is deprecated. Please use tf.estimator.NanLossDuringTrainingError instead.\n",
            "\n",
            "Loading dataset from file... This may take a few minutes...\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/nasbench/api.py:146: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use eager execution and: \n",
            "`tf.data.TFRecordDataset(path)`\n",
            "Loaded dataset in 53 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oFhFRmck7NzM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Standard imports\n",
        "import copy\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "\n",
        "# Useful constants\n",
        "INPUT = 'input'\n",
        "OUTPUT = 'output'\n",
        "CONV3X3 = 'conv3x3-bn-relu'\n",
        "CONV1X1 = 'conv1x1-bn-relu'\n",
        "MAXPOOL3X3 = 'maxpool3x3'\n",
        "NUM_VERTICES = 7\n",
        "MAX_EDGES = 9\n",
        "EDGE_SPOTS = NUM_VERTICES * (NUM_VERTICES - 1) / 2   # Upper triangular matrix\n",
        "OP_SPOTS = NUM_VERTICES - 2   # Input/output vertices are fixed\n",
        "ALLOWED_OPS = [CONV3X3, CONV1X1, MAXPOOL3X3]\n",
        "ALLOWED_EDGES = [0, 1]   # Binary adjacency matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "llC2AebQOWq9",
        "colab_type": "text"
      },
      "source": [
        "## Basic usage"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZvm6i0VGP_M",
        "colab_type": "code",
        "outputId": "2999b6a6-9f1d-4361-8be9-ee54afdb82be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "source": [
        "# Query an Inception-like cell from the dataset.\n",
        "cell = api.ModelSpec(\n",
        "  matrix=[[0, 1, 1, 1, 0, 1, 0],    # input layer\n",
        "          [0, 0, 0, 0, 0, 0, 1],    # 1x1 conv\n",
        "          [0, 0, 0, 0, 0, 0, 1],    # 3x3 conv\n",
        "          [0, 0, 0, 0, 1, 0, 0],    # 5x5 conv (replaced by two 3x3's)\n",
        "          [0, 0, 0, 0, 0, 0, 1],    # 5x5 conv (replaced by two 3x3's)\n",
        "          [0, 0, 0, 0, 0, 0, 1],    # 3x3 max-pool\n",
        "          [0, 0, 0, 0, 0, 0, 0]],   # output layer\n",
        "  # Operations at the vertices of the module, matches order of matrix.\n",
        "  ops=[INPUT, CONV1X1, CONV3X3, CONV3X3, CONV3X3, MAXPOOL3X3, OUTPUT])\n",
        "\n",
        "# Querying multiple times may yield different results. Each cell is evaluated 3\n",
        "# times at each epoch budget and querying will sample one randomly.\n",
        "data = nasbench.query(cell)\n",
        "for k, v in data.items():\n",
        "  print('%s: %s' % (k, str(v)))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "module_adjacency: [[0 1 1 1 1 0 0]\n",
            " [0 0 0 0 0 0 1]\n",
            " [0 0 0 0 0 0 1]\n",
            " [0 0 0 0 0 0 1]\n",
            " [0 0 0 0 0 1 0]\n",
            " [0 0 0 0 0 0 1]\n",
            " [0 0 0 0 0 0 0]]\n",
            "module_operations: ['input', 'conv3x3-bn-relu', 'conv1x1-bn-relu', 'maxpool3x3', 'conv3x3-bn-relu', 'conv3x3-bn-relu', 'output']\n",
            "trainable_parameters: 2694282\n",
            "training_time: 1155.85302734375\n",
            "train_accuracy: 1.0\n",
            "validation_accuracy: 0.9376001358032227\n",
            "test_accuracy: 0.9311898946762085\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uXnVdG32Oe19",
        "colab_type": "text"
      },
      "source": [
        "## Example search experiment (random vs. evolution)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xtl_Aqr7OeOF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def random_spec():\n",
        "  \"\"\"Returns a random valid spec.\"\"\"\n",
        "  while True:\n",
        "    matrix = np.random.choice(ALLOWED_EDGES, size=(NUM_VERTICES, NUM_VERTICES))\n",
        "    matrix = np.triu(matrix, 1)\n",
        "    ops = np.random.choice(ALLOWED_OPS, size=(NUM_VERTICES)).tolist()\n",
        "    ops[0] = INPUT\n",
        "    ops[-1] = OUTPUT\n",
        "    spec = api.ModelSpec(matrix=matrix, ops=ops)\n",
        "    if nasbench.is_valid(spec):\n",
        "      return spec\n",
        "\n",
        "def mutate_spec(old_spec, mutation_rate=1.0):\n",
        "  \"\"\"Computes a valid mutated spec from the old_spec.\"\"\"\n",
        "  while True:\n",
        "    new_matrix = copy.deepcopy(old_spec.original_matrix)\n",
        "    new_ops = copy.deepcopy(old_spec.original_ops)\n",
        "\n",
        "    # In expectation, V edges flipped (note that most end up being pruned).\n",
        "    edge_mutation_prob = mutation_rate / NUM_VERTICES\n",
        "    for src in range(0, NUM_VERTICES - 1):\n",
        "      for dst in range(src + 1, NUM_VERTICES):\n",
        "        if random.random() < edge_mutation_prob:\n",
        "          new_matrix[src, dst] = 1 - new_matrix[src, dst]\n",
        "          \n",
        "    # In expectation, one op is resampled.\n",
        "    op_mutation_prob = mutation_rate / OP_SPOTS\n",
        "    for ind in range(1, NUM_VERTICES - 1):\n",
        "      if random.random() < op_mutation_prob:\n",
        "        available = [o for o in nasbench.config['available_ops'] if o != new_ops[ind]]\n",
        "        new_ops[ind] = random.choice(available)\n",
        "        \n",
        "    new_spec = api.ModelSpec(new_matrix, new_ops)\n",
        "    if nasbench.is_valid(new_spec):\n",
        "      return new_spec\n",
        "\n",
        "def random_combination(iterable, sample_size):\n",
        "  \"\"\"Random selection from itertools.combinations(iterable, r).\"\"\"\n",
        "  pool = tuple(iterable)\n",
        "  n = len(pool)\n",
        "  indices = sorted(random.sample(range(n), sample_size))\n",
        "  return tuple(pool[i] for i in indices)\n",
        "\n",
        "def run_random_search(max_time_budget=5e6):\n",
        "  \"\"\"Run a single roll-out of random search to a fixed time budget.\"\"\"\n",
        "  nasbench.reset_budget_counters()\n",
        "  times, best_valids, best_tests = [0.0], [0.0], [0.0]\n",
        "  while True:\n",
        "    spec = random_spec()\n",
        "    data = nasbench.query(spec)\n",
        "\n",
        "    # It's important to select models only based on validation accuracy, test\n",
        "    # accuracy is used only for comparing different search trajectories.\n",
        "    if data['validation_accuracy'] > best_valids[-1]:\n",
        "      best_valids.append(data['validation_accuracy'])\n",
        "      best_tests.append(data['test_accuracy'])\n",
        "    else:\n",
        "      best_valids.append(best_valids[-1])\n",
        "      best_tests.append(best_tests[-1])\n",
        "\n",
        "    time_spent, _ = nasbench.get_budget_counters()\n",
        "    times.append(time_spent)\n",
        "    if time_spent > max_time_budget:\n",
        "      # Break the first time we exceed the budget.\n",
        "      break\n",
        "\n",
        "  return times, best_valids, best_tests\n",
        "\n",
        "def run_evolution_search(max_time_budget=5e6,\n",
        "                         population_size=50,\n",
        "                         tournament_size=10,\n",
        "                         mutation_rate=1.0):\n",
        "  \"\"\"Run a single roll-out of regularized evolution to a fixed time budget.\"\"\"\n",
        "  nasbench.reset_budget_counters()\n",
        "  times, best_valids, best_tests = [0.0], [0.0], [0.0]\n",
        "  population = []   # (validation, spec) tuples\n",
        "\n",
        "  # For the first population_size individuals, seed the population with randomly\n",
        "  # generated cells.\n",
        "  for _ in range(population_size):\n",
        "    spec = random_spec()\n",
        "    data = nasbench.query(spec)\n",
        "    time_spent, _ = nasbench.get_budget_counters()\n",
        "    times.append(time_spent)\n",
        "    population.append((data['validation_accuracy'], spec))\n",
        "\n",
        "    if data['validation_accuracy'] > best_valids[-1]:\n",
        "      best_valids.append(data['validation_accuracy'])\n",
        "      best_tests.append(data['test_accuracy'])\n",
        "    else:\n",
        "      best_valids.append(best_valids[-1])\n",
        "      best_tests.append(best_tests[-1])\n",
        "\n",
        "    if time_spent > max_time_budget:\n",
        "      break\n",
        "\n",
        "  # After the population is seeded, proceed with evolving the population.\n",
        "  while True:\n",
        "    sample = random_combination(population, tournament_size)\n",
        "    best_spec = sorted(sample, key=lambda i:i[0])[-1][1]\n",
        "    new_spec = mutate_spec(best_spec, mutation_rate)\n",
        "\n",
        "    data = nasbench.query(new_spec)\n",
        "    time_spent, _ = nasbench.get_budget_counters()\n",
        "    times.append(time_spent)\n",
        "\n",
        "    # In regularized evolution, we kill the oldest individual in the population.\n",
        "    population.append((data['validation_accuracy'], new_spec))\n",
        "    population.pop(0)\n",
        "\n",
        "    if data['validation_accuracy'] > best_valids[-1]:\n",
        "      best_valids.append(data['validation_accuracy'])\n",
        "      best_tests.append(data['test_accuracy'])\n",
        "    else:\n",
        "      best_valids.append(best_valids[-1])\n",
        "      best_tests.append(best_tests[-1])\n",
        "\n",
        "    if time_spent > max_time_budget:\n",
        "      break\n",
        "\n",
        "  return times, best_valids, best_tests\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HMfF2zXxpQNA",
        "colab_type": "code",
        "outputId": "edff2ca4-8a55-4537-838a-dedc7a9f360b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "# Run random search and evolution search 10 times each. This should take a few\n",
        "# minutes to run. Note that each run would have taken days of compute to\n",
        "# actually train and evaluate if the dataset were not precomputed.\n",
        "random_data = []\n",
        "evolution_data = []\n",
        "for repeat in range(10):\n",
        "  print('Running repeat %d' % (repeat + 1))\n",
        "  times, best_valid, best_test = run_random_search()\n",
        "  random_data.append((times, best_valid, best_test))\n",
        "\n",
        "  times, best_valid, best_test = run_evolution_search()\n",
        "  evolution_data.append((times, best_valid, best_test))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Running repeat 1\n",
            "Running repeat 2\n",
            "Running repeat 3\n",
            "Running repeat 4\n",
            "Running repeat 5\n",
            "Running repeat 6\n",
            "Running repeat 7\n",
            "Running repeat 8\n",
            "Running repeat 9\n",
            "Running repeat 10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2d-yRmuhkz35",
        "colab_type": "code",
        "outputId": "2ae12897-074f-4705-db94-6be1aa4208dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        }
      },
      "source": [
        "plt.figure(figsize=(20, 5))\n",
        "\n",
        "plt.subplot(1, 3, 1)\n",
        "for times, best_valid, best_test in random_data:\n",
        "  plt.plot(times, best_valid, label='valid', color='red', alpha=0.5)\n",
        "  plt.plot(times, best_test, label='test', color='blue', alpha=0.5)\n",
        "\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('time spent (seconds)')\n",
        "plt.ylim(0.92, 0.96)\n",
        "plt.grid()\n",
        "plt.title('Random search trajectories (red=validation, blue=test)')\n",
        "\n",
        "\n",
        "plt.subplot(1, 3, 2)\n",
        "for times, best_valid, best_test in evolution_data:\n",
        "  plt.plot(times, best_valid, label='valid', color='red', alpha=0.5)\n",
        "  plt.plot(times, best_test, label='test', color='blue', alpha=0.5)\n",
        "\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('time spent (seconds)')\n",
        "plt.ylim(0.92, 0.96)\n",
        "plt.grid()\n",
        "plt.title('Evolution search trajectories (red=validation, blue=test)')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Evolution search trajectories (red=validation, blue=test)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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N2aeemvXrZpzOTtizB44+GubMOXB466pVjD/uuBwKll0KKb95m9fubrjvPpg7\nF6ZPD31afWMjkzIoVizMyDAMwzCMfCISgaIiOOqorF96f0NDTq6bcfbvh8mT4eSTYcGCA4f3VFZm\n3cUklxRSfvM2r+3t8MYbcP75cNppoU/bU12dOZniYBO/DcMwDCOf8I0MI334blJ2X43hTh6VVRvJ\nMAzDMIx8wrm8aGDkFb5/ezrua3MzrF8/9HRywJi1a6GyMtdiZIW8zWtbG9TXw9q1KZ1WWVOTGXkS\nYEaGYRiGYeQTNpKRftLZO/z887B8+dDTyQGTampgx45ci5EV8javHR3wzjtaVjdvDn3a2La2DAoV\nGzMyDMMwDCOfMCMj/fhGhsjQ0+ru1lWLsrySTzrY+vzzzD7rrFyLkRXyNq979kBxMVx4IbzrXaFP\n27lkCdmeTWVGhmEYhmHkE2ZkpJ90jmREIlBSkpeuOJFRo/JS7sGQt3lta4PSUhg9OiX5I2VlGRQq\nNlZLGYZhGEY+EYmkp8fd6CPdRoYZgUamsInfioicD9wKFAO/cs79ICp8FvAbYAqwB7jMOVfnhfUC\nb3pRtzrnLvSOzwEWA5OA14HLnXNdmcyHYRiGkRlMTwwCa8SmHzMy8ofnn0/LXIopq1fDrl1pECjL\n7NkDq1fDk0/qd0jG19aOnB2/RaQY+BnwfqAOWCoiDznn1gSi/RC42zl3l4icA9wCXO6FtTvnjo+R\n9H8BP3LOLRaRXwKfA36RqXwYhmEYmcH0xCCxRmz6SefqUvZ8Msuzz0JFxZBdncqamvJz4ndTk7pM\nNTbq3IyQlOzbl0Gh4lwzg2mfCmxwzm0CEJHFwEVAUHnMB77u/X4WeDBRgiIiwDnAp7xDdwE3MZKU\nh2EYRuFgemIwWCM2/aR7JCOFxp+RApGIGoQLFsDZZw8pqfrqao7Ix834tmyB3l644grd9Tsku0fY\nZnyHArWB/3XesSArgYu93x8FxoiIv+v5KBFZJiKviMhHvGOTgGbnXE+CNA3DMIz8wPTEYDAjI/2k\n08jo7bXnkyl6e/W7kI04m5MRmuuBn4rIlcDzQD3glSBmOefqRWQu8IyIvAnsDZuwiFwFXAUwdepU\nqgdpwbW2tg763HyjkPIKlt+RTCHlFUZ8fk1PRDHlrbco3bePbTl45iO1rJXv2MG0mhoaXn2Vjrq6\nA8cHk9+D16zBFRWxIw/v03B/vtLVxayaGvasXElLT0/yExIw3PMaj4r6eqbW1LD9tdfoTGGDvVzk\nN5NGRj0wI/B/unfsAM65bXg9VCJSBXzMOdfshdV735tEpBo4AXgAGC8iJV4v1YA0A2nfDtwOcPLJ\nJ7tFgxwSq66uZrDn5huFlKZhXiAAACAASURBVFew/I5kCimvkNf5NT0xGLZvh5YW5qXrmpEINDT0\n9ZAm4OWXX2bh8bGmweQ55eUwcSKz58yBmTMPHB5UfidPhvJyjj788DQLmXlC5zcSgZ07++ayZIvi\nYigtZXZlpS7hOgRef/ttTjrppDQJlkWqqvrK6rRpoU9b8vrrnDFSJn4DS4EjvFU+6oFL6PORBUBE\nJgN7nHMR4FvoCiKIyARgv3Ou04tzOvDfzjknIs8CH0dXDvk08NcM5sEwDMPIHKYnBkO63aWWL4dH\nHgkVdVpNTUor2uQNTU2wcqXe2/HjDxweVH5ff133MWhqSq+MWSB0fuvrddfpbNPTA3V1WmbHjBlS\nUoc1N/d71nlHinuxTIxE4KKLMijQQDJmZDjnekTkOuAJdGnC3zjnVovIzcAy59xDwCLgFhFx6DD4\ntd7pRwO3iUgEnTfyg8BqIzcAi0Xke8AbwK8zlQfDMAwjc5ieGCTp3idj/379/uQnkxovO159ldkL\nFiRPs6cH/vpXXQUnH+jt7ZtU7Pv9gx4L/g+bVmlp6ucNB8Lmt6ND79Wxx2ZepiCdnfqZMwemTBlS\nUq319Yw/NIvTtfx7lg6Ki/Uda2kJf0oOymNG52Q45x4FHo06dmPg9/3A/THOewmIuVe6twrJqemV\n1DAMw8gFpicGQbpHMnzf9nnzkhov7fX1cMQRydNsbtZG1axZMGFCGoTMMNu3q/vPMcfApEkHDreW\nlKTekG5o0F72PHQrSym/vb1wzjmZFSiaffu0YX3aaVq2hkDzW28xPVtGUn09LFmS3jT3hp5+BkDZ\nEN3LBkOuJ34bxsigtRXuuw+6hu9+X4esXw9vv51rMbJCIeUVYMr27VnfZMnIAM5pAyrZ3Ih9+9QY\nSJc7TnMzdHfrdxJK9u0Ld92mJmhvhyOPzH5v92B45x2orYUzz4SDDz5wuLmsDM44I7W0Nm6Egw7K\nfgM8DYTOb0+PuutkO4+NjVBTA+edpwbhENg9aVL26s3nn9dN9D7+8ZytCtXw1lvMyfI1zcgwjHSw\ncyds3aoTBoe4QVCm6Kmqym//0xQopLwC9KbYo2VkgVWrtPMhFdasgZdfTh5v3TptqCxfPjjZQGXz\nXZl27lTjZuXKpKcd1djYr6c/Ll1d2hh85ZUh+85nhX37dDRj+XKdBO4RKr89PXq+7wqzbRuMGgW3\n355BgTPDsS0tMHZs8oh79uhI1RNPZF6oWNTVwcSJQ0piztat8OijySOmg4YGfd9SmKidbqp27876\nNc3IMIx04Ps6nnsuTJ+eW1nisLO6mvkF0ttdSHkF2JOHyzCOaPbuhT//OfXzNm7UxtORRyaOV1x8\nYJWdQVNfr41E6HNtamhIelr5vn066pGM7m41ZHbvHl7zMvx5F9F0dKi8O3f2u6+h8rt3b/88trbq\n80nBX364UNHZqQZEMtrb9V7mavS+vn7I85KmdnSoMZgtKit1JCZHTKqqyvo1zcgwjHTguzcU8gZB\nhmEofsProotg/vzYcWpqBjY4Xn5Zl+W84gpNY/Pm2K5T+/dr4+iUU8LLtHdvfzenqVPh0EPV5WTD\nBm0kf+ITSZPZ8MYbnHDCCcmv19gIDz+s7jSBJWFzSns73H9/4onNF1+sPfnbtkFLC9vWr+fIefMS\np7tqlRpo732v/l+xQvO8cGH6ZM8Sa1eu5Ph3vzt5xBUrdDTt05/OvFAZYumSJZyZqivcUCgrS++C\nDSmy4aWXOCTL1zQjwzDSga+04vlarl8Pr76aPXliMHXtWvU5LgAKKa8Ak2prbU7GcMLv+a6o6Od6\n04+//KVvJMFn/XrYtQv+/ndt5K5fH/vcbdvUHz5eeCy2b9dVeYLU1GgDGWDcOAgxIjahpibchNOW\nFk1/6VLYtCm8nJlk3z4dLTrkkNh7LJSW6vKzoD70zjG1uTm5W9ru3fosgyMAvb3Z30MiDYyvqQk/\n8jR//pBXeMolvRMm5LX8qRLJ5qiNhxkZxuBwTv2Co5VWHjF6w4b0rXryzjvak7V2bWyXg2eeUeU2\neXJ6rjcIRtXXa09KAVBIeQUYFca9wcge/mpNidyZurp0hZxgT+rDD8OWLXDddfDaa/DUU3DVVQNd\nOn77W50ncOGF/Y87p/VMrHq5ulp71/2JuiL9549VVIQaia194QVmn3lm0njU1sLvfgeXXAJz5yaP\nnw02bVKj7/LLYcaM+PG6u/Vz+ums7uzk9Pe8J3G6jzyi9f7nP993bPTonPZaD5bQzxeG7fxDY/hg\nRoYxOLZvh8WLcy3FkJhSU6P+z+mgoUFXM3rsMVXW0axerb1DOTQyJA8V3mAppLwCuALL77DHNzJK\n4qjYSEQ/lZW6e69PaWnfsfJyNZQPOWSgsVJVpR0kU6f2P75jBzz5ZOxrFhXpXI8hNvh7R40Kt9Py\nqFEqd1XVkHdmThsiek8nT+5/36Pp6NB4kybR3dHRb7WpmIwdq4Zdsnh5QG9FReJ7YxgpYEaGMTj8\nYf6Pf1z9evOQuiVLmJ0uf8yVK9XAuOaa2CtzPPCAug985jPpud4gSGt+hzmFlFeAHS++SBKvcSNb\nRCJw9936O95oWjwjpLe371hwLsatt/ZfXvbVV3XFpuidmRsb4c03dZ5FdENRBJ5+WkdVh8Dsmhp4\n7rnkEf0dtPfuHX4rvSXrgffdX8POsevttfl4hhEDMzKMweEryQkT8mOjpRj0jBmTPtlHj9YRjEmT\nYvcClZer8ZHDe5XW/A5zCimvAL3W8zh88OvG8eMHjjREx4k2Mvy9B6Cvobtpk04IP/jgviU7Dz1U\nNyKLdmtZv17nHVxwQcZ6o5tXrAi3yVxdnRoYp58+vPzex41LPrJiRoZhpAUzMozB4U9sHMoSiiMJ\nv9cx3sTvYOPBMIyRiz/Z99RTE9cHkNjIiER09OH113VZ1eDO2WPGqMtq9A7CkQjMng3nn5+x+qYZ\nwi0ysG6drlp1+unq8pVPmJFhGGnBWj3G4Ejmc1xoJFNKvb3xV5kxDGPk4BsZiebJJDIy/I6bSESN\nlLY2/T1rVt9SsG1t6n4Zyy1q8uThUS8n63gZzpiRYRhpYRjURMaQcE6XM/SV1hAob2jQlU3CsHWr\n+ghv367D83lISvlNxrZtej/q6mIr+B07tPcxXdcbBGnN7zCnkPIKUJaDnVyNOPhGRrzGdV2d7jGw\ndasuHxusP9etU7fLF1/UsLo6XRZ11y5dRnX79r64FRVw7LGx530Mpuw3NencsSS6ZM6WLeF2ee7t\n1RW0tm3Lv1UI/dXaUjEyCmg1O8MIixkZ+c7GjfD736clqWk1NbpCUhjq6nQo/J578tZlKqX8JqOm\nRj933x27B/O119QPOIdLjaY1v8OcQsorwOSmJl2Ewcg9iUYynIOvflUb3tu3w1tv9Z+EXF+v9elz\nz+kk7rY27bRobtb/wWVXu7vhD3+ILcO2bbphXyo0Nur1J05MOAoz1u9cCkNxMfzv/+blUq4AvPoq\nE3fuHLifSTTNzbYik2HEwIyMJExYuhR+/vNwmw+BDmHX1ibeURRUQXR1qdLp7u6/aY9zyc+PJl6v\nWazdYoMErjvTudSVwQsvpBY/nQxxo6OM7EEb7SPt49/bhx7KxFVDMaOAhvQLKa8Ac4qLdQUiI/v0\n9FBRWwuvvKJ6oqNDO2Beekm/ly3TToaGBq0Htm/XuqC4uK+Hv6NDw5zT483NaiR0dambZUmJduyE\n2SQtEumbM5fKO9Dbq5PVTz45oR7YV1vLhER7TIwUioth2zaqNm8Op4/zdJVFw8gkZmQkoaK+XpcJ\nrKwM51NfXKwN/ilTBm6gFKSxEdrb9dPT03ce9K2hHhaR5MZBvHD/XBGc7wOcCsOhITdIGXp7eylK\np/xFRYl9oUtLc+or3d3ZSXGBzAsppLwCdBUVkZ/jiSOAJ55g3o9/rEZBe7vW3S0t8OijffV4T48a\nEP4eOsXFOrI5dqwaGN3dqi8iEf3f1aV1RXm57msxYQLMmxd7D55YlJTAccelPso8cyaceGLCKJuq\nq5lZQLvLb62uZm4B5dcw0okZGWEoKYFzz4Urrkget7tbXYg++EFdVSMef/mL+s02NcGaNXDRRXDK\nKRpWU6NDzEceCR/4wNBkv/9+HWJfsCB2eFmZ9sCIsOzVV1kQL95wpLgY5swZtJHxYnU1iwpIebxS\nQPktpLwCLK2uZlGuhShU2tt1M8Tjj9clrOfN0w3xfCP3+ON1TlZLC3zoQ32jmeefr6MGr7yiox3X\nXaduTg88oPX15Mlp2TzPMAwjV5iRkQx/CHv0aK3wk+HvFJqs4eu7+kQimvbs2X3pi6h/54wZut75\nUPA3ZgqRTvv27eHyaBiGYSjO4YqLYfp0eNe7tMNozx6tw8vK4Ctfgb/9TUfEzzlHv5uadO7DkUfq\nPIgtW/R3ebkaF2eeqTrBMAwjj8nDteVyRFg3olSWvgtOEAy6M4VZAjEsg5lnYRiGYaSOSF/9Hax7\nIxHVCcF5ZH5YdLxgmGEYRh5jRkYSJNXJxWGNDH+ExFcwQSMmOAFwqJiRYRiGkTmidUQsQ8Kf7xbW\nyMjHvSUMwzCisJosLGEb6qmOZCQyMtJlHJiRYRiGkXmCIxn+f1C9UFTUf0GPWEZGsj02DMMw8gir\nyZKRaqU/mJGMoqKBRkYq10x2HTMyDMMwMkO0UWEjGYZhGIAZGenH37Qn1ZGM6DkZvvExVIa4l4Rh\nGIaRAukwMqxjyDCMEYAZGckY7JyMsOfFcpeKRNJrZJjCMgzDyAypjGTE2v/IRjIMwxihWE0WhlQa\n/F1d+j12bPI0fWMi1pwMMCPDMAxjuJNOdymbk2EYxgjC9slIRix3pkR0d+t32J1WE038NiPDMAwj\nP2hv18301q+H3buhrU2Pvf027NtnczIMwyg4MmpkiMj5wK1AMfAr59wPosJnAb8BpgB7gMucc3WB\n8LHAGuBB59x13rFqYBrQ7kU71zm3M5P5SMllyjcymprg5Zfjn/vnP+susBMnwtSpmZ2TYUaGYRjD\nlBGjJ9avh4YG2L4d3npLO5pGjeozHI44IvnqUjYnwzCMEUTGjAwRKQZ+BrwfqAOWishDzrk1gWg/\nBO52zt0lIucAtwCXB8K/CzwfI/lLnXPLMiR6Pw7skxG20vfdpd55B1asgPHjB8ZxDtauhZ4eNTJm\nzFBDIxiern0ywBSWYRjDkhGhJ3wdEYloPX7ZZXqsslLdZi+7TMMnTlQDJFqn2EiGYRgjlEyOZJwK\nbHDObQIQkcXARWiPk8984Ove72eBB/0AETkJmAo8DpycQTnDEbbS90cyAMrK4GtfGxinqwtefBFm\nz4Yf/nBguO34bRhGYZD/esKvY53TOn/KFBgzRo2MCRNg2rT+cWHgyLUZGYZhjEAyaWQcCtQG/tcB\nC6LirAQuRofKPwqMEZFJQBPwv8BlwPtipP1bEekFHgC+51wG12kNJr1sGVRXJ47vj2RE730RpKdH\nv+ONVKTLXcpPxzAMY3iS/3oizMRvn6A7lE38NgxjhJPrid/XAz8VkSvR4e56oBe4BnjUOVcnA3vh\nL3XO1YvIGFR5XA7cHR1JRK4CrgKYOnUq1cmMgzgc1NVFa2sr2zZsoGvvXipra2mbOzf+CSJ0H3QQ\npatWUbllC7Uxrlvc1sZRu3axv7yczTHCx6xbx+zGRnauW8eOQcoNgHPMrqmhefx4mkOMZrS2tg76\nPuUjlt+RSyHlFUZ8foe1njho7Vom9fTQtGcPrXV11L30EtNqaugpL6dn924aAmlW1NczdfNmitvb\n2b1iBXudY9KqVVRs305ddTVj1qxhUk0NW5csIVJenrIs2WCEl7UBWH5HLoWUV8hNfjNpZNQDMwL/\np3vHDuCc24b2UCEiVcDHnHPNIrIQOFNErgGqgDIRaXXOfdM5V++du09E7kGH2wcoD+fc7cDtACef\nfLJbtGjRoDKxYfFiqkaPZt68eepvO2kSXHutrhbS1hb/xCeegM5ODjvqqIFhLS06pD5nDrNiyTV6\nNDz4IJOOOYajByk3oL1mzz0HJ5wAZ5+dNHp1dTWDvU/5iOV35FJIeYW8zm/+64m6OhqffpoJY8cy\nYcYMZpx2GqxZo+5SU6ZwVDDNd96BdeugtZUZJ54IZ54Jzc1QVcXhixZBeTns3Mnss8/WSePDkDwu\na4PC8jtyKaS8Qm7ym0kjYylwhIjMQZXGJcCnghFEZDKwxzkXAb6FriCCc+7SQJwrgZOdc98UkRJg\nvHNut4iUAh8CnspgHoLCqptTSYm6RN16a5/bUyzWroW9e+OHOxd/mdt0bcaXzrkdhmEY6Sf/9YTv\nAuXX2c5BR4euNNXYCM8+2xe3thY2b9bwN95QHbJqFezcqfFqPc8xc5cyDGMEkDEjwznXIyLXAU+g\nSxP+xjm3WkRuBpY55x4CFgG3iIhDh8GvTZJsOfCEpziKUcVxR6byAN7qUr7/rG9k7N6toxHvfjfE\nc50qK1PFcd55scNLSrTXqrFxYFhzc9/mTUPBjAzDMIYxI0VP9MM5XZ5882ZdXbCioi9s927YskUX\nCFmxAvbs0Q6plhYddQZdhaok157MhmEYQyejNZlz7lHg0ahjNwZ+3w/cnySNO4E7vd9twEnpljMp\nfmO9p0eVwU9+Aq+9piMV77wT+5zVq9Wd6oknYoe/+SZ0dsZ2uaqt1dGSsrL0yG1GhmEYw5S81xN+\nPRtcZaq9XUcrLroIPvOZvrhr18I992j4Rz8Kp58ODzygm/h9+cvs26eqwb2cNelT5s03x4bea3Yk\nYPkdfpSWqhf4cJfTyP3E7/xBRHufurtVicydC//wDxBrzgVo71VLC1x8cezwUaNg//7Y4StWqIFx\n1lnpk90wDMPIHL6R0dGhnUcicMopA+MER8eD5wHLl/f3rhqO1NRMjDkAP1Kx/A5Pxo+HefNyLYWR\nDDMykhEcDejp6fO5nTwZjj4a3vWu2Oe9+aYqmjlztPcqmq4uNVg6OgaGlZXpJPOxY9Mju5Fzmpt1\nQ+BcsnbtGCorcytDtiikvAJs2jSaApq/OHzp6oLbboOtW3Xi94wZ/cOT7Pjd26s/v/3tLMk7CJ57\nbgtnnz0712JkDcvv8GLHDvjVr/RdMYY/ZmQkI7iba3u7luzubjU24hkJoMf91Z2Wxdh0dtUqPT/e\nMoWjRw99x29zlxo2vPACvP56bmWoqZnEjh25lSFbFFJeAZqbx+VahMIl6C7V1qbzKSZOhJkzVU80\nN/fF3bdPO6vijGT4c8eHsxtISYkb1vIloqcHHnww/sKQFRXqxRbMXz7ndzAM9/z6slkfan5gRkYY\nenrUb7aoSBv+DQ2qFOrqdMwuFrW1fY6D06bB5Zf3D7/3XjVaPvvZ2OeXlQ1t4ndnJ7z1lv42IyPn\n9PTowNTVV+dOhuef38pZZ83OnQBZpJDyCvDCCw2A+Q7kBOe0Q2nrVp3YXVUFra3akr3wQv2O7nY9\n7jjYsEE7mZqa+hkZYarr9nYdGQ0OjGSLd96pYlye2rRNTfDkk6q2o1cI7unRx7dnD/3yt2bNFLZv\nz66cuWS453fvXnUUGT0aVq4cWlrDPa+J2LlTPe5Toa3toKyPeJuRkQRxTmufujo48URtKdbXqwHQ\n26the/dq7RTNQQf1uVk9+GD/sNdf16H16OPpYsOGPg1UVZWZaxgpUVxMTl14Ro2KFIwLUSHlFaC8\nPAetTUOpq6N81y4dpfC7WUtKVEcceijs2tV/EY+SEq0M1q7VehrgsMMArbLDGBmvvw5PZWfx9gHU\n1Eymvj55vOFIa6tuU3LMMf0X/QK97xs2aHiQ5uZDeeGF7MmYa4Z7fru6dJ2ErVvV0BgK2cxrT48a\nsOkagYnV5EzGpEmH8r3vpef6YTEjIxn+RD2Af/xHdXFatQqOPx6++EWdc/HAA7Bpk87TCDJqVN9o\nxL59/cPa27XURR9PF1OnwoQJcMEFMGZMZq5hhMaGdg1jhLJlC8Xt7dqt6Ps6jRmj/9va4NRTVXfM\nnBk/Da8jKOxIRne3fn/ta2mQP0WWLKnjjDNmZ//CaaChQW28iy+OPWm4o0OdAIK8/PJaFi5cmB0B\nhwHDPb9NTXDnnXD++TotdihkM69r1uhio5MnD313gt5eqK6GWbO0HyMsPT11QHaHIc3ISEZwXkNl\nZZ9/bXm5NuLHj9deqjlz4POfD59uZaXWZqmcY+QtYRsPhmHkH66oSPXAzJmwcCEcfrj65Zx/vu6n\nNH9+qL0vwtYTfrx43rqZpKqqJyfXTQetrdr356vuMGze3MmsWZmVazgx3PM7Zow+u0MOYchyZjOv\njY0q95e/zJDdDTs71dA491x4z3vCn1ddvWtoFx4EZmQkoV9977tI+bPzfHPU36QvFazVWVDY4zaM\nEUpwmHLcODUwjjhC/ZkOPhhOOy2lpFIxMqJpa4Of/zz2eiQi8MEP6jTBQsX3ILYN1fMXv9znYj7S\nUPCnZQ11PR/oq3LyoRybkZGE8oYG7f4oKYGXX9axuoYGWLJEjYsxY3SOxqRJ8Ne/hk94167cdEMZ\nOcGMDMMYoURvxudvyAcptwLC1hORSOykW1rU0Jg/X1VSkJde0smihUw6G3pGbgguypZPpLPs+QZW\nPrQpQhkZIvJn4NfAY865PLMfh4BzVGzfrg6wkYgOf+/bpy5TIrq8h79ExbhxsHFj+LRF1MXKKBjy\noUIwjMFSsHrCx3/Bg0ZGii/9UEcy/MbH8ccPnHOwbFn+9f6mGxvJyH/y1cjwy56NZMTm58BngJ+I\nyJ+A3zrn1iU5J//xlyYsL9eJeYcfriMaHR262/d3vqMrSBUgPT26BUj0JLl8YtWqiSkvATdYli/X\nRcgefTQ714tFNvObawoprwDvvDNuOGzGV5h6IpocGhmJLltUZEaGjWTkP/lqZPhlLx2GwYgbyXDO\nPQU8JSLjgE96v2uBO4DfO+e6Myhj7ohEKPZXCOnu1q6g8vK+TQ8KuKbavl03mCsry9/bsHnz6Ky9\npFu26GIzb76ZnevFIpv5zTWFlFeA3bsrkkfKMIWsJ4D+LfwcuUsl6qkXMSMjnQ09Izfku5FhIxlx\nEJFJwGXA5cAbwB+AM4BPA4syIVzOiUQo6u7WJ1paqqWjqkqdXRcu1CUqChS/kF9yCcydm1tZBkt1\ndS2LFh2WlWstXqzTeb70paxcLibZzG+uKaS8AlRXNwBH5VqMwtQT0QRb8zlyl4rV+LCRjPS6rBi5\nIZ+NjKKi9Iw+jLiRDBH5C3Ak8Dvgw845f4/EP4rIskwJl3P8PTKKinTXl6oqnVF38MFwzjn5YUZm\niEF6AxQsNvHbGOkUrJ4gahXC/ft13t4gXnozMjKLuUvlP37ZzkcjI13lLp/aX2FHMn7inHs2VoBz\n7uQ0yjO8iER0x29QxdHe3rdzaz483QyST4V8uGD3yhjhFKaecI4D7Z22Np2sVlmpLfrgTt/hkgpt\nZMQyJGxORmJs4nf+k88jGek2MvKhHIcVcb6IHFhvVUQmiMg1GZJp+BCskdvb9dtfZLzAW4xmZKRG\nvlWIhjEIClNPBF/unh79XrAATjxR3WpTTCrsnAwbyUgdG8nIf/J5n4x0lbsR5y4FfME59zP/j3Ou\nSUS+gK4mMnLZto3ijg5VHMXFun/73Lk6izcfnm4GMSMjNcxdyigAClNPBI0M70VfK0fT3ruPE8pH\nkcprH6+eWLtWPbCC/+vrdeumIFu2QG2trmZXW9s/rKZG54VFn5Mqq1ePpbx8aGnkiq1b9TsfeoCN\n2NhIRn6NZIQ1MopFRJzTrIlIMZDaOHA+8tRTFPlGBuBGV7E/UgFdpbA/vuooL099A/B8w4yM1DAj\nwygAClNP+KPcHj2U8MeXZkJ7O9MaYNq08EnFqie6u+G++/o3qt5+W42OJ57oH3fXLt2u6fnndQph\nkLVroaKirzd/sNTUTGTXrqGlkUsqK/u2tzLyj3w1MiIRG8lIxOPo5L3bvP9f9I6NbPzlAMrLobyc\nJbMu5enfT4ONp9PYVEZnnMp69Gi49NLESZeUwLHH5q8xYkZGapiRYRQAhaknoN8LHinqq9Sj7I9Q\nyUT3Tvb26vH3vQ9OOUWPPfgg1NXBddf1j7t2Ldx/P1x1FUyd2j/sjjtgzBhdEXAoPPfcFs4+e/bQ\nEskhJSXmLpXP5KuRYSMZibkBVRj+ApxPAr/KiETDieZmirq6tFQXFbG3axSjyiKcPXc9d3W+Bxej\nwOzZoxuBjx2bvFE5ahQclftVJweFGRmpY/fKGOEUpp7waOmppGb3IexnCluKHMXdwuLFfSMZRx+t\n0zR8IpG+KRw+7e26wWlrq/7v6YF9+/R4e7vOKw/G8//7+Fs6BeP6dHfrsaam2PKXloaro9rbiwak\nPRLxn8/u3SU0NCSP7692D3ofS0szK1+mCJtfCF9m0klPj5bv5mZtbw2FvXuLh5xGWPbt0zLiv9tD\noaUFurp0PaJU0uvszL5VEnYzvgjwC+9TODQ0IP4YV3k5PaUVjHK9nDRhG08Vw6Jz++aB+7z4oi4u\n8vWvx7daGxvht78d+rB1LjEjIzVsJMMY6RSsnvB8F/b3lNLV2Us3JTox2wmvvKJR2tp0hDtoZCxd\nOtAQ2LlTGyIPP6wNiJ07NfmWFrj7bg7MhWhrU/1x++39z/cbHr/73UAx9+/Xc264IXY2iovVlSgZ\nnZ2n5u2cjLA4p/fYOejpOS2px0Fv78CJyCUl+empECa/PqWlWq6ziXOwdy/cddfQ3d46Ok7Jqutc\neTn89a9DT6ezUzdEfuqpcO+sT1XV4Zx33tCvnwph98k4ArgFmA8ceCTOuTzdhi0kzc1Id7fWFCK4\nde9AmcMdAQ6homKg32tVla5aWFkZvyejo0O/8224L4gZGamRz8/aMMJQsHoCkN5eRvd2cGjXFiZU\ndPF0RQvTpkY41hvTefxxNR7e//6+c7ZtgylTYPr0vmPLlqkxcNZZsHkzrF4Ns2Zp59WYMTB+fN+5\nvb3qQhWsg3fuhHfe0KNInQAAIABJREFUiS1jV5c2TmLhGyCjRiVftaeoqIeKinBWRlGR7lmbj3qi\ntlbnsPT0dDB6dFXS+KWluk8v6NyYsWP7P9t8YefO3Rx00MEh4qlhetxxWRAqgHPw0kswcybMmDG0\ntGprdzJjxsz0CBaCiRPTs4dzU5PWCQsWaB0Slt27G4FDhi5ACoS1s38L/AfwI+C9wGcIv/xt/tLU\n1Nc6dA7X0YmUC73zjiZSUxJ3LXJIXFHnq09hEDMyUsNGMowCoDD1RHc3JW1tRFwxkaIi3OQp0F3G\n8WeUsOB0jbJ5s7p4nH5632nPPKMr3L7vfX3H7r1Xe2mvvhpeeUU7q666Cq65Rhusxxyj8V5+WUfE\n//3f+wwPgDfe0J7Sri69VtgVdB9/HF57TXtax49P7OqzceN2Djvs8KRpdnbqCMxnP5taQ2g40NkJ\nP/4xvPe90NW1gjPOOCPpOePG9Xkv3HqrNoAvvjjDgmaA6uq3WbQouZGxeLE2kb70paRR04pz8J3v\nwKJF+hkK1dWbWLQoe0ZGuti6Vd/xyy7TBU/DUl3dmDmh4hDWyKhwzj3trRyyBbhJRF4HbsygbLnH\ntxQmT9bPSSch006h66PAj+OvRR48NRFmZBQOZmQYBUBh6omGBqS7m2IXobdiLG7uYTD2FAi40ooM\nrO8jkYE6JFhPBPq3cA4OPrivQbdvn7rlRs/pCOqdykrtOQ3DuHFqWFRUwBVX6Grt8aiurmPRouRG\nxpo1uirW+PHh5RgutLfrvZgwAdrbe1KWv6Rk4LMZaeQqjyKx36dCYiSuLtUpIkXAOyJyHVAPJB8/\nzHec0zXO/SVsvRXPk+2qCjaSYfQn3g69hjGCKEw94e2M11U0iu0zTmHSUUfDtv51Y3SjyDccouvP\nWEZGJKK/o+f4icQ2Mgaz8kxxcfo3qkulw2244d+LwdbZwfs5UsmlIVVUBC+8oG5TQ2Hz5pm8+GJ6\nZMome/boqGVra2ruV/v2TR3y6E+qhDUyvgpUAl8BvosOhX862Ukicj5wK1AM/Mo594Oo8FnAb4Ap\nwB7gMudcXSB8LLAGeNA5d5137CTgTqACeBT4qr8ue9rxNYE3fd+VlvVTFmZkmJGRCnavjBFOYeqJ\nzk4kEsFJEV3lo3FFxZ5cwTwMNDIg3EiG/zvY+C8q0njRDVnfyPB7e8MSbBSnqzMkn/XcUA0uMzIy\ny4c/TFr2aikv38e73z30dLLN9u06J+aEE+Cgg8Kft2HD/swJFYekRoa3odI/OeeuB1pRP9ukeOf9\nDHg/UAcsFZGHnHNrAtF+CNztnLtLRM5BJw1eHgj/LvB8VNK/AL4AvIoqj/OBx8LIlDJ+7VhWBscc\ng5t6MOL6DIjBukvlc+XrY0ZGauTzszaMZBS0nti/H5wjIkW0Fo/nF0/P48Xd6nJz/PF9y3wG64B4\nOiSRu1S0kQEDG3l+3FhpJ6KoqC+tdBkZ+TySkUjHh8HcpTLL8cenJ53S0qas9+yngw0bYNMmXSAi\nlcnvpaX7MidUHJK+Qs65XiD5rKeBnApscM5tcs51AYuBi6LizAee8X4/Gwz3eqKmAn8PHJsGjHXO\nveL1St0NfGQQsoUjWFuPHn2g9g8zkpGoF2MkNMzNyEgNm5NhjGQKWk9EIkgkQgtj2RmZxJ6OSoqL\ndVKsv0RtvJGMMO5SsYwMf6Qi3pwMb2un0Pg97+l068xnI8NGMpJTXDzyDanhykjcjO8NEXkI+BNw\nYGVv59yfE5xzKFAb+F8HLIiKsxK4GB0q/ygwRkQmAU3A/wKXAYG1NzjUSyeYZoIpakMk6BSLzskQ\nsZEMMCMjFo2NuuJDLJqadCLh9u3ZlSlIY2NZTq+fTQoprwDNzcNi56/C1BM+InD8SVB0GFU1eijY\nGEh1JMOP4xsZwb0LEhkZfhqpjmQ4pxPKd+6Mv9QthH+3du/W9HbsyL+N6XzZGxsHV5e0tOhIVj7W\nQWHzu3evboj3xhv52w7YsGF0v9XZ8oW6ur53NZX3vKUl+xu3hL3iKKAROCdwzAGJlEcYrgd+KiJX\nosPd9UAvcA3wqHOuTgZZekXkKuAqgKlTp1JdXZ1yGsc1NjIe6G1tZUd9PW+P2sieyB5efHE3NTWH\nsHTpTnbu7O/jtnlzJTU1B/HCC/VMmNAdM939+4upqZnB0qW7aWkZ2vaP3d3C2rVj6ekZ+lve3l7O\n8uUrQsWtrx/FW2+Np6RkF5WV+dllk0p+k9HSUsLLL0+OG15bW0FxsePhhzvScr3B0N09g9tvD7mV\na55TSHkFGD16KuPHV+dajILUEwva2ih3johzbGrZy5b929i9u4LOzh5eeGETY8f2sGbNFPbsKaO6\nup5XXpnIxo2jeeONCaxa1cbUqX0t+nXrxuAcLF26j/r6CrZtq2Dz5r1s3jyJ6uq9dHXpEpQvvDCZ\njRvH8Otfb+fgg/vqlI0bR7Nu3RiKiqClpYWXXgrng711ayUrV05k165RrFzZRklJ/B6wsO9We3sx\n9fUVvPZae97piO5uYc+ecjZtaqOiYjIPP1yT0vmbN1fS2VnM6tXZd08ZKl1d4fK7a1c59fUVPP10\n5mXKFL29cykubs61GIOmvr6FUaPCDxUedFAlY8dWZ06gGITd8TuUf20U9UDQW2y6dyyY7ja0hwoR\nqQI+5pxrFpGFwJkicg26OkmZiLSiPVnTE6UZSPt24HaAk08+2S0ajOPdhAlEgJKSEg79xCc4svhU\n9jQJCxfOY9UqWLBgNkcd1f+UqVNhyxZYuHA2B8dZarq1VdckP+mk2Zx8cupiBVm3TjdlSXWiXyw2\nb97MnDlzQsXdv9/fXfbgvBwOh9Tym4zmZr0fc+fG3gG1u1t78448Mi2XGxR1dXVMn558/fORQCHl\nFaC1dRODquPSSMHqCSACSFExs48+hs5dk+jp0VVfTj/9ECZN0h7xbdvgrLOO4H/+R3u6GxvBuYk0\nB9o4fg+yyFSamrReqa2d6I1QjKW1VW9VV5e64zQ2jutX7+/dq6MQxcXQ0XGwv2ZJUsaNg9mzdTTj\niCOqKCuLHzfsu9XSorLMnVvFuHHh5BhOlJTAxz42no0bX2XBgujBtcQ8/riO4Fx44aQMSZc5li9f\nzonBrenj0N2t5TWfPTLWrFnN/PnH5FqMQVFWBtOmpTYMs27dzqzribA7fv8W7ZHqh3PuswlOWwoc\nISJz0Ar+EuBTUelOBvY45yLAt9AVRHDOXRqIcyVwsnPum97/FhE5DZ3QdwXwf2HyMCi8t6fHFbF6\n96GsbRL27dONkLZsgeXLB65wsHWrGhFDdZfasQPWr08uYk2NyvKRjwxuLfJRo+Ckk3TIrbp6C4sW\nhWt0+5s+/fM/k5cKBFLLbzLeeQf+8Af4whdirzF/2226Y++nPjUwLFtUV29g0aI83IJ2EBRSXgGq\nq7cCud1Yu2D1BNBNKXuLJvD8q6Ooa1J3m9Gj4Y47oKoKVq7UToZnntF5GgsXqu5YuBCOProvnb/9\nTeviCy6A11/Xuv1HP9LP3LlwkTcb5ec/hyefHDjp8/DD1VgoLoYLL9S6PSyvvgqPPQY33KCunfEI\n+27V1cGvfgWXXgpHHBFejuFGd3d7yp1D69eri+zjj2dGpkxSUzORnTtzLUV22LJldN66eoHWK6nQ\n1jY2M4IkIKy71COB36NQv9htiU5wzvV4a6U/gS5N+Bvn3GoRuRlY5px7CFgE3CIiDh0GvzaELNfQ\ntzThY2RqxRA4YCl0dJewfIXwptcz45zu4PryywMNgcZGaGjQ8L/9LbYh0dmpCqSzUxvrJSUDjYTq\nali7NrmIO3fqtZYsid2DHobp02HatNTOsTkZ/Um2/KNN/DYKgILUE8459lNJU3cVy5fDnk7o6FCj\n4qGHtMdx1y7tZBg3TuuBTZvUECkt1REOnzVr1EAoK9M4/ihH9MZ9ZWVw5plw7rkD5Skq0k3wUp0U\nOtQVlaIZCXMPB8t73wtpGiTPOkuX7uKUU2bnWoysUEh5BVi1KvuuYWHdpR4I/heRe4ElIc57FF0+\nMHjsxsDv+4H7k6RxJ6os/P/LgGNDiD10ensRdML3YYcJH52tQ8Dnnw933qk9NIcd1v+UW25RpbFl\ni34fHmNj1JISVS6jRqmyqKmB+vr+RkZvrzb8P/e5xCKuWgUPPgjXXguTUhyZ3bgR7r13cKtgmJHR\nn2QK2owMY6RTqHrCb0OXFfVy6gJh7MFa/0+YADfeqIbF5z8PU6bA178OP/2pjlSXlenxoGfKr38N\n5eVw2WV6bjwjwzl1zT02Rg6bmvQ7VWMh3ftk5PPqUkOlqir2s8kHdu9uy1vZU6WQ8gqwe3f254QO\ndqr5EUAKW4DkKY06yc4hFJcKRUXay1RcrBVoaWn/FT9A/zunFXZxsRoi0bS3q1vVeefBvHnwk58M\nrIh9pRKdfjS+LGVlyeNG46/4MRglYEZGf5IteWhGhlGAFIaeCPTU+/qh5P9v797j5K7q+4+/3rO7\n2d3cQ4JrIOESCWIsl2KKoKCRR0Vs/amgLfJrK7S2FK0+annQCg9b2vLQem29VH5V2tp6qSJitdQb\nUHXBCyJBckOaEEIgCSGRQMg92eyc3x/nzO7s7Mzs7OzMzs7M+/l4jPnOd76Xc9bh+5nP95zvOZ3x\n1d0N06cPT57X0TE8nK00+rqdH1fyh70tTDLSJONF5Q9jOx61bslo5yTDzKJKn8nYy8i+tk8B76lL\niaaSZ54B4OC0WXR0aCgwlLsYd3TEwHD0aOmLfH4zcqkLcWFQKaXagAITCwJOMkaqJED7b2WtrG3j\nRKpykAjE/8gLr/H5cSNfuSFs868XxVoyxkoyGt1dKnecduwuZWZRpd2lZtW7IFNVAI5qGpmO4eAx\n1mR8IcTnLUpdrCtJMiqdFGkigcFJRu1U0l3KrJW1bZwY+m9bI/77z2+xgOE5LGB4feH1M7+FolxL\nRrkko9qJunLnrtU1vXC+DzNrPxVdhiRdImlO3vu5kuo3g+oUlOnUiAABxS/GPT1j/wDPX1/qbk+5\n5vDC7fKPMx5OMmrH3aWs3bVtnAiBDgbJErvWFs7anZsYrzDJgLFbMsp1lyp1zZ9IS0YtZxB2dykz\nq/SS8tchhOdyb0IIu4G/rk+Rpp4AFXeX6uyMnw8OTm53KScZjeUHv83aNE4cPgLAoIantS4cWSmX\nZBQqvCbUsrtUNc9k1CPJcCuuWfuq9FHhYpeeyZ+fvGE01JIxVneprq7hB78nkmRU+qO0FkmGR5ea\nOLdkmLVrnAiAeKLzZJTRiGtjfpIRQnUtGbnXZDyTUer6VY3csb7xjTinUrPavPlE7r670aWYPO1U\n33aqK8CRI31M9pytlQaAlZL+Abgpvf8T4IH6FGmqEbGv7fAVfayWjLEe/M6pRUvGRH7suyWjdvzg\nt1n7xolBMgSGf6GXasmoJskodm2pR3epwcHatmTMnAm/+Zuwd2/tjtkIs2Y9x1lnNboUk6ed6ttO\ndQXYtGn/pJ+z0iTjXcBfAV8h3ra5i8omRGoZylBRS0YuySj3TEV+APrc5+CHP4Tt2xmRUd93X3y+\n44knypdr82bYuDGed7x3ofbvjxMKPvssPP/5sGnTCdx7b2X7Pv54nOX6wx+u7d2v8ejogMsvHz3r\nbSO4u5RZe8aJewfP4Q5W8PCRs3hiTQfTeuMP695euOKK2Lq9ZUsczvbjH4/X9r1748zchdeEAwfg\nm9+M1/RHH43rDh6M1+jt2+MEfQADA/V58LuWSYYEv/ZrtTteo2Qyuyf97m8jtVN926muAJnMvkk/\nZ6WjS+0HrqtzWaaevM6k6siMK8k4enR4HopCuf0OH4atW+NMsEuXwumnD2/z5JPxTlD+RE3FdHXF\nZOHss8f/Y3/v3piknHYaLFkCHR17OfPMyvadNg327avuvLUwMAArV8bZ1adCklFJdymzVtauceJJ\nFiICJ3Zs4eAC6J4Rr48zZsDs2fGH+65dcVK+M86IycOuXXFW8MIf9fv2xaQiN4nr7Nlw552wenX8\n7Be/GN62p6d4eabKMxlmZpXOk3EX8FvpQT4kzQNuCSG8pp6FmwqeYR7v2v9Bnv3YUp5NPxTnzo13\nlX7ykxhM8j31FDz2WPwsm4UPfaj4cffuHW4+DwG+853hz3JJCsBHPlK+fLkfr7feWjyolHveInfu\nL30pt+b00huXcMst496lZrJZuPHGiRzhFbUqypD3va/4+lyC2qhWH4Bs9oK2+RHRTnUFyGRezsGD\njS1Du8aJgJjOAZZ0Psz+RYHpc+CXv4wJwoIFcTK+hx+ON3RuuCEmGAcOxFaJiy6Kx9i3L14jBgbi\nNXvVqpiEHDgAX/lK/PG/f388Rs7PfgY33VSkPOk4AwNx1vFKHToUb2yZmdVKpd2lFuQCB0AI4VlJ\nrT+TK3CQHvaF6Rx/7GG6M7BnDxxzTLyQn356DCD59uyJSciTT8aL9vHHFz/uE0/EO1Y9PaMnajp6\nNDaPd3aWvluVL5OJTfHF7N1b+sdtLhgNn3eQzs7Kf5lVMiN5PR09OrHhEcdb37FIpVuvICakjUwy\nDh06Qk9Pb+MKMInaqa4AmcxhoMyXb3K0ZZyIN3pCnIhPGtXS/epXx+vs2rUxadi4McaJefPghS+M\nScfOnTGu7NsXk4lly+K+u3bFf3PbVhIPIF6HTj99/NfnE08c3/ZmZuVUegnKSjohhPAEgKSTGDmz\na2sKcdSQXh3k/5z/LPfv6ePRR2H58phcvP3tpS/6X/96fG7h3e8u/vmtt8a7XX9SpMfyxo3wrnfB\nG98If/zHE6vChz8ML35xfABvLP39P2ZFG3VQbL/63tc29W2nugL0968EVjS6GO0ZJyjeL+ngwZg0\nLFwIb3tbXLdjB3zhC7B7d2zFeMUr4Ec/iq0S11wTu0Pdcw9ce61bFcys+VWaZLwX+JGku4lX1AuA\nq+pWqikkpNGlSM9idHXBe99bwX5jzNg9OFj+LlOlM35XUg4/cGxmk6BN40S8GQUMXWx37YKnn44P\nf3/608Nb7t0LDzwQE5AjR2JSsWlTfDA8t10mU3mLhZnZVFbpg9/flbScGDAeBL4BNLgH8OQYoIuj\n2Qwr7xer9sfA8ZrXDDdj5xxzDMyfP/x+y5YYSEqN1nTgQHzgu1h3n50740PhtUgOnGSY2WRo1zgR\nEGL4QpubJwliK/Jllw1vu2NH7Ea7Zw+88pXwspfF5/fWrBnebvbsxnZDNTOrlUof/P5D4E+BRcAq\n4FzgXuDC+hWt8QIwmMY+nz4TutPcF7t2xRaNuXPjdnv3xrtS+Q9ZDw4Oz/xdTHd3DCa//OXoz555\nZvgYP/5x9eXv66tdi4iZWTntGicOhh52cQzPHe1m65PiwME4MMiBA/H5itNOG77RM3s2HHtsTCJO\nOgle9KJ4Q2rHjrhsZtZKKr1f8qfArwE/DSG8StJpwN/Vr1hTw4GBLgIiG8SRgQxdXXE+id7e2J/2\nHe+I2915Z7wblS+EkQ/wlVKsJSM36tT69TFYVWvGjPLzdZiZ1VBbxoknswvZwfPZPdjHxkcz7N0b\nE4yBAXjwwXhTasGCuG2xoax9jTazVlVpknEohHBIEpK6Qwj/K+mFdS3ZFHBosDM2hQsGjg5PshfC\nyObsV78aXvWqkft+9asxuFx99fjPu2FDHBr2D/8QjjuuurLfeWcczcTdpcxskrRlnBgMGXo5yNm9\n95Jd+hs89VRssVi7No4ol38jKT/JyJ+U1a3NZtaKKk0ytkqaS+xje5ekZ4HH61esqSEAzzGH7RzH\nvL2z2LIrDkE6ffrIoUqLDV2aG9613JCmpeRm7+7qqm7/3DFyCZGTDDObBG0ZJ3I6MvG6n8nE5+2O\nOSZ2iRprIk63ZJhZq6r0we9L0uLfSPoBMAf4bt1KNVUE2M0cDtJDSOOf5/rSjnXnqRY/7ieyf25m\nct8lM7PJ0LZxIskqM6LVYvr00fMXlWrJcJJhZq1o3GNYhBDurkdBpqQQRw7p5Cjnnb6fvY/EZvAz\nz6xg1wkEjrHufFUik3FLhpk1RjvFidzoUoOKg4TkrreZzPCNnqFtSzyT4RtBZtaKfGkrIz8eZGfM\nGl5fwQ/3Rv+4d5JhZjYJQgACQZlRLRWV3DDyNdrMWpWTjDLiHaoULGbOHgoYlQaOau9O5Y5fi+5S\nEz2OmZmVFtJEfNmOaUWv+4UtGYXXZbdkmFmr8qWtjBBEALLqINM18k/VDC0ZxZbNzKyW4s2o3Ud6\n2blz+AZPrrtUvmI3fhodK8zM6sU/P8eQu0v12Gaxc2flXZBq8UzGRFsyii2bmVntHcl20tMDJ544\ncn2pZzI8hK2ZtTpf2so4SudQl6lHNordu+Hgwcr2bfTdqfyg5STDzKx+RCBLBz09cehaKN6SUYyH\nsDWzVlXXJEPSxZLWS9oo6boin58o6XuS1kjql7Qob/3PJa2S9JCkq/P26U/HXJVez6tX+WN8ECiQ\nycQokOtTO9VbMpxkmFkzaPY4kUVAYDBkig4dXqwlQ/IQtmbW+sY9hG2lJHUANwGvBrYC90u6PYTw\ni7zNPgp8PoTwOUkXAh8Afg/YDpwXQjgsaSawLu37ZNrvd0IIK+tV9ny5loxcFDhyBL7//ThZ3o4d\npffbvx9OOGEySlicn8kws6muVeJElgw/HzyTpx6HwUGYMSOGjJ074eabYc6cuN3TT8PKlXD4cIwl\nK1fCgQMwf/5klNLMbHLVLckAzgE2hhA2AUi6BXgDkB88lgHXpOUfEGeKJYRwJG+bbhrUrSven4pN\n4Zk4BDqHD8O+fXDyyXDKKeX3P+206s5bi5aMF74wBrTcspnZFNT0cQJi19oDoZfubjj2WFi4MHab\nOnQoPqOxYEHcbsYMePTR2O120aLhGLJkSaNKbmZWP/VMMo4HtuS93wq8tGCb1cClwCeAS4BZkuaH\nEHZJWgx8CzgF+PO8u1MA/yZpEPga8L4QajF93Whx9PP4S18Fv/hf/GJ4/evrcdbamDcPXve6RpfC\nzKysFogTIkOWbBAzZ8abOosWwWOPQVcXXHRRfA/wyCOwbVu8UXX++XDBBfUokZnZ1FDPJKMS1wKf\nknQlcA+wDRgECCFsAc6QdBzwDUm3hRB2EJvAt0maRQwevwd8vvDAkq4CrgLo6+ujv79/3IU79ehh\nAEI2y65dOzlwYCbPPnuYQ4eybNjwNP3928d9zEps2jSDzZuP5Sc/2cacOQN1OUcx+/btq+rv1Kxc\n39bVTnWFlq/vlI4TWURAHDgsHt9wgBNOeJr9+w+wfXsPnZ3wk59s53nPi7Fky5ZeNm/u4+DBDh58\n8GkGB58b9/karcW/a6O4vq2rneoKjalvPZOMbcDivPeL0roh6a7TpQCpT+2bQgi7C7eRtA64ALgt\nhLAtrd8r6UvE5vZRwSOEcDNwM8Dy5cvDihUrxl2BDWEWIDIZ0df3PLZvh7lzpzNjBrzoRfNZsaI+\n/ZDmz4cnnoCXv/ykoWb2ydDf3081f6dm5fq2rnaqKzR1fZs+TtzFj9KzeyKbnU5Hxwmceir09sbn\n4c4996Sh5/M2bIitGfv3w9lnL+b888d9uoZr4u9aVVzf1tVOdYXG1LeefVjvB5ZKOlnSNOAtwO35\nG0haIClXhuuBz6b1iyT1puV5wPnAekmdkhak9V3A64B19arAfmZwkB56dWjESCDx/PU6q2fqNrO2\n0fRxItepdn/XXDo6hudSKrplKP2ZmVmrqVuSEUI4CrwTuAN4GLg1hPCQpBsl5Z5mWEEMChuAPuD9\naf2LgPskrQbuBj4aQlhLfLjvDklrgFXEO17/XK86PJ5dxHpOpTtzZNQP/o6Oep3VzKw9tEKcGBaD\nRDYb35UbwhZ8E8nMWl9dn8kIIXwb+HbBuhvylm8Dbiuy313AGUXW7wdeUvuSFndH9iJ2MY8jHdOH\nkopK58moBQchM2t1zR4n4viDMJjViHkycstF9/C13czagGdQKCONK8WhrlnAyKbues494eZ0M7Nm\nEQgp0ShMMqB0S4aZWatzklFWjAgD2Q727Rv5yWRMcOe7XWZmzSEQWzIKu0uN2CbvRpWv72bW6pxk\nlBXvUB040snjaSbXnMl48NvMzKa6GAxycypV0pLhBMPM2oGTjDKUWjIOZ3ro6oLubjjhhBgo3JJh\nZmZH6OIQPWTD6GcyyvH13cxanZOMMQTE3sGZ7NkDnZ3DrRl+JsPMzAKZoef3Mpk4m/fOnWOPLmVm\n1uqcZIwhIAYRAwNw+DAcPQoLFngIWzMzy3WTCiCYNSvGhq4uOPHEItvmdZdyS4aZtbq6DmHbKubN\nGGDRoi46O+FVr4qJxmS0ZDgImZlNbRkGEYHuriyLF8NLXwp/9Edw6BCsXz+69cKtGWbWLpxkVEAE\njhyJycUzz8QuUz098MQTle2/YAFMn17fMpqZ2eTLpAFCDtLDwYPw3HOwdWts+X7uOVizJnafAnj8\ncdizB3p7G1tmM7PJ4CSjAtkg9u6Nz2Rs3Ag7dsCcObB9e2X7n3IK/O7vVn4+t2SYmTWHLBkGmMaB\ngWns3g0PPhgHCTl6NC4/+ODofc47L25jZtbKnGSMISCyQcyZA8uWwZveBPfeC4sWwUUXjb3/XXfF\nO1pmZtZ6QnpN7xzgssu6+fVfh+c9L94s2rEDjhwZuX1vL/T1weLFjSitmdnkcZJRISm2Xhx/PMyf\nD89/PixZMvZ+06ePP8lwS4aZWbMQAjoygeOOg3PPHf7kBS9oWKHMzBrOo0tVIDfmef6QhJUmAJnM\n8AywZmbWWkLe/3b6tp2Z2RAnGWMIiAOH45+po2P8rQy5yZnGdU63ZJiZNZ2lSxtdAjOzqcNJRgWy\nIcOSJbFV4uDB8bVMVJNkmJlZc5nTc5i+vkaXwsxs6nCSUcZcdhOATIe48MI4Gsidd8YZXSttFndL\nhplZKxNK3aU8YpSZ2TD3IC3j+WxnE0vo6c5y2WWwbVtMMCAOS1sJt2SYmbWu3OV91uw4J5KZmUVO\nMsoIxKaEnmlqSB5yAAAWcUlEQVRZMhk49dTxH0Ma/4PfbskwM2sOgymMdk8bHhzEzMzcXWoM8Vd+\nRtX/4M9k3JJhZtaqAhpKNHxjyMxsmJOMMQSACSQZE+ku5YBlZja1KaUZ4Gu2mVk+JxkV0CQnGW75\nMDNrFoGjdOUavs3MLHGSMSahjNySYWZmRQxfqP1MhpnZMF8SKyBUdfBwS4aZWWvLkKVv7mHfGDIz\ny+MkowJSmNCD3+MdXWr4vNXtZ2ZmZmbWSE4yyhigk6N0+pkMMzMrKjfUufCF28wsn5OMMvYyC4DO\nrsaMLmVmZk3CLc9mZiPUNcmQdLGk9ZI2SrquyOcnSvqepDWS+iUtylv/c0mrJD0k6eq8fV4iaW06\n5iel+nUqGqQDgNkzBqs+xkRaMtxdysxaXbPHiaFz1vsEZmZNpm5JhqQO4CbgtcAy4HJJywo2+yjw\n+RDCGcCNwAfS+u3AeSGEs4CXAtdJOi599k/AHwFL0+vietUh1wyeyVQfPtySYWZWXGvEiRynGWZm\n+TrreOxzgI0hhE0Akm4B3gD8Im+bZcA1afkHwDcAQghH8rbpJiVDkhYCs0MIP03vPw+8EfhOPSqw\nj5kEoGMCf6VMBgYGoL+/8n2eeCL+65YMM2txTR8ndrGAfcxkpq/XZmYj1DPJOB7Ykvd+K/FuU77V\nwKXAJ4BLgFmS5ocQdklaDHwLOAX48xDCk5KWp+PkH/P4elXgKF2AWLb0CNBb1TEWLIDBwfElGQDz\n5kFHR1WnNDNrFk0fJwLiEL1Mr3IUQTOzVlXPJKMS1wKfknQlcA+wDRgECCFsAc5Izd/fkHTbeA4s\n6SrgKoC+vj76x/srH+jhEN0cYv/erfT3Pzju/XNe+crq9rvnnqpPWZV9+/ZV9XdqVq5v62qnukLL\n13dKxwmATgYIR56iv/+RqvZvJi3+XRvF9W1d7VRXaEx965lkbAMW571flNYNCSE8SbxDhaSZwJtC\nCLsLt5G0DrgA+HE6Tslj5u13M3AzwPLly8OKFSvGXYFvcj/TGGDhicexYsWLx71/s+nv76eav1Oz\ncn1bVzvVFZq6vk0fJ77IPyMGmTH/NFaseMm49282Tfxdq4rr27raqa7QmPrWc3Sp+4Glkk6WNA14\nC3B7/gaSFkjKleF64LNp/SJJvWl5HnA+sD6EsB3YI+ncNFrIW4H/qlcFsoh9zCTjhyPMzOqhBeJE\nBg0NE2JmZjl1SzJCCEeBdwJ3AA8Dt4YQHpJ0o6TXp81WAOslbQD6gPen9S8C7pO0Grgb+GgIYW36\n7B3AvwAbgUep08N8MDyE7fReDw9lZlZrrRAnAiJD8OBSZmYF6vpMRgjh28C3C9bdkLd8GzCqD20I\n4S7gjBLHXAn8Sm1LWlxId6iWnFD9PBlmZlZas8eJSM4xzMwKeMbvsmILRnbatAaXw8zMzMyseTjJ\nKCPemQqo20mGmZmV4aYMM7MRnGSUEdsx5FnxzMyspIDDhJlZIScZFcg4eJiZWRnCA4SYmeVzklEB\n36EyM7NihlILxwkzsxGcZJQx9ExGg8thZmZTVYwQZ57pSGFmls9JRgUynQ4eZmZW2uJFY29jZtZO\nnGRUoqOu04mYmVmz870oM7MRnGSUkU1RQx3+M5mZWWkeIMTMbCT/eq6AH/w2M7NRwvCIUnI0NTMb\nwZfFCjjJMDOz0oLjhJlZAT9sUIFpnvDbzMyKGKQD8CMZZmaF3JJRRi5o9PU1tBhmZjYVhTD07N68\n2YMNLoyZ2dTiJGMMcjO4mZmVJLo5RFdXo8thZja1OMmoQMZ/JTMzK0m+GWVmVsA/nyvgJMPMzMzM\nrHL++VwBJxlmZmZmZpXzz+cyciOguxnczMzMzKxyTjIq4CTDzMxKEcGBwsysgJOMCri7lJmZlRbG\n3sTMrM3453MFOjoaXQIzM5vK3JBhZjaSk4wxBA9NaGZmJQSE5/s2MxvNScYYRHBLhpmZmZnZODjJ\nqIBbMszMrCwHCjOzEZxkVMAPfpuZmZmZVa6uP58lXSxpvaSNkq4r8vmJkr4naY2kfkmL0vqzJN0r\n6aH02WV5+/y7pMckrUqvs+pZh3jOep/BzKw9tUqcMDOzkeqWZEjqAG4CXgssAy6XtKxgs48Cnw8h\nnAHcCHwgrT8AvDWE8GLgYuDjkubm7ffnIYSz0mtVveqQe5ivt7d+ZzAza1etESci34wyMxupni0Z\n5wAbQwibQghHgFuANxRsswz4flr+Qe7zEMKGEMIjaflJYCdwbB3LWkJABLq6Jv/MZmZtoAXihJmZ\nFVPPJON4YEve+61pXb7VwKVp+RJglqT5+RtIOgeYBjyat/r9qXn8Y5K6a1vsEWev36HNzKzp44Sn\n4TMzK66zwee/FviUpCuBe4BtwGDuQ0kLgS8AV4QQsmn19cBTxIByM/AeYhP6CJKuAq4C6Ovro7+/\nv+pC/uhHP2TmzMGxN2xy+/btm9Dfqdm4vq2rneoKLV/fqRsnstmhxTVr17Jr9uPj278Jtfh3bRTX\nt3W1U12hMfWtZ5KxDVic935RWjckNXFfCiBpJvCmEMLu9H428C3gvSGEn+btsz0tHpb0b8QANEoI\n4WZicGH58uVhxYoV467A11hDQFxwwQXMmTPu3ZtOf38/1fydmpXr27raqa7Q1PVt7jiRzfIZvgzA\nGaefzukrTh7f/k2oib9rVXF9W1c71RUaU996dpe6H1gq6WRJ04C3ALfnbyBpgaRcGa4HPpvWTwO+\nTnzY77aCfRamfwW8EVhXxzqQcWO4mVm9NHecCMPxwQ9+m5mNVLckI4RwFHgncAfwMHBrCOEhSTdK\nen3abAWwXtIGoA94f1r/28ArgCuLDEH4H5LWAmuBBcD76lUHMzOrn5aKE84yzMxGqOszGSGEbwPf\nLlh3Q97ybcBtRfb7IvDFEse8sMbFNDOzBmmFOCG3eJuZjeK5rMvIhQ3foDIzs1KcYpiZjeYkw8zM\nzMzMaspJhpmZWdUUZ1Ryk7eZ2QhOMszMzCYg4BzDzKyQk4wydjMXcPAwM7Mi0hC2HurczGw0Jxll\ndJDlEN2NLoaZmZmZWVNxklFWYD67Gl0IMzMzM7Om4iTDzMxsQoL71ZqZFXCSYWZmVo3gZzHMzEpx\nklFG7r6Ub1CZmVkxIUUKz/ptZjaSkwwzM7MJCPhOlJlZIScZZmZmEyA/k2FmNoqTjDICchO4mZmV\n5fTCzGw0JxljCMg3qMzMzMzMxsFJhpmZ2YS4xdvMrJCTDDMzswlyi7eZ2UhOMszMzCbKWYaZ2QhO\nMszMzKrkjlJmZsU5yaiAb1CZmdkonvHbzKwkJxlmZmYT4PtQZmajOckwMzObgABu8jYzK+AkYwye\njM/MzMbiHMPMbCQnGWZmZhPgm1FmZqM5ySgjS4YsGd+hMjMzMzMbBycZZYhAhmyji2FmZlPdzJmN\nLoGZ2ZRS1yRD0sWS1kvaKOm6Ip+fKOl7ktZI6pe0KK0/S9K9kh5Kn12Wt8/Jku5Lx/yKpGn1rIOZ\nmdVP88cJxdfcufU7hZlZE6pbkiGpA7gJeC2wDLhc0rKCzT4KfD6EcAZwI/CBtP4A8NYQwouBi4GP\nS8pdwT8EfCyEcArwLPC2etXBzMzqp1XihHvUmpmNVs+WjHOAjSGETSGEI8AtwBsKtlkGfD8t/yD3\neQhhQwjhkbT8JLATOFaSgAuB29I+nwPeWMc6AMHPZJiZ1UeLxAmPLmVmVqieScbxwJa891vTunyr\ngUvT8iXALEnz8zeQdA4wDXgUmA/sDiEcLXNMMzNrDo4TZmYtqrPB578W+JSkK4F7gG3AYO5DSQuB\nLwBXhBCyGsetIklXAVelt/skra+yjAv+tffqp6vct9ksANqlruD6trJ2qitMrL4n1rIgddAUceLT\np/1Ju3zf/N9Wa2un+rZTXaEBcaKeScY2YHHe+0Vp3ZDUxH0pgKSZwJtCCLvT+9nAt4D3hhB+mnbZ\nBcyV1JnuUo06Zt6xbwZunmglJK0MISyf6HGaQTvVFVzfVtZOdYWmrq/jRJNpp7qC69vK2qmu0Jj6\n1rO71P3A0jTKxzTgLcDt+RtIWiApV4brgc+m9dOArxMf9sv1qyWEEIh9ct+cVl0B/Fcd62BmZvXj\nOGFm1qLqlmSkO0jvBO4AHgZuDSE8JOlGSa9Pm60A1kvaAPQB70/rfxt4BXClpFXpdVb67D3ANZI2\nEvve/mu96mBmZvXjOGFm1roUb/pYKZKuSk3qLa+d6gqubytrp7pC+9V3qmmnv3871RVc31bWTnWF\nxtTXSYaZmZmZmdVUXWf8NjMzMzOz9uMkowRJF0taL2mjpOsaXZ5iJH1W0k5J6/LWHSPpLkmPpH/n\npfWS9MlUnzWSzs7b54q0/SOSrshb/xJJa9M+n0yTXFV1jhrUdbGkH0j6haSHJP1pi9e3R9LPJK1O\n9f3btP5kSfelc34lPfyKpO70fmP6/KS8Y12f1q+X9Jq89UW/49Wco0Z17pD0oKRvtkFdN6fv2ipJ\nK9O6lvwut7JS36upQm0UI9Lx2yZOqA1jRDqH40QzfZdDCH4VvIAO4qROS4gTPK0GljW6XEXK+Qrg\nbGBd3roPA9el5euAD6Xl3wC+Awg4F7gvrT8G2JT+nZeW56XPfpa2Vdr3tdWco0Z1XQicnZZnARuI\nMwG3an0FzEzLXcB96Ry3Am9J6z8NvD0tvwP4dFp+C/CVtLwsfX+7gZPT97qj3Hd8vOeoYZ2vAb4E\nfLOacjRZXTcDCwrWteR3uVVf5b5XU+VFG8WIdPy2iRO0YYxIx3WcaKLvcsMvglPxBZwH3JH3/nrg\n+kaXq0RZT2JkAFkPLEzLC4H1afkzwOWF2wGXA5/JW/+ZtG4h8L9564e2G+856lTv/wJe3Q71BaYD\nPwdeSpxIp7Pwe0ocnee8tNyZtlPhdze3XanveNpnXOeoUR0XAd8DLgS+WU05mqWu6ZibGR08Wv67\n3EqvUt+rRperSDlPog1jRDp+W8QJ2iBGpGM6TjTZd9ndpYo7HtiS935rWtcM+kII29PyU8QhH6F0\nncqt31pkfTXnqKnUJPmrxDs3LVvf1Cy8CtgJ3EW8y7I7xGE/C883VJb0+XPEoTvH+3eYX8U5auHj\nwF8A2fS+mnI0S10BAnCnpAcUZ52GFv4ut6hm/Zu1xfesHeJEm8UIcJyAJvsu13PGb2uwEEKQFJr9\nHPkUZ/z9GvDuEMKe1IVw0soymfUNIQwCZ0maS5x07LTJOO9kk/Q6YGcI4QFJKxpdnklyfghhm6Tn\nAXdJ+t/8D1vtu2xTU6t+z9olTrRLjADHCZo0Trglo7htwOK894vSumawQ9JCgPTvzrS+VJ3KrV9U\nZH0156gJSV3EwPEfIYT/rLIsTVPfnBDCbuIMxucBcyXlbg7kn2+oLOnzOcCuMmUstX5XFeeYqJcD\nr5e0GbiF2BT+iSrK0Qx1BSCEsC39u5P44+Ac2uC73GKa9W/W0t+zdowTbRAjwHGiKeOEk4zi7geW\nKo4oMI34QM/tDS5TpW4HrkjLVxD7pObWvzWNDnAu8FxqDrsDuEjSvDSCwEXE/obbgT2Szk0jDry1\n4FjjOceEpTL8K/BwCOEf2qC+x6a7U0jqJfYrfpgYSN5coiy5Mr4Z+H6InSZvB96iOArGycBS4sNe\nRb/jaZ/xnmNCQgjXhxAWhRBOSuX4fgjhd1qxrgCSZkialVsmfgfX0aLf5RbWrHGiZb9n7RQn2ilG\ngOMEzRonyj2w0c4v4lP0G4h9HN/b6PKUKOOXge3AALFv3NuI/QG/BzwC/A9wTNpWwE2pPmuB5XnH\n+QNgY3r9ft765elL/SjwKRiavHHc56hBXc8n9k9cA6xKr99o4fqeATyY6rsOuCGtX0K8IG4Evgp0\np/U96f3G9PmSvGO9N5VxPWn0iHLf8WrOUcN6r2B41JCWrGs65+r0eihXnlb9Lrfyq9T3aqq8aKMY\nkY7fNnGCNo0R6TwrcJxoiu+yZ/w2MzMzM7OacncpMzMzMzOrKScZZmZmZmZWU04yzMzMzMysppxk\nmJmZmZlZTTnJMDMzMzOzmnKSYVOapLmS3pH3/jhJtzWyTNWQtELSy8p8/kZJN0xmmcqR1C9peZnP\nPyrpwsksk5lZMY4TjeE4YWNxkmFT3VxgKHiEEJ4MIby5zPZT1QqgZPAA/gL4f5NTlJr4R+C6RhfC\nzAzHianKcaLNOcmwqe6DwAskrZL0EUknSVoHIOlKSd+QdJekzZLeKekaSQ9K+qmkY9J2L5D0XUkP\nSPqhpNMKTyLplekcq9L+s9JdpXskfUvSekmflpRJ218k6V5JP5f0VUkz0/rNkv42rV8r6TRJJwFX\nA3+Wjn9BwblPBQ6HEJ5O739L0jpJqyXdk9Z1pPrfL2mNpD/O2/896VyrJX0wrTsr/Q3WSPp6mukz\nd+fpQ5J+JmlDriySeiXdIulhSV8HevPO+++pPGsl/RlACOFxYL6k59fm/2Yzs6o5TuA4YVNQPWZj\n9MuvWr2Ak4B1xd4DVxJnsJwFHAs8B1ydPvsY8O60/D1gaVp+KfD9Iuf5b+DlaXkm0Em8q3SIOPNm\nB3AX8GZgAXAPMCNt/x6GZ1vdDLwrLb8D+Je0/DfAtSXq+PvA3+e9Xwscn5bnpn+vAv4yLXcDK4GT\ngdcCPwGmp89yM3OuAV6Zlm8EPp6W+3PnIs5u+j9p+Rrgs2n5DOAocTbQlwB35ZVtbt7yPwNvavR3\nxC+//Grvl+OE44RfU/PViVlz+0EIYS+wV9JzxCAA8QJ8Rrpz9DLgq5Jy+3QXOc6PgX+Q9B/Af4YQ\ntqbtfxZC2AQg6cvA+cSAsgz4cdpmGnBv3rH+M/37AHBpBXVYCPyyoCz/LunWvGNdlOqT6wIwB1gK\n/DrwbyGEAwAhhGckzSFe5O9O234O+GqJ8p2Ull8BfDIdY42kNWn9JmCJpH8EvgXcmXecncBxFdTP\nzKyRHCccJ6wBnGRYszuct5zNe58lfr8zwO4QwlnlDhJC+KCkbxHv2vxY0mtyHxVuCoh41+byMco0\nSGX/jR0kBoNcWa6W9FLgN4EHJL0knfNdIYQ78nfMK+d4VFy+EMKzks4EXkNsyv9t4A/Sxz2p7GZm\nU5njxPg5TtiE+ZkMm+r2Epu5qxJC2AM8Jum3ABSdWbidpBeEENaGED4E3A/k+uOeI+nk1Mf2MuBH\nwE+Bl0s6Je07I/WXrbYeDwOnFJTlvhDCDcQ7V4uBO4C3S+pK25wqaQaxaf73JU1P648JITwHPJvX\np/f3gLsp7x7g/6Zj/AqxKRxJC4BMCOFrwF8CZ+ftcyqwbozjmpnVm+OE44RNQU4ybEoLIewi3jFa\nJ+kjVR7md4C3SVoNPAS8ocg2707nWAMMAN9J6+8HPkW8wD8GfD2E8EtiP98vp+3vZTjYlPLfwCXF\nHugjXrh/VcPt9B9JD8+tI/ajXQ38C/AL4Odp/WeAzhDCd4HbgZWSVgHXpmNckY6zBjiL2N+2nH8C\nZkp6OG37QFp/PNCfjv1F4HqAFMROIfb5NTNrGMcJxwmbmhRCYSufmUEcs5z4EN7rJuFcnwD+O4Tw\nP/U+Vy1IugQ4O4TwV40ui5lZozhOlOY4YW7JMJsa/g6Y3uhCjEMn8PeNLoSZWRtxnLCm4pYMMzMz\nMzOrKbdkmJmZmZlZTTnJMDMzMzOzmnKSYWZmZmZmNeUkw8zMzMzMaspJhpmZmZmZ1ZSTDDMzMzMz\nq6n/D2wF67IP/o7nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F9lB_qL2oz9M",
        "colab_type": "code",
        "outputId": "2a9d25c8-3adb-456a-ae2d-3229a7ed8bfe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "# Compare the mean test accuracy along with error bars.\n",
        "def plot_data(data, color, label, gran=10000, max_budget=5000000):\n",
        "  \"\"\"Computes the mean and IQR fixed time steps.\"\"\"\n",
        "  xs = range(0, max_budget+1, gran)\n",
        "  mean = [0.0]\n",
        "  per25 = [0.0]\n",
        "  per75 = [0.0]\n",
        "  \n",
        "  repeats = len(data)\n",
        "  pointers = [1 for _ in range(repeats)]\n",
        "  \n",
        "  cur = gran\n",
        "  while cur < max_budget+1:\n",
        "    all_vals = []\n",
        "    for repeat in range(repeats):\n",
        "      while (pointers[repeat] < len(data[repeat][0]) and \n",
        "             data[repeat][0][pointers[repeat]] < cur):\n",
        "        pointers[repeat] += 1\n",
        "      prev_time = data[repeat][0][pointers[repeat]-1]\n",
        "      prev_test = data[repeat][2][pointers[repeat]-1]\n",
        "      next_time = data[repeat][0][pointers[repeat]]\n",
        "      next_test = data[repeat][2][pointers[repeat]]\n",
        "      assert prev_time < cur and next_time >= cur\n",
        "\n",
        "      # Linearly interpolate the test between the two surrounding points\n",
        "      cur_val = ((cur - prev_time) / (next_time - prev_time)) * (next_test - prev_test) + prev_test\n",
        "      \n",
        "      all_vals.append(cur_val)\n",
        "      \n",
        "    all_vals = sorted(all_vals)\n",
        "    mean.append(sum(all_vals) / float(len(all_vals)))\n",
        "    per25.append(all_vals[int(0.25 * repeats)])\n",
        "    per75.append(all_vals[int(0.75 * repeats)])\n",
        "      \n",
        "    cur += gran\n",
        "    \n",
        "  plt.plot(xs, mean, color=color, label=label, linewidth=2)\n",
        "  plt.fill_between(xs, per25, per75, alpha=0.1, linewidth=0, facecolor=color)\n",
        "\n",
        "plot_data(random_data, 'red', 'random')\n",
        "plot_data(evolution_data, 'blue', 'evolution')\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylim(0.92, 0.95)\n",
        "plt.xlabel('total training time spent (seconds)')\n",
        "plt.ylabel('accuracy')\n",
        "plt.grid()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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v96k451w6PKikw2OKc86lJadBRdKJklZJWi1pj2fcSxoiaY6kJZLKJJUkpb8q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\nGw8qzjnnYuNBxTnnXGz+P/49ipLwK8GAAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DPnRtp0zXUDh",
        "colab_type": "text"
      },
      "source": [
        "## More information\n",
        "\n",
        "For more information on using the dataset, see the API documentation at https://github.com/google-research/nasbench/blob/master/nasbench/api.py.\n"
      ]
    }
  ]
}