enformer-usage.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rb_ShvB9E8yM"
   },
   "source": [
    "Copyright 2021 DeepMind Technologies Limited\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": "kXQjDxgdwUmW"
   },
   "source": [
    "This colab showcases the usage of the Enformer model published in\n",
    "\n",
    "**\"Effective gene expression prediction from sequence by integrating long-range interactions\"**\n",
    "\n",
    "Žiga Avsec, Vikram Agarwal, Daniel Visentin, Joseph R. Ledsam, Agnieszka Grabska-Barwinska, Kyle R. Taylor, Yannis Assael, John Jumper, Pushmeet Kohli, David R. Kelley"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tFnAHhx-ze9X"
   },
   "source": [
    "**Note:** This colab will not yet work since the model isn't yet publicly available. We are working on enabling this and will update the colab accordingly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "si-w2NPretDg"
   },
   "source": [
    "### Steps\n",
    "\n",
    "This colab demonstrates how to\n",
    "- Make predictions with Enformer and reproduce Fig. 1d\n",
    "- Compute contribution scores and reproduce parts of Fig. 2a\n",
    "- Predict the effect of a genetic variant and reproduce parts of Fig. 3g\n",
    "- Score multiple variants in a VCF "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wCCJsjaHwTYC"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NqR7ol3rxrtM"
   },
   "source": [
    "**Start the colab kernel with GPU**: Runtime -> Change runtime type -> GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "hTGOLrbZxNHK"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-08 09:52:19.905757: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
      "2021-11-08 09:52:24.528712: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "2021-11-08 09:52:24.534327: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n",
      "2021-11-08 09:52:24.570526: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
      "pciBusID: 0000:02:00.0 name: GeForce GTX 980 computeCapability: 5.2\n",
      "coreClock: 1.2155GHz coreCount: 16 deviceMemorySize: 3.95GiB deviceMemoryBandwidth: 208.91GiB/s\n",
      "2021-11-08 09:52:24.571316: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 1 with properties: \n",
      "pciBusID: 0000:03:00.0 name: GeForce GTX 980 computeCapability: 5.2\n",
      "coreClock: 1.2155GHz coreCount: 16 deviceMemorySize: 3.95GiB deviceMemoryBandwidth: 208.91GiB/s\n",
      "2021-11-08 09:52:24.571351: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
      "2021-11-08 09:52:24.576648: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n",
      "2021-11-08 09:52:24.576722: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n",
      "2021-11-08 09:52:24.579131: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n",
      "2021-11-08 09:52:24.580164: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n",
      "2021-11-08 09:52:24.584384: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n",
      "2021-11-08 09:52:24.585916: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n",
      "2021-11-08 09:52:24.586914: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n",
      "2021-11-08 09:52:24.589735: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0, 1\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "# Make sure the GPU is enabled \n",
    "assert tf.config.list_physical_devices('GPU'), 'Start the colab kernel with GPU: Runtime -> Change runtime type -> GPU'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "Eg8hcb45wqMM"
   },
   "outputs": [],
   "source": [
    "# !pip install kipoiseq==0.5.2 --quiet > /dev/null\n",
    "# You can ignore the pyYAML error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MCDk7UQPG0Lr"
   },
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "NRI9KisU11bM"
   },
   "outputs": [],
   "source": [
    "import tensorflow_hub as hub\n",
    "import joblib\n",
    "import gzip\n",
    "import kipoiseq\n",
    "from kipoiseq import Interval\n",
    "import pyfaidx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "g0F1A9AaCrkQ"
   },
   "outputs": [],
   "source": [
    "transform_path = 'gs://dm-enformer/models/enformer.finetuned.SAD.robustscaler-PCA500-robustscaler.transform.pkl'\n",
    "model_path = 'https://tfhub.dev/deepmind/enformer/1'\n",
    "work_dir = \"/mnt/storage/idl-0/bio/boxiang/deepmind-research/enformer/data/\"\n",
    "fasta_file = f'{work_dir}/genome.fa'\n",
    "clinvar_vcf = f'{work_dir}/clinvar.vcf.gz'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 141
    },
    "id": "OlE6JAVfI08a",
    "outputId": "25e8b4c7-b2ef-4e8a-a81f-a032a3490bd9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>genome</th>\n",
       "      <th>identifier</th>\n",
       "      <th>file</th>\n",
       "      <th>clip</th>\n",
       "      <th>scale</th>\n",
       "      <th>sum_stat</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ENCFF833POA</td>\n",
       "      <td>/home/drk/tillage/datasets/human/dnase/encode/...</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>mean</td>\n",
       "      <td>DNASE:cerebellum male adult (27 years) and mal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>ENCFF110QGM</td>\n",
       "      <td>/home/drk/tillage/datasets/human/dnase/encode/...</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>mean</td>\n",
       "      <td>DNASE:frontal cortex male adult (27 years) and...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>ENCFF880MKD</td>\n",
       "      <td>/home/drk/tillage/datasets/human/dnase/encode/...</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>mean</td>\n",
       "      <td>DNASE:chorion</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  genome   identifier  \\\n",
       "0      0       0  ENCFF833POA   \n",
       "1      1       0  ENCFF110QGM   \n",
       "2      2       0  ENCFF880MKD   \n",
       "\n",
       "                                                file  clip  scale sum_stat  \\\n",
       "0  /home/drk/tillage/datasets/human/dnase/encode/...    32      2     mean   \n",
       "1  /home/drk/tillage/datasets/human/dnase/encode/...    32      2     mean   \n",
       "2  /home/drk/tillage/datasets/human/dnase/encode/...    32      2     mean   \n",
       "\n",
       "                                         description  \n",
       "0  DNASE:cerebellum male adult (27 years) and mal...  \n",
       "1  DNASE:frontal cortex male adult (27 years) and...  \n",
       "2                                      DNASE:chorion  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download targets from Basenji2 dataset \n",
    "# Cite: Kelley et al Cross-species regulatory sequence activity prediction. PLoS Comput. Biol. 16, e1008050 (2020).\n",
    "targets_txt = 'https://raw.githubusercontent.com/calico/basenji/master/manuscripts/cross2020/targets_human.txt'\n",
    "df_targets = pd.read_csv(targets_txt, sep='\\t')\n",
    "df_targets.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q8ZhswycGux3"
   },
   "source": [
    "### Download files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dowTJknFJOHu"
   },
   "source": [
    "Download and index the reference genome fasta file\n",
    "\n",
    "Credit to Genome Reference Consortium: https://www.ncbi.nlm.nih.gov/grc\n",
    "\n",
    "Schneider et al 2017 http://dx.doi.org/10.1101/gr.213611.116: Evaluation of GRCh38 and de novo haploid genome assemblies demonstrates the enduring quality of the reference assembly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "flOUYxP7Fjvh",
    "outputId": "bc48adb1-393c-408e-d699-5e70b183e2e9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-11-07 21:37:33--  http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz\n",
      "Resolving hgdownload.cse.ucsc.edu (hgdownload.cse.ucsc.edu)... 128.114.119.163\n",
      "Connecting to hgdownload.cse.ucsc.edu (hgdownload.cse.ucsc.edu)|128.114.119.163|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 983659424 (938M) [application/x-gzip]\n",
      "Saving to: ‘STDOUT’\n",
      "\n",
      "-                   100%[===================>] 938.09M  35.7MB/s    in 25s     \n",
      "\n",
      "2021-11-07 21:37:58 (37.8 MB/s) - written to stdout [983659424/983659424]\n",
      "\n",
      "genome.fa  genome.fa.fai\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p /mnt/storage/idl-0/bio/boxiang/deepmind-research/enformer/data/\n",
    "!wget -O - http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz | gunzip -c > {fasta_file}\n",
    "pyfaidx.Faidx(fasta_file)\n",
    "!ls {work_dir}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VwMZhF42JH18"
   },
   "source": [
    "Download the clinvar file. Reference:\n",
    "\n",
    "Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, Gu B, Hart J, Hoffman D, Jang W, Karapetyan K, Katz K, Liu C, Maddipatla Z, Malheiro A, McDaniel K, Ovetsky M, Riley G, Zhou G, Holmes JB, Kattman BL, Maglott DR. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res . 2018 Jan 4. PubMed PMID: 29165669 .\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4PjXFN5OcGbH",
    "outputId": "ca4e8ca5-d388-4ba9-afb9-414f30e83f4a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-11-07 21:40:43--  https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz\n",
      "Resolving ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)... 165.112.9.230, 165.112.9.228, 2607:f220:41f:250::229, ...\n",
      "Connecting to ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)|165.112.9.230|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 45673023 (44M) [application/x-gzip]\n",
      "Saving to: ‘/mnt/storage/idl-0/bio/boxiang/deepmind-research/enformer/data//clinvar.vcf.gz’\n",
      "\n",
      "/mnt/storage/idl-0/ 100%[===================>]  43.56M  15.3MB/s    in 2.9s    \n",
      "\n",
      "2021-11-07 21:40:47 (15.3 MB/s) - ‘/mnt/storage/idl-0/bio/boxiang/deepmind-research/enformer/data//clinvar.vcf.gz’ saved [45673023/45673023]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz -O {work_dir}/clinvar.vcf.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Omj-KERcwSdB"
   },
   "source": [
    "### Code (double click on the title to show the code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "cellView": "form",
    "id": "47E4AEgLx1VT"
   },
   "outputs": [],
   "source": [
    "# @title `Enformer`, `EnformerScoreVariantsNormalized`, `EnformerScoreVariantsPCANormalized`,\n",
    "SEQUENCE_LENGTH = 393216\n",
    "\n",
    "class Enformer:\n",
    "\n",
    "  def __init__(self, tfhub_url):\n",
    "    self._model = hub.load(tfhub_url).model\n",
    "\n",
    "  def predict_on_batch(self, inputs):\n",
    "    predictions = self._model.predict_on_batch(inputs)\n",
    "    return {k: v.numpy() for k, v in predictions.items()}\n",
    "\n",
    "  @tf.function\n",
    "  def contribution_input_grad(self, input_sequence,\n",
    "                              target_mask, output_head='human'):\n",
    "    input_sequence = input_sequence[tf.newaxis]\n",
    "\n",
    "    target_mask_mass = tf.reduce_sum(target_mask)\n",
    "    with tf.GradientTape() as tape:\n",
    "      tape.watch(input_sequence)\n",
    "      prediction = tf.reduce_sum(\n",
    "          target_mask[tf.newaxis] *\n",
    "          self._model.predict_on_batch(input_sequence)[output_head]) / target_mask_mass\n",
    "\n",
    "    input_grad = tape.gradient(prediction, input_sequence) * input_sequence\n",
    "    input_grad = tf.squeeze(input_grad, axis=0)\n",
    "    return tf.reduce_sum(input_grad, axis=-1)\n",
    "\n",
    "\n",
    "class EnformerScoreVariantsRaw:\n",
    "\n",
    "  def __init__(self, tfhub_url, organism='human'):\n",
    "    self._model = Enformer(tfhub_url)\n",
    "    self._organism = organism\n",
    "  \n",
    "  def predict_on_batch(self, inputs):\n",
    "    ref_prediction = self._model.predict_on_batch(inputs['ref'])[self._organism]\n",
    "    alt_prediction = self._model.predict_on_batch(inputs['alt'])[self._organism]\n",
    "\n",
    "    return alt_prediction.mean(axis=1) - ref_prediction.mean(axis=1)\n",
    "\n",
    "\n",
    "class EnformerScoreVariantsNormalized:\n",
    "\n",
    "  def __init__(self, tfhub_url, transform_pkl_path,\n",
    "               organism='human'):\n",
    "    assert organism == 'human', 'Transforms only compatible with organism=human'\n",
    "    self._model = EnformerScoreVariantsRaw(tfhub_url, organism)\n",
    "    with tf.io.gfile.GFile(transform_pkl_path, 'rb') as f:\n",
    "      transform_pipeline = joblib.load(f)\n",
    "    self._transform = transform_pipeline.steps[0][1]  # StandardScaler.\n",
    "    \n",
    "  def predict_on_batch(self, inputs):\n",
    "    scores = self._model.predict_on_batch(inputs)\n",
    "    return self._transform.transform(scores)\n",
    "\n",
    "\n",
    "class EnformerScoreVariantsPCANormalized:\n",
    "\n",
    "  def __init__(self, tfhub_url, transform_pkl_path,\n",
    "               organism='human', num_top_features=500):\n",
    "    self._model = EnformerScoreVariantsRaw(tfhub_url, organism)\n",
    "    with tf.io.gfile.GFile(transform_pkl_path, 'rb') as f:\n",
    "      self._transform = joblib.load(f)\n",
    "    self._num_top_features = num_top_features\n",
    "    \n",
    "  def predict_on_batch(self, inputs):\n",
    "    scores = self._model.predict_on_batch(inputs)\n",
    "    return self._transform.transform(scores)[:, :self._num_top_features]\n",
    "\n",
    "\n",
    "# TODO(avsec): Add feature description: Either PCX, or full names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "cellView": "form",
    "id": "uLYRbOXDyA36"
   },
   "outputs": [],
   "source": [
    "# @title `variant_centered_sequences`\n",
    "\n",
    "class FastaStringExtractor:\n",
    "    \n",
    "    def __init__(self, fasta_file):\n",
    "        self.fasta = pyfaidx.Fasta(fasta_file)\n",
    "        self._chromosome_sizes = {k: len(v) for k, v in self.fasta.items()}\n",
    "\n",
    "    def extract(self, interval: Interval, **kwargs) -> str:\n",
    "        # Truncate interval if it extends beyond the chromosome lengths.\n",
    "        chromosome_length = self._chromosome_sizes[interval.chrom]\n",
    "        trimmed_interval = Interval(interval.chrom,\n",
    "                                    max(interval.start, 0),\n",
    "                                    min(interval.end, chromosome_length),\n",
    "                                    )\n",
    "        # pyfaidx wants a 1-based interval\n",
    "        sequence = str(self.fasta.get_seq(trimmed_interval.chrom,\n",
    "                                          trimmed_interval.start + 1,\n",
    "                                          trimmed_interval.stop).seq).upper()\n",
    "        # Fill truncated values with N's.\n",
    "        pad_upstream = 'N' * max(-interval.start, 0)\n",
    "        pad_downstream = 'N' * max(interval.end - chromosome_length, 0)\n",
    "        return pad_upstream + sequence + pad_downstream\n",
    "\n",
    "    def close(self):\n",
    "        return self.fasta.close()\n",
    "\n",
    "\n",
    "def variant_generator(vcf_file, gzipped=False):\n",
    "  \"\"\"Yields a kipoiseq.dataclasses.Variant for each row in VCF file.\"\"\"\n",
    "  def _open(file):\n",
    "    return gzip.open(vcf_file, 'rt') if gzipped else open(vcf_file)\n",
    "    \n",
    "  with _open(vcf_file) as f:\n",
    "    for line in f:\n",
    "      if line.startswith('#'):\n",
    "        continue\n",
    "      chrom, pos, id, ref, alt_list = line.split('\\t')[:5]\n",
    "      # Split ALT alleles and return individual variants as output.\n",
    "      for alt in alt_list.split(','):\n",
    "        yield kipoiseq.dataclasses.Variant(chrom=chrom, pos=pos,\n",
    "                                           ref=ref, alt=alt, id=id)\n",
    "\n",
    "\n",
    "def one_hot_encode(sequence):\n",
    "  return kipoiseq.transforms.functional.one_hot_dna(sequence).astype(np.float32)\n",
    "\n",
    "\n",
    "def variant_centered_sequences(vcf_file, sequence_length, gzipped=False,\n",
    "                               chr_prefix=''):\n",
    "  seq_extractor = kipoiseq.extractors.VariantSeqExtractor(\n",
    "    reference_sequence=FastaStringExtractor(fasta_file))\n",
    "\n",
    "  for variant in variant_generator(vcf_file, gzipped=gzipped):\n",
    "    interval = Interval(chr_prefix + variant.chrom,\n",
    "                        variant.pos, variant.pos)\n",
    "    interval = interval.resize(sequence_length)\n",
    "    center = interval.center() - interval.start\n",
    "\n",
    "    reference = seq_extractor.extract(interval, [], anchor=center)\n",
    "    alternate = seq_extractor.extract(interval, [variant], anchor=center)\n",
    "\n",
    "    yield {'inputs': {'ref': one_hot_encode(reference),\n",
    "                      'alt': one_hot_encode(alternate)},\n",
    "           'metadata': {'chrom': chr_prefix + variant.chrom,\n",
    "                        'pos': variant.pos,\n",
    "                        'id': variant.id,\n",
    "                        'ref': variant.ref,\n",
    "                        'alt': variant.alt}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "cellView": "form",
    "id": "Up1oCMFiPucp"
   },
   "outputs": [],
   "source": [
    "# @title `plot_tracks`\n",
    "\n",
    "def plot_tracks(tracks, interval, height=1.5):\n",
    "  fig, axes = plt.subplots(len(tracks), 1, figsize=(20, height * len(tracks)), sharex=True)\n",
    "  for ax, (title, y) in zip(axes, tracks.items()):\n",
    "    ax.fill_between(np.linspace(interval.start, interval.end, num=len(y)), y)\n",
    "    ax.set_title(title)\n",
    "    sns.despine(top=True, right=True, bottom=True)\n",
    "  ax.set_xlabel(str(interval))\n",
    "  plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zEwfoz3cwOzt"
   },
   "source": [
    "## Make predictions for a genetic sequenece"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "WC-pgC35DgnL"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-08 09:53:20.770208: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2021-11-08 09:53:20.772509: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "2021-11-08 09:53:20.995722: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
      "pciBusID: 0000:02:00.0 name: GeForce GTX 980 computeCapability: 5.2\n",
      "coreClock: 1.2155GHz coreCount: 16 deviceMemorySize: 3.95GiB deviceMemoryBandwidth: 208.91GiB/s\n",
      "2021-11-08 09:53:20.996135: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 1 with properties: \n",
      "pciBusID: 0000:03:00.0 name: GeForce GTX 980 computeCapability: 5.2\n",
      "coreClock: 1.2155GHz coreCount: 16 deviceMemorySize: 3.95GiB deviceMemoryBandwidth: 208.91GiB/s\n",
      "2021-11-08 09:53:20.996184: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
      "2021-11-08 09:53:20.996244: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n",
      "2021-11-08 09:53:20.996267: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n",
      "2021-11-08 09:53:20.996287: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n",
      "2021-11-08 09:53:20.996306: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n",
      "2021-11-08 09:53:20.996328: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n",
      "2021-11-08 09:53:20.996346: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n",
      "2021-11-08 09:53:20.996368: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n",
      "2021-11-08 09:53:20.997631: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0, 1\n",
      "2021-11-08 09:53:20.999070: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
      "2021-11-08 09:53:24.119030: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-11-08 09:53:24.119089: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 1 \n",
      "2021-11-08 09:53:24.119102: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N Y \n",
      "2021-11-08 09:53:24.119108: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 1:   Y N \n",
      "2021-11-08 09:53:24.124497: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 144 MB memory) -> physical GPU (device: 0, name: GeForce GTX 980, pci bus id: 0000:02:00.0, compute capability: 5.2)\n",
      "2021-11-08 09:53:24.128784: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 2174 MB memory) -> physical GPU (device: 1, name: GeForce GTX 980, pci bus id: 0000:03:00.0, compute capability: 5.2)\n",
      "2021-11-08 09:53:36.560352: W tensorflow/core/common_runtime/bfc_allocator.cc:433] Allocator (GPU_0_bfc) ran out of memory trying to allocate 8B (rounded to 256)requested by op Identity\n",
      "Current allocation summary follows.\n",
      "2021-11-08 09:53:36.560426: I tensorflow/core/common_runtime/bfc_allocator.cc:972] BFCAllocator dump for GPU_0_bfc\n",
      "2021-11-08 09:53:36.560463: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (256): \tTotal Chunks: 18, Chunks in use: 18. 4.5KiB allocated for chunks. 4.5KiB in use in bin. 144B client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560554: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (512): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560575: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (1024): \tTotal Chunks: 1, Chunks in use: 1. 1.2KiB allocated for chunks. 1.2KiB in use in bin. 1.0KiB client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560595: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (2048): \tTotal Chunks: 34, Chunks in use: 34. 109.0KiB allocated for chunks. 109.0KiB in use in bin. 109.0KiB client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560612: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (4096): \tTotal Chunks: 29, Chunks in use: 29. 124.0KiB allocated for chunks. 124.0KiB in use in bin. 124.0KiB client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560631: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (8192): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560644: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (16384): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560657: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (32768): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560670: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (65536): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560683: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (131072): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560697: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (262144): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560709: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (524288): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560722: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (1048576): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560742: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (2097152): \tTotal Chunks: 4, Chunks in use: 4. 10.62MiB allocated for chunks. 10.62MiB in use in bin. 10.62MiB client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560758: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (4194304): \tTotal Chunks: 4, Chunks in use: 4. 18.12MiB allocated for chunks. 18.12MiB in use in bin. 18.12MiB client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560774: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (8388608): \tTotal Chunks: 2, Chunks in use: 2. 24.38MiB allocated for chunks. 24.38MiB in use in bin. 24.38MiB client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560793: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (16777216): \tTotal Chunks: 2, Chunks in use: 2. 40.00MiB allocated for chunks. 40.00MiB in use in bin. 40.00MiB client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560809: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (33554432): \tTotal Chunks: 1, Chunks in use: 1. 51.27MiB allocated for chunks. 51.27MiB in use in bin. 28.12MiB client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560823: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (67108864): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560841: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (134217728): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560858: I tensorflow/core/common_runtime/bfc_allocator.cc:979] Bin (268435456): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n",
      "2021-11-08 09:53:36.560870: I tensorflow/core/common_runtime/bfc_allocator.cc:995] Bin for 256B was 256B, Chunk State: \n",
      "2021-11-08 09:53:36.560881: I tensorflow/core/common_runtime/bfc_allocator.cc:1008] Next region of size 151650304\n",
      "2021-11-08 09:53:36.560899: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 23090c0000 of size 1280 next 1\n",
      "2021-11-08 09:53:36.560911: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 23090c0500 of size 256 next 2\n",
      "2021-11-08 09:53:36.560925: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 23090c0600 of size 3072 next 3\n",
      "2021-11-08 09:53:36.560935: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 23090c1200 of size 3072 next 4\n",
      "2021-11-08 09:53:36.560945: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 23090c1e00 of size 256 next 5\n",
      "2021-11-08 09:53:36.560955: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 23090c1f00 of size 3072 next 6\n",
      "2021-11-08 09:53:36.560969: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 23090c2b00 of size 3072 next 7\n",
      "2021-11-08 09:53:36.560979: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 23090c3700 of size 3072 next 8\n",
      "2021-11-08 09:53:36.560988: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 23090c4300 of size 3072 next 9\n",
      "2021-11-08 09:53:36.560998: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 23090c4f00 of size 3072 next 10\n",
      "2021-11-08 09:53:36.561009: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 23090c5b00 of size 11796480 next 11\n",
      "2021-11-08 09:53:36.561019: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 2309c05b00 of size 256 next 12\n",
      "2021-11-08 09:53:36.561029: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 2309c05c00 of size 3072 next 13\n",
      "2021-11-08 09:53:36.561039: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 2309c06800 of size 3072 next 14\n",
      "2021-11-08 09:53:36.561049: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 2309c07400 of size 256 next 15\n",
      "2021-11-08 09:53:36.561059: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 2309c07500 of size 3072 next 16\n",
      "2021-11-08 09:53:36.561068: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 2309c08100 of size 3072 next 17\n",
      "2021-11-08 09:53:36.561078: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 2309c08d00 of size 3072 next 18\n",
      "2021-11-08 09:53:36.561088: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 2309c09900 of size 3072 next 19\n",
      "2021-11-08 09:53:36.561097: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 2309c0a500 of size 3072 next 20\n",
      "2021-11-08 09:53:36.561109: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 2309c0b100 of size 2359296 next 21\n",
      "2021-11-08 09:53:36.561119: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 2309e4b100 of size 2359296 next 22\n",
      "2021-11-08 09:53:36.561129: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230a08b100 of size 256 next 23\n",
      "2021-11-08 09:53:36.561141: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230a08b200 of size 3072 next 24\n",
      "2021-11-08 09:53:36.561151: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230a08be00 of size 3072 next 25\n",
      "2021-11-08 09:53:36.561161: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230a08ca00 of size 256 next 26\n",
      "2021-11-08 09:53:36.561180: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230a08cb00 of size 3072 next 27\n",
      "2021-11-08 09:53:36.561199: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230a08d700 of size 3072 next 28\n",
      "2021-11-08 09:53:36.561217: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230a08e300 of size 3072 next 29\n",
      "2021-11-08 09:53:36.561235: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230a08ef00 of size 3072 next 30\n",
      "2021-11-08 09:53:36.561253: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230a08fb00 of size 3584 next 31\n",
      "2021-11-08 09:53:36.561271: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230a090900 of size 13762560 next 32\n",
      "2021-11-08 09:53:36.561291: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230adb0900 of size 256 next 33\n",
      "2021-11-08 09:53:36.561309: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230adb0a00 of size 3584 next 34\n",
      "2021-11-08 09:53:36.561327: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230adb1800 of size 3584 next 35\n",
      "2021-11-08 09:53:36.561346: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230adb2600 of size 256 next 36\n",
      "2021-11-08 09:53:36.561364: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230adb2700 of size 3584 next 37\n",
      "2021-11-08 09:53:36.561384: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230adb3500 of size 3584 next 38\n",
      "2021-11-08 09:53:36.561402: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230adb4300 of size 3584 next 39\n",
      "2021-11-08 09:53:36.561420: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230adb5100 of size 3584 next 40\n",
      "2021-11-08 09:53:36.561442: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230adb5f00 of size 3584 next 41\n",
      "2021-11-08 09:53:36.561462: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230adb6d00 of size 3211264 next 42\n",
      "2021-11-08 09:53:36.561483: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230b0c6d00 of size 3211264 next 43\n",
      "2021-11-08 09:53:36.561505: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230b3d6d00 of size 256 next 44\n",
      "2021-11-08 09:53:36.561555: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230b3d6e00 of size 3584 next 45\n",
      "2021-11-08 09:53:36.561588: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230b3d7c00 of size 3584 next 46\n",
      "2021-11-08 09:53:36.561609: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230b3d8a00 of size 256 next 47\n",
      "2021-11-08 09:53:36.561632: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230b3d8b00 of size 3584 next 48\n",
      "2021-11-08 09:53:36.561654: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230b3d9900 of size 3584 next 49\n",
      "2021-11-08 09:53:36.561674: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230b3da700 of size 3584 next 50\n",
      "2021-11-08 09:53:36.561691: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230b3db500 of size 3584 next 51\n",
      "2021-11-08 09:53:36.561713: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230b3dc300 of size 4096 next 52\n",
      "2021-11-08 09:53:36.561734: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230b3dd300 of size 18350080 next 53\n",
      "2021-11-08 09:53:36.561751: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230c55d300 of size 256 next 54\n",
      "2021-11-08 09:53:36.561772: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230c55d400 of size 4096 next 55\n",
      "2021-11-08 09:53:36.561791: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230c55e400 of size 4096 next 56\n",
      "2021-11-08 09:53:36.561813: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230c55f400 of size 256 next 57\n",
      "2021-11-08 09:53:36.561836: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230c55f500 of size 4096 next 58\n",
      "2021-11-08 09:53:36.561862: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230c560500 of size 4096 next 59\n",
      "2021-11-08 09:53:36.561884: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230c561500 of size 4096 next 60\n",
      "2021-11-08 09:53:36.561901: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230c562500 of size 4096 next 61\n",
      "2021-11-08 09:53:36.561924: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230c563500 of size 4096 next 62\n",
      "2021-11-08 09:53:36.561942: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230c564500 of size 4194304 next 63\n",
      "2021-11-08 09:53:36.561959: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230c964500 of size 4194304 next 64\n",
      "2021-11-08 09:53:36.561978: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230cd64500 of size 256 next 65\n",
      "2021-11-08 09:53:36.561999: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230cd64600 of size 4096 next 66\n",
      "2021-11-08 09:53:36.562018: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230cd65600 of size 4096 next 67\n",
      "2021-11-08 09:53:36.562037: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230cd66600 of size 256 next 68\n",
      "2021-11-08 09:53:36.562056: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230cd66700 of size 4096 next 69\n",
      "2021-11-08 09:53:36.562074: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230cd67700 of size 4096 next 70\n",
      "2021-11-08 09:53:36.562092: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230cd68700 of size 4096 next 71\n",
      "2021-11-08 09:53:36.562110: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230cd69700 of size 4096 next 72\n",
      "2021-11-08 09:53:36.562129: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230cd6a700 of size 4608 next 73\n",
      "2021-11-08 09:53:36.562147: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230cd6b900 of size 23592960 next 74\n",
      "2021-11-08 09:53:36.562167: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230e3eb900 of size 256 next 75\n",
      "2021-11-08 09:53:36.562184: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230e3eba00 of size 4608 next 76\n",
      "2021-11-08 09:53:36.562203: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230e3ecc00 of size 4608 next 77\n",
      "2021-11-08 09:53:36.562225: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230e3ede00 of size 256 next 78\n",
      "2021-11-08 09:53:36.562245: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230e3edf00 of size 4608 next 79\n",
      "2021-11-08 09:53:36.562263: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230e3ef100 of size 4608 next 80\n",
      "2021-11-08 09:53:36.562280: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230e3f0300 of size 4608 next 81\n",
      "2021-11-08 09:53:36.562299: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230e3f1500 of size 4608 next 82\n",
      "2021-11-08 09:53:36.562317: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230e3f2700 of size 4608 next 83\n",
      "2021-11-08 09:53:36.562337: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230e3f3900 of size 5308416 next 84\n",
      "2021-11-08 09:53:36.562361: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230e903900 of size 5308416 next 85\n",
      "2021-11-08 09:53:36.562379: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230ee13900 of size 256 next 86\n",
      "2021-11-08 09:53:36.562400: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230ee13a00 of size 4608 next 87\n",
      "2021-11-08 09:53:36.562419: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230ee14c00 of size 4608 next 88\n",
      "2021-11-08 09:53:36.562439: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230ee15e00 of size 256 next 89\n",
      "2021-11-08 09:53:36.562457: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230ee15f00 of size 4608 next 90\n",
      "2021-11-08 09:53:36.562481: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230ee17100 of size 4608 next 91\n",
      "2021-11-08 09:53:36.562499: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230ee18300 of size 4608 next 92\n",
      "2021-11-08 09:53:36.562518: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230ee19500 of size 4608 next 93\n",
      "2021-11-08 09:53:36.562536: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230ee1a700 of size 5120 next 94\n",
      "2021-11-08 09:53:36.562560: I tensorflow/core/common_runtime/bfc_allocator.cc:1028] InUse at 230ee1bb00 of size 53757184 next 18446744073709551615\n",
      "2021-11-08 09:53:36.562578: I tensorflow/core/common_runtime/bfc_allocator.cc:1033]      Summary of in-use Chunks by size: \n",
      "2021-11-08 09:53:36.562608: I tensorflow/core/common_runtime/bfc_allocator.cc:1036] 18 Chunks of size 256 totalling 4.5KiB\n",
      "2021-11-08 09:53:36.562637: I tensorflow/core/common_runtime/bfc_allocator.cc:1036] 1 Chunks of size 1280 totalling 1.2KiB\n",
      "2021-11-08 09:53:36.562667: I tensorflow/core/common_runtime/bfc_allocator.cc:1036] 20 Chunks of size 3072 totalling 60.0KiB\n",
      "2021-11-08 09:53:36.562696: I tensorflow/core/common_runtime/bfc_allocator.cc:1036] 14 Chunks of size 3584 totalling 49.0KiB\n",
      "2021-11-08 09:53:36.562723: I tensorflow/core/common_runtime/bfc_allocator.cc:1036] 14 Chunks of size 4096 totalling 56.0KiB\n",
      "2021-11-08 09:53:36.562747: I tensorflow/core/common_runtime/bfc_allocator.cc:1036] 14 Chunks of size 4608 totalling 63.0KiB\n",
      "2021-11-08 09:53:36.562774: I tensorflow/core/common_runtime/bfc_allocator.cc:1036] 1 Chunks of size 5120 totalling 5.0KiB\n",
      "2021-11-08 09:53:36.562801: I tensorflow/core/common_runtime/bfc_allocator.cc:1036] 2 Chunks of size 2359296 totalling 4.50MiB\n",
      "2021-11-08 09:53:36.562825: I tensorflow/core/common_runtime/bfc_allocator.cc:1036] 2 Chunks of size 3211264 totalling 6.12MiB\n",
      "2021-11-08 09:53:36.562852: I tensorflow/core/common_runtime/bfc_allocator.cc:1036] 2 Chunks of size 4194304 totalling 8.00MiB\n",
      "2021-11-08 09:53:36.562878: I tensorflow/core/common_runtime/bfc_allocator.cc:1036] 2 Chunks of size 5308416 totalling 10.12MiB\n",
      "2021-11-08 09:53:36.562904: I tensorflow/core/common_runtime/bfc_allocator.cc:1036] 1 Chunks of size 11796480 totalling 11.25MiB\n",
      "2021-11-08 09:53:36.562928: I tensorflow/core/common_runtime/bfc_allocator.cc:1036] 1 Chunks of size 13762560 totalling 13.12MiB\n",
      "2021-11-08 09:53:36.562951: I tensorflow/core/common_runtime/bfc_allocator.cc:1036] 1 Chunks of size 18350080 totalling 17.50MiB\n",
      "2021-11-08 09:53:36.562973: I tensorflow/core/common_runtime/bfc_allocator.cc:1036] 1 Chunks of size 23592960 totalling 22.50MiB\n",
      "2021-11-08 09:53:36.562998: I tensorflow/core/common_runtime/bfc_allocator.cc:1036] 1 Chunks of size 53757184 totalling 51.27MiB\n",
      "2021-11-08 09:53:36.563023: I tensorflow/core/common_runtime/bfc_allocator.cc:1040] Sum Total of in-use chunks: 144.62MiB\n",
      "2021-11-08 09:53:36.563043: I tensorflow/core/common_runtime/bfc_allocator.cc:1042] total_region_allocated_bytes_: 151650304 memory_limit_: 151650304 available bytes: 0 curr_region_allocation_bytes_: 303300608\n",
      "2021-11-08 09:53:36.563074: I tensorflow/core/common_runtime/bfc_allocator.cc:1048] Stats: \n",
      "Limit:                       151650304\n",
      "InUse:                       151650304\n",
      "MaxInUse:                    151650304\n",
      "NumAllocs:                          95\n",
      "MaxAllocSize:                 53757184\n",
      "Reserved:                            0\n",
      "PeakReserved:                        0\n",
      "LargestFreeBlock:                    0\n",
      "\n",
      "2021-11-08 09:53:36.563113: W tensorflow/core/common_runtime/bfc_allocator.cc:441] ************************************************************************************xxxxxxxxxxxxxxxx\n"
     ]
    },
    {
     "ename": "InternalError",
     "evalue": "Failed copying input tensor from /job:localhost/replica:0/task:0/device:CPU:0 to /job:localhost/replica:0/task:0/device:GPU:0 in order to run Identity: Dst tensor is not initialized. [Op:Identity]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInternalError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_2726957/3181206396.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEnformer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mfasta_extractor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFastaStringExtractor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfasta_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_2726957/4187206304.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, tfhub_url)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtfhub_url\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhub\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtfhub_url\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mpredict_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/enformer/lib/python3.8/site-packages/tensorflow_hub/module_v2.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(handle, tags, options)\u001b[0m\n\u001b[1;32m    104\u001b[0m         module_path, tags=tags, options=options)\n\u001b[1;32m    105\u001b[0m   \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m     \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mv1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaved_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_v2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtags\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    107\u001b[0m   \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_hub_module_v1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mis_hub_module_v1\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/enformer/lib/python3.8/site-packages/tensorflow/python/saved_model/load.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(export_dir, tags, options)\u001b[0m\n\u001b[1;32m    857\u001b[0m     \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIf\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mtags\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mdon\u001b[0m\u001b[0;31m'\u001b[0m\u001b[0mt\u001b[0m \u001b[0mmatch\u001b[0m \u001b[0ma\u001b[0m \u001b[0mMetaGraph\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mSavedModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m   \"\"\"\n\u001b[0;32m--> 859\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mload_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexport_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"root\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    860\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/enformer/lib/python3.8/site-packages/tensorflow/python/saved_model/load.py\u001b[0m in \u001b[0;36mload_internal\u001b[0;34m(export_dir, tags, options, loader_cls, filters)\u001b[0m\n\u001b[1;32m    887\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m       \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m         loader = loader_cls(object_graph_proto, saved_model_proto, export_dir,\n\u001b[0m\u001b[1;32m    890\u001b[0m                             ckpt_options, filters)\n\u001b[1;32m    891\u001b[0m       \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNotFoundError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/enformer/lib/python3.8/site-packages/tensorflow/python/saved_model/load.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, object_graph_proto, saved_model_proto, export_dir, ckpt_options, filters)\u001b[0m\n\u001b[1;32m    159\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_load_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_restore_checkpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nodes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/enformer/lib/python3.8/site-packages/tensorflow/python/saved_model/load.py\u001b[0m in \u001b[0;36m_restore_checkpoint\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    485\u001b[0m                                   self._checkpoint_options).expect_partial()\n\u001b[1;32m    486\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 487\u001b[0;31m       \u001b[0mload_status\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msaver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariables_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_checkpoint_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    488\u001b[0m     \u001b[0mload_status\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_existing_objects_matched\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m     \u001b[0mcheckpoint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_status\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_checkpoint\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/enformer/lib/python3.8/site-packages/tensorflow/python/training/tracking/util.py\u001b[0m in \u001b[0;36mrestore\u001b[0;34m(self, save_path, options)\u001b[0m\n\u001b[1;32m   1334\u001b[0m         \u001b[0mgraph_view\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_graph_view\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1335\u001b[0m         options=options)\n\u001b[0;32m-> 1336\u001b[0;31m     base.CheckpointPosition(\n\u001b[0m\u001b[1;32m   1337\u001b[0m         checkpoint=checkpoint, proto_id=0).restore(self._graph_view.root)\n\u001b[1;32m   1338\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/enformer/lib/python3.8/site-packages/tensorflow/python/training/tracking/base.py\u001b[0m in \u001b[0;36mrestore\u001b[0;34m(self, trackable)\u001b[0m\n\u001b[1;32m    251\u001b[0m         \u001b[0;31m# This object's correspondence with a checkpointed object is new, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m         \u001b[0;31m# process deferred restorations for it and its dependencies.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m         \u001b[0mrestore_ops\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrackable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_restore_from_checkpoint_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    254\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrestore_ops\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_checkpoint\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_restore_ops\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrestore_ops\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/enformer/lib/python3.8/site-packages/tensorflow/python/training/tracking/base.py\u001b[0m in \u001b[0;36m_restore_from_checkpoint_position\u001b[0;34m(self, checkpoint_position)\u001b[0m\n\u001b[1;32m    970\u001b[0m       \u001b[0mpython_saveables\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_python_saveables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    971\u001b[0m     restore_ops.extend(\n\u001b[0;32m--> 972\u001b[0;31m         current_position.checkpoint.restore_saveables(\n\u001b[0m\u001b[1;32m    973\u001b[0m             tensor_saveables, python_saveables))\n\u001b[1;32m    974\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrestore_ops\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/enformer/lib/python3.8/site-packages/tensorflow/python/training/tracking/util.py\u001b[0m in \u001b[0;36mrestore_saveables\u001b[0;34m(self, tensor_saveables, python_saveables)\u001b[0m\n\u001b[1;32m    305\u001b[0m             (\"Saveable keys changed when validating. Got back %s, was \"\n\u001b[1;32m    306\u001b[0m              \"expecting %s\") % (tensor_saveables.keys(), validated_names))\n\u001b[0;32m--> 307\u001b[0;31m       new_restore_ops = functional_saver.MultiDeviceSaver(\n\u001b[0m\u001b[1;32m    308\u001b[0m           validated_saveables).restore(self.save_path_tensor, self.options)\n\u001b[1;32m    309\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/enformer/lib/python3.8/site-packages/tensorflow/python/training/saving/functional_saver.py\u001b[0m in \u001b[0;36mrestore\u001b[0;34m(self, file_prefix, options)\u001b[0m\n\u001b[1;32m    343\u001b[0m       \u001b[0mrestore_ops\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_function_restore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m       \u001b[0mrestore_ops\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrestore_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_after_restore_callbacks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/enformer/lib/python3.8/site-packages/tensorflow/python/training/saving/functional_saver.py\u001b[0m in \u001b[0;36mrestore_fn\u001b[0;34m()\u001b[0m\n\u001b[1;32m    319\u001b[0m       \u001b[0;32mfor\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaver\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_single_device_savers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    320\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 321\u001b[0;31m           \u001b[0mrestore_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msaver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_prefix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    322\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mrestore_ops\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/enformer/lib/python3.8/site-packages/tensorflow/python/training/saving/functional_saver.py\u001b[0m in \u001b[0;36mrestore\u001b[0;34m(self, file_prefix, options)\u001b[0m\n\u001b[1;32m    113\u001b[0m     for saveable, restored_tensors in zip(self._saveable_objects,\n\u001b[1;32m    114\u001b[0m                                           structured_restored_tensors):\n\u001b[0;32m--> 115\u001b[0;31m       restore_ops[saveable.name] = saveable.restore(\n\u001b[0m\u001b[1;32m    116\u001b[0m           restored_tensors, restored_shapes=None)\n\u001b[1;32m    117\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrestore_ops\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/enformer/lib/python3.8/site-packages/tensorflow/python/training/saving/saveable_object_util.py\u001b[0m in \u001b[0;36mrestore\u001b[0;34m(self, restored_tensors, restored_shapes)\u001b[0m\n\u001b[1;32m    128\u001b[0m     \u001b[0;31m# Copy the restored tensor to the variable's device.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_var_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m       \u001b[0mrestored_tensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrestored_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    131\u001b[0m       return resource_variable_ops.shape_safe_assign_variable_handle(\n\u001b[1;32m    132\u001b[0m           self.handle_op, self._var_shape, restored_tensor)\n",
      "\u001b[0;32m~/miniconda3/envs/enformer/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    199\u001b[0m     \u001b[0;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    202\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m       \u001b[0;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/enformer/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py\u001b[0m in \u001b[0;36midentity\u001b[0;34m(input, name)\u001b[0m\n\u001b[1;32m    285\u001b[0m     \u001b[0;31m# variables. Variables have correct handle data when graph building.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m     \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 287\u001b[0;31m   \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgen_array_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    288\u001b[0m   \u001b[0;31m# Propagate handle data for happier shape inference for resource variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_handle_data\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/enformer/lib/python3.8/site-packages/tensorflow/python/ops/gen_array_ops.py\u001b[0m in \u001b[0;36midentity\u001b[0;34m(input, name)\u001b[0m\n\u001b[1;32m   3931\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3932\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3933\u001b[0;31m       \u001b[0m_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from_not_ok_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3934\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_FallbackException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3935\u001b[0m       \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/enformer/lib/python3.8/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mraise_from_not_ok_status\u001b[0;34m(e, name)\u001b[0m\n\u001b[1;32m   6860\u001b[0m   \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\" name: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6861\u001b[0m   \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6862\u001b[0;31m   \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6863\u001b[0m   \u001b[0;31m# pylint: enable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6864\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/enformer/lib/python3.8/site-packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
      "\u001b[0;31mInternalError\u001b[0m: Failed copying input tensor from /job:localhost/replica:0/task:0/device:CPU:0 to /job:localhost/replica:0/task:0/device:GPU:0 in order to run Identity: Dst tensor is not initialized. [Op:Identity]"
     ]
    }
   ],
   "source": [
    "model = Enformer(model_path)\n",
    "\n",
    "fasta_extractor = FastaStringExtractor(fasta_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "8u8Gt8WWyG53"
   },
   "outputs": [],
   "source": [
    "# @title Make predictions for an genomic example interval\n",
    "target_interval = kipoiseq.Interval('chr11', 35_082_742, 35_197_430)  # @param\n",
    "\n",
    "sequence_one_hot = one_hot_encode(fasta_extractor.extract(target_interval.resize(SEQUENCE_LENGTH)))\n",
    "predictions = model.predict_on_batch(sequence_one_hot[np.newaxis])['human'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 440
    },
    "id": "_wL4c66ZXK0k",
    "outputId": "3538b7f3-ebb0-48c8-f23a-1e8734253e81"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 423,
       "width": 1432
      },
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# @title Plot tracks\n",
    "tracks = {'DNASE:CD14-positive monocyte female': predictions[:, 41],\n",
    "          'DNASE:keratinocyte female': predictions[:, 42],\n",
    "          'CHIP:H3K27ac:keratinocyte female': predictions[:, 706],\n",
    "          'CAGE:Keratinocyte - epidermal': np.log10(1 + predictions[:, 4799])}\n",
    "plot_tracks(tracks, target_interval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gM2KwV8gwMNj"
   },
   "source": [
    "## Contribution scores example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "o4c2W_MjNBzp"
   },
   "outputs": [],
   "source": [
    "# @title Compute contribution scores\n",
    "target_interval = kipoiseq.Interval('chr12', 54_223_589, 54_338_277)  # @param\n",
    "\n",
    "sequence_one_hot = one_hot_encode(fasta_extractor.extract(target_interval.resize(SEQUENCE_LENGTH)))\n",
    "predictions = model.predict_on_batch(sequence_one_hot[np.newaxis])['human'][0]\n",
    "\n",
    "target_mask = np.zeros_like(predictions)\n",
    "for idx in [447, 448, 449]:\n",
    "  target_mask[idx, 4828] = 1\n",
    "  target_mask[idx, 5111] = 1\n",
    "# This will take some time since tf.function needs to get compiled.\n",
    "contribution_scores = model.contribution_input_grad(sequence_one_hot.astype(np.float32), target_mask).numpy()\n",
    "pooled_contribution_scores = tf.nn.avg_pool1d(np.abs(contribution_scores)[np.newaxis, :, np.newaxis], 128, 128, 'VALID')[0, :, 0].numpy()[1088:-1088]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "id": "y-s0yQMOXO7x",
    "outputId": "a5cc620e-2680-4780-d546-845e9ca02740"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 207,
       "width": 1433
      },
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tracks = {'CAGE predictions': predictions[:, 4828],\n",
    "          'Enformer gradient*input': np.minimum(pooled_contribution_scores, 0.03)}\n",
    "plot_tracks(tracks, target_interval);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iiDN_ScSv3sI"
   },
   "source": [
    "## Variant scoring example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "hIaN9GKDVwLK"
   },
   "outputs": [],
   "source": [
    "# @title Score the variant\n",
    "variant = kipoiseq.Variant('chr16', 57025062, 'C', 'T', id='rs11644125')  # @param\n",
    "\n",
    "# Center the interval at the variant\n",
    "interval = kipoiseq.Interval(variant.chrom, variant.start, variant.start).resize(SEQUENCE_LENGTH)\n",
    "seq_extractor = kipoiseq.extractors.VariantSeqExtractor(reference_sequence=fasta_extractor)\n",
    "center = interval.center() - interval.start\n",
    "\n",
    "reference = seq_extractor.extract(interval, [], anchor=center)\n",
    "alternate = seq_extractor.extract(interval, [variant], anchor=center)\n",
    "\n",
    "# Make predictions for the refernece and alternate allele\n",
    "reference_prediction = model.predict_on_batch(one_hot_encode(reference)[np.newaxis])['human'][0]\n",
    "alternate_prediction = model.predict_on_batch(one_hot_encode(alternate)[np.newaxis])['human'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 368
    },
    "id": "in1PyiWoXXjc",
    "outputId": "664617ba-97c1-4ed3-8d4d-e5e8879ce474"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kpB6iqSSlxL+/eBzfffkEHt/dlOlyUmpbTS9u+sVOPLyjEZ3eSErW+cKhNnzq4XfQ44/g0V2NKVknTV8MOBMRERERERFNHQaciYiIiGjKPLmneUq3F4wpw6aaJiIiIiKimUNKiZ+9UY2vPXMEkfjo4eLf7W0Z+vr+LXXwhOIp2fap9wScq9omHlRWVImvP3MEoVgiJTURTQUpJf7ntdN4ap/2v/Xfr50e8f8wU7X0hfCVpw4jHU2pDzYP4PqfbUdNN8Ovs119b/DcCxERERERERFRSjDgTERERERTom0ghLdPd0/5dp/Z3zJiemkiIiIiIpreInEFX336CO57u+6sy+1p6ENDbwCt/SE8tqspZds/2Tn8PcTRSQScAaDRHcTXnj6CcExJRVlEaSWlxM/erMGD2xuGboslVHzlqcOIxGf233AolsAXfnMA3nBqBkGMJp3rpumjuS+IxAQ7+hMRERERERHR5DDgTERERERT4nfvtEBNQ5ekc1El8J9/PgmZjhZNRERERESUcttrevGxX+7Cy1Ud41r+qX0t+Mkb1YilMHBW3eWDcsYbmKNt3kmv642T3fjkQ3vQ44+kojSilAhGE6jvfbfbsKpKfP9PJ/HzUQYV1PYE8MstZx9sMJ1JKfHtF47hNAc/UwrEFYnWgXCmyyAiIiIiIiKaExhwJiIiIqK0i8QVPL2v5dwLpsmOWje21vRmbPtERERERHRudT0BfOaRd3Dbo/smFET8/TsteOnI+MLQ4xWJq2h0BwFonV9rus8vGFnV5sXHfrFrWKCUKFM6PGH8za/24NqfbsMdT+zHnvo+/H8vHMXju5vGfMzju5vgi8zMDsVP7G4a94AJovGo75me+/JefxSBaCLTZRAR0Rn6g7FMl0B03pRMdC8iIiJKMmS6ACIiIiKa/f58tBMDocxeCP3hn0/hqsX5MOo5xo+IiIiIaLp5/XgX/uXZIwjGlAk/djKPGY9TnT4sLnTgRIcvJbPRdHgj+KffH8aLX74CZoP+/FdINAlHWj34/JMH0OuPAgA2n+rB5lM953ycP5LAb/Y048ubFqe7xJQ62DyA//zzqUyXQbNMfW8A16Eo02UM8YbieGBbPR7b1QgJ4OolBdi0vADdviiOtHpgN+lx/9+tgxAi06USEc0qqiqRUCVMhtGvOext6MN3XzqB17+2gftgmtE++dAeqBJ4//JC3LyuDCVZ1nE9rq4ngMWFjjRXR0REsx3THURERESUVgeb+/G/f6nOdBmo6wngi785iGPnMbU0ERERERGllqpK/PSNatz524NpCypP1qlOHwCgqtWT0nX+3+balK2PaCKqWj249aG9Q+HmiXpkZyNCsZnTHdYdiOLLvzuEBDvOUYpNh278qiqxr7Ef33v5BDb8+G38als9ogkVsYSKzae6cfcfj+O+t2qxvaYXrx3vYhdzIqIUk1LiP145ie++fGLU+2u6/fjCkwdQ3e3H4RS+nyCaagea+rG/aQAHmwfwv3+pxh1PHEAsoZ7zcbGEitsf3YfXj3dNQZVERDSbsYMzEREREaWFokr8cksd7n2rdtpMX/X26R68fboHVy8twF0fXIYLyrIyXRIRERER0ZzlDkTx9WeOYEetO9OljGow4Hw0xYMkH9xWj2uXF+KSBbkpXS/R2TS5g/j7x/cjHJ/8QIL+YAxP7WvFP1xVmcLKJkdKCSkBnW5kN8SEouLFIx24761adPkiGaiOZruG3uCUb1NKid31fTjcMoCTnT4caBpAzwQGK/zglZPYuLQQWTZjGqskIppdXjnagV5/FJX5dqwocaHIZRm6757NtXh8dxMAYG1FNv52fcXQfe2eMD776D74ItrAsD8casO6eTlTWjtRqvxqW8Ow7090+HDvWzX41geXn/VxfzjUhnZPGP/2x2NYNz8bhU7LWZcnIiIaCwPORETTlDcUx76mfly/cvpMdUdENF5N7iDuev4o9jX1Z7qUUW2v6cWuOjfuuKoSX7tuKawmTg9NREREExdNKHjxcDsEBD54QTGyrEZIKXGk1YO3TvWgKMuC1WVZWFHihNnA4w2iM73T0IevPH0Y3b7JdZKdCqc6/QCAo22p7bimSuAbz1bh1a9ugMPMU/SUfu5AFLc/tg99wdh5r+uh7fX49GXzMvq61uEJ4/+9dBx9wRj+75aLMD/PDkALNr9c1YGfv12HRvfUB1Bp7khVB+edtW7U9wbwqffNg1E/9qS7wWgC//7icfzxcPukt+UOxPA/r5/Gf9+8etLrICKaSw429+OrTx8Z1rzl4vk5+MiaUvjCcdz31ruzsvz7S8exvMSJJYVO/HpHAx7YVo/QGbPT/KmqE9+5cSXPC9CMU9fjx+ZT3SNuf2BrPTYte3fQrjsQhdNiGPobjyVU/GJLHQBtkOS/vnAMv779EggxcnAiERHRuQgpp0c3vZlCCHFw3bp16w4ePJjpUoholvKG43hkZyMe29kIfzSBf37/Ynzj+qXDDvgTiorangCOtnlwaWUeKvPtGayYiOhdqirxxJ4m/Oj104jEzz1F1XRQnmPFyhIXQjEFMUXFHVdV4gOrijNdFhEREU1jiirx+O4mPLy9YagzpMmgw4bF+ajvDaCpLzRs+RUlLjzzxcvgsrBjHtF0nOnlbLZ8cyM2/WRrWtZ944Ul+Pmta3mRl9IqElfwyYf24kgKp0afn2fDunk5WFOehdJsK4x6HfQ6Ab1OQAjAoNOhJMuC0mwr9KN0WB4PKSXaPWGc7PCh0R2EzaRHls2ETk8Y971Vi2AyNGQ36fHdm1ZBrxP4+du1I16DidLl0HeuR67dNKnHqqrEA9vq8dM3qqFKYHGhA3d/aAVKsi2o7Q6g0R2E02JARY4NZqMO333pBBpSFNp/6vOX4fJFeSlZFxHRbDUQjOHD9+1Ah3f8M0EUuywQAugc4zG/+vQ63HBBSapKJJoSdz1fhWcPtI16X0WuFd++YQX+eLgdW6t7sHZeNh78zCXItZvw9L4WfPsPx4Yt/6NPrMYt6+dNRdlERDR9TeokEQPOE8SAMxGlU223H3//xH609oeH3f4PV1Xi7g+twN6GPjy1vxWbT3YPTSdpMerw7RuW47bLF4w6JSMR0VQ53eXDv//xOA40D2S6lPN23YpCfPemVajItWW6FCIiIppmEoqKf3muCi8d6ZjQ465YlIfHPreeHZtoTmv3hPH1p49M25leRvOljYvwwNb6tK3/Pz66CrddviBt66e5TUqJu54/iucOjh5KSDeTXofyXCtybSZk20wocJqxtMiBZcVah8N8h2lEwL/bF8HT+1rx9P6WMQNCRNPBc3dejvXJroUT4Q3F8S/PHcHmUz1pqOrcXBYDnr3zciwvdmVk+0RE052qStzx5AG8fTq1++nrVxbh4dsuSek6B4VjCgx6cdbZAIgmqtsXwYYfbUFMGX8zo3m5Njx028W444kDaBsYnndwWQx4+5sbke8wp7pUIiKaORhwngoMOBNRumyr6cU//e4Q/NHEqPfn2k3oP8s0kpcvzMN/fvwCLCpwpKtEIqJRecNx3L+lDr/e2TgjOrCNl8WowxevXoQ7r1kEq4lBJCIiItLCzd94tgovV00s3DzopjWluPeWizg4leac4+1ePL2/BX881D7UdXWmKHCa0euPpm39Rr3A83degTUV2WnbBs1dj+9qxPf+dDLTZYzJYTZgQb4NTrMRCVVFNKHiZIcPiVl0boFmr8l0ITzR4cWXfnsILf2Z7TRe6DTjhS9dwYH9RESjeHRnI/7jldQfPxl0Avvuvm7c3f+7fRFk24zjGiT9reeqUJptxdevX3q+ZRJBVSVeP9GFn75Rjfreic8gYdCJMY/nb15bhp/dctH5lkhERDMXA85TgQFnIkqHLad7cMeTB847GGjUC3zh6oX48qbFsJkMKaqOiGgkbziO14934rXjXdhV50Zcmb3HlKVZFnz9+qW4aU0pLEYGnYmIiOaqYDSBu54/ij8f6zyv9Xzrg8vw5U2LU1QV0fR2ssOH77x0HAdnwSwv6VSWbcVdNyzD+5cXwmkxZrocmiV21Pbis4/tn1UDkYmmky9cvRD/9qEV41pWSoln9rfiuy+fQDQx/i6I6TQ/z4Yffmw1lhQ5UOg0j+imTkQ0F9X1+PHh+3ambV/9r3+1HF+8ZtE5l+sPxnDTz3ciHFfwiXVluPXSeVg4RoOr5w+24ZvPVUGvE3j+zsuxdl5OqsumGaiq1YOddW7U9wbgDsTw0TWluHFNyVBgPhxT0OOPIM9hht2kR0xRUdMVwLF2L36/rxnH231pq+33n38frliUn7b1ExHRtMaA81RgwJmIUs0XieMDP9uOLl/qplwsdJrx8XVluHltOZYVO1O2XiKa26SUeKexH8/sb8WrxzqnzQWZqZJrN+GW9RX4/IaF4+6ycC6Dx+K8iEREg2q6/XhidxMUVaLQZUGOzQhfOIH+YBQSwMXzc7B+QS5Ks62ZLpVo1pBSorU/jIMt/bAaDVhW7MS8XBv0Z3RZPtQygG88cwRNfeffbc+oF3jpy1dhZSmnBafZKxJXcO9btXhoewPDlRNgMuiwYXE+lhQ5Uewyo7LAgQ2L81PW9T2aUFDd5cexdi9cFiM+vLqEHeVnqWf3t+LuF4/N6sHIRJl27fJCPPLZ9edcrqUvhLtfPIYdte4pqGpyHGYDLizPwuUL83DF4jysrcjh6wMRzTlxRcUnHtiNo23etG0jx2bE9rs2nXVQY0JRcduj+7C7vm/oNiGAmy4sxVeuXYzFhe9e963p9uMjv9iJSFy7VrMgz4Y/f2UD7GY2wZqrAtEEfvz6afxmbzPeGwXLd5hxzdICVHf7cKrTP/Re3WrUI6GqU/beYWGBHa99dcO4upMTEdGsw4DzVGDAmYhS7V//cBRP7WtN2/rn59lQ5LIg12aCw2KAXgjodMAFZVm4eW05rCa+eZgMKSX2NvSjqs0DKbWTC0uLHFi/IJfdlmjWkVJia00v7t1ciyOtnkyXk3FryrPw9Bcun/T+s8MTxgsH23C41YMjrR4MhGKwGfVwWY34wUcvwHUri1JcMRHNBAeb+/HLLfV4+3TPuJa3GvXQ6wT0OoELy7Pw8bVl+OCqYl7AIJqgl460479ePYVuX3TY7WaDDosLHVhW5ITFpMfT+1qQyozm8mInXvqnK3kxh2YFRZVodAeHgrNVrR4ca/ciEE1kurRZYVmRE1+/fgmuX1mMkx0+bKnuwekuH6xGA+xmPaxGPUwGHYx6HRRVIpJQEI2r6A1E0eOLoNcfRTCmIBJXEIopwwLnK0pcuOuGZdi4tICDLmcJRZX4n9dO4eEdjZkuhWjWW5Bnw9ZvbUJCUfHYria0DYRw+aI8XFqZh0AkgdNdPhxu9eCxXY1DwbOZYmGBHZ+9YgFuXlcOB99jEtEcce/mWtyzuSbt2/nadUvwteuWjnn/D145iUd2jn4sJwRw6YJc7bqv3YTtNb1ocAeHLXPrpRX475svTGnNNL3FFRUnOnzY19iHx3Y1odObuqZq6bJ2Xja+ePUiXL+yaFiDASIimvUYcJ4KDDgT0WREE8qoF65317nxqV+/k4GKNDk2Iz592XxcsSgf8/JsKHZZ+CYiSUqJEx0+5DlMKHZZIISAokq09Iewp74PT+5pwuku/4jH6XUCK0tcyLYZYdAJFDjN+PyGhVhSxE7aNDMEowl0esNo90RQ3xNAbU8Ah1sGRv17n8s+sLIID3z64gntMzs8Ydy/tQ7P7G8dcyS8TgDfuXElPndlZapKnRKDIfi2gTAKHGYUZ1lwQakLBr0u06URTXvH2rz46ZvV2Frde97rspv0uPvDK3HrpRUMKRGNwyM7G/GDV05mbPt3XrMI3/6r5RnbPtF4JBR1xDFdS18If/PgbigqYNAJDIRic252l0ywGHVpC8gVOM24tDIX76vMxRWL8rGowD5njiW6fREcb/dCCGBJoRNl2dYZ0bXUHYji7dM9aO0PobU/hC5fBO5ADL3+KLzheKbLI5oT9DqB5+68HP/vpeNpncY9k1wWA768aTFuv2IBLEYOzCOi2et0lw833rcTiSmYfcZhNmD7XZtGzBLZH4zhvrdq8fjupvPexvc/sgq3X7HgvNdD56fdE4ZeCOQ5TDDqdVBVCX8kgUAsAQFAJwSyrMZJN9OJJhTcv6UeD+9oQCimpLb4KTIv14b1C3IRU1RE4woMegGr0QCbSQ8JiYQioUqJPIcZJVkWVOTasG5eDrKsbDZGRDRDMeA8FRhwJqKJqOvx47636vCnox1wmg0oy7GhPMeKebk2zMu14ZGdjWjpP/8pjlPFYtTh1kvn4UvXLEKhy5LpcoY52DyAl460Q5USOiFgMeqRbTMix2bC4kIHVpdlpewk6zsNffjpGzXY19QPQDuRW+iyoKU/hNgkLtoKAXx0TSm+tHExlhUPDzqHYwosRt2cuXA4U6mqFniv7w3ggjIXFuY7RlzwlFJiX2M/egNRGHQ6WE16rCp1Id9hzlDV59brj+LHr5/Gjlo3ogkFkbiKcHxmngTJhH+4qhLfuXHluJat6/Hj4/fvhj8yvi52/3BVJf79wytmxL6hptuPH7xycsRUq1lWI96/vBDXryzCpmWFnDFgmpNSoi8Yg14IZNuMM+Jvbybr8ITx6rFOvHqsE4daUt8Z/7oVhfifT1w4rV+DiDLJG47j/i11eHB7Q0br0Ang17dfgvcv5+wNNH30+CN47VgX9jf142ibF4oq8co/X4Wc5MV3KSU++9h+bKs5/4E5NH2VZFlwzdICfHxtGS6tzJ1Vx4ahWALba3rxxolu7Khzo9c/vIO/xah18F9S6MSSIgcq8+yYn2dHZb494+9ppJSo6wng0V1NeOFQ26TOURERTUZplgW3XbEATosBOiFQ5DLjikX5I87HSynR3BfCoZYB6HUC+Q4zynOsmJ9nz1DlRDTTReIKev1R9Pgj6PFFMRCKQ0nmWxYXOHDZwrMfqza6g3juQCtOdfpw761r4RplBlYpJW59eC/2NvSn7ed4r89vqMTdH9auLQwEY/jt3mY8uL0hZbPgCAE8+OmL8YFVxQC0QX3PH2zD+gW5uHh+DptdpUm3L4JDzQPYVe/G9hr3sAyAw2xAMJbAe+NZQgCVeXYsL3Hikvm5uHppwbgGnB5o6se3/3AMdT2BdPwo05oQwMoSF65OvmddOkaTsdb+EA409+N4uw813X5ICRj1Aka9DhajNhuSzayHy2KEy2rEVYvzR1zHJyKilGPAeZAQohzAfwC4AUAegE4ALwL4vpRy4DzXzYDzHPXqsU5sre5BTXcAVyzKw7c+uGxWndyn0VV3+fHnY51YVuTEJQtyUDTO0G9rfwg/eaMaL1d1jHijMhOYDTrcsr4Cf3tJBVaVutLytx5LqOjyRuCLxKGoEoqUkFJCUTEUYtYJwBeJ45GdjdhV13fW9Rn1AheUZeHW9fPw0bWlE5rquccXweZTPTje4cXRNk9aO24sK3LiwxeWINduwlunurGrvg83ri7B/3ziQpgM7HQ6XcQVFdVdflS1eXCweQDba9xwB9696Ok0G7C6PAtrKrKxpjwLTX0hPL2vBU19IwcsLMiz4aKKbFTmO1BZYEdlnh2VBfZh0ztKKeENx+EORNEXiCHbZkJptgXOUU62pYI/EsezB9rwf2/WwM9po8/LhiX5+Np1S3Dx/NwxlxkIxvCx+3eheZS/j7O564Zl+MeNi8+3xFFF4graBkJwWozjfm17L3cginverMFT+1pwrsYWVqMe164oxBWL8mE362Ex6uG0GJBnNyPPYUKuzTQjuqTNNvW9AfzxUDt217tR1xOALxnAN+oFirMsuHxhHjYt035vWbbR90eBaAKnOn1o6A2gfSCMTm8E+U4zlhY5sCDPjkhchScUgyccRzSuIKaoiMRVBKIJ+CMJZNuMWFuRjbXzcpBrN0GVWkcGXzgBTygGdyCGpr4g6noC6PFHUeQ0ozTbiisW52F5sWsqn66UONjcj19ta8CbJ7vTvi2n2YDrVhbhhguKcfWSgowHcmh8vOE49jf240irB0fbvegLROGLxBGJq8ixGZFnN6PQZUZZthXlOTYsKRo50C+uaAOW4gkVep1Atm14R6BIXEF9bwA2kwF2kx55DvOMv7AlpcShFg8Otwyg3RNGhyc8FPrSCQGX1TjU2eVAcz9OdvjO+do1VSxGHX7/+cuwbl5OpkuhWcIfiQ9dvGvuC6HAaUZlvh2l2RaoUttHROIK/JEEfOE4QjEF0YR228HmAeyud4/4/9i4rACP3r4eOp3AK0c78E+/P5yZH44yYkGeDR9bW4YrF+fjwvKsCZ1vORcpJVSJKXkdCscUPLyjAb/aVj+p7mZGvcBlC/Nw/coi3LCqOK2NAaSUqO8N4kSHF3U9AdT1BNDUF0JLXxDBGdqZjYhmn8H3nPPzbOjyRtDhjeBUp2/EwBEAWFOehVsvnYcb15QOOydKRDNbMJpAMJZAvt08dG43ElfQ7gkjEEkgpqiIKyqsRj0cZgNMBt3QjBO+cBxq8gKqQa+Dw2yAw2xAhzesnRNp86C1P3zOmSnWVGTjHzcuwvUrioadX/aG4rjrhSr85cS75+DWL8jBE39/KWym4fuhV4914h9/dyhVT8u4mAw63P2hFXj7dA921bnT0jnaYtTh0dvXY19TPx7c1jDU3CbfYcIn1pXji9csGtFFeq6KJhQ09AZR0+2HL5KASS+g1+kQjivwR+IIRBIIx7UmQbGECgnt95VQJEIxBaFYAs19IbR7wimppzTLgrXzcrC6PAvLi51Qkp2f3YEoDrd6cKh5AJ3eSEq2NRusKHHhykV5qCywY0GeHTXdfrx0pANHWifeWOS6FYW485pFWDcvh9esiIjSgwFnABBCLAKwG0AhgJcAnAZwKYBNAKoBXCmlPHtK7uzrZ8B5jvqHx/fjrdM9Q9//3fvm4QcfvYAHNrNIQlHRH9LeWLf2h/H0/pYRU4UXucxYmO/AwgI75uXaUJxlQbHLAofFALNBDyGA3+1twW/2NiGuzI7967IiJ96/ohAmvQ5CADaTHhU5NlTk2rCs2Amjfvyh3B5/BL/e0YiXjrSjxx9NW/i70GnGbZfPx8ZlhVhZ4hrz/1RKiWf2t+KHr54ad1fVdLlqcT4e+PS6tAVaZ5JYQkVTXxAtfSHk2E2oyLEi32Ee9ntsdAfx2K5GnO7yozzbivJcG25ZX4GybOuEthWJayctanv8qO7yo64ngEZ3EM19IcSU9HZCKnKZYTcb4A3F4QlrQf/3spn0EAAUKWE16rG6PBsXVWRjZYkTpdlWlGZbYTboEI4pCMcV5DnMIy4SBKMJtPSH0NwXQm23Hztq3TjUMjAlU63NJVctzscnLi7DxqWFQ93tAO3v+TOPvIN3GifX/eHeT16Ej15UNuJ2RZXaFGYTOA7xhuN4YncTntnfig5veGgfvKrUhfcv10Ksa+dln7Mbvj8Sx2/2NuP+LfUp6yhhN+mxtNiJpYVOxFUVvf4oBkIxVOTYsLTIiXm5NoTiCnzhOILRBBKqNjVZTHm383iR04ILylxYWerCgjz7tJ46VVElun0RBKIJ2Ex62E2GSXVNjisqQlFlKBSsSgydXAW0UB+ghTlCMQWeUAwNbi0sfKB5AFUTOMHnNBtQnGXR/r6lNhipPxhDY18wI4O5hAA+sa4c37h+KUonuO+fau2eMN440YWXqzpwOA3dmsfDYTbgpjUl+NtLKnBRRTYHak5D3b4Ifr2jAb9/p2XCwSWDTmBFiQt6nUCHJ4zewPDj7CWFDmxcVoDFhQ5sq+nF1ureYcGubJsRVy7Ox9VL8rGqNAsLC+wjLvYB2lSpdT0BVORaUeyyjPp35A3HcarTh9puP5r6QijNtmJFiRNLi5xpGczS6Q3jlapOPHOgdUZ3rMm2GfH8nZdjceHEu8QEoglE4gry7Kah34k3HEdttx86nTbVqdNs0E4bSkAIAbtZ61Iz2u8wlgzGD4YNB4IxnOr0oa43AH8kgXBMQVxVYdbrYDIkO96Y9LCZ9Mi1a9OFFjktMBuHv0+UUtt3m/Q66HQCUkoMhOLo9kUQiinaNpO1ZdtMcFkMMEzgveZ4qaqELxKHQa+DSa9LDqqJwxeJw2UxosBpnrH7yP5gDI/ubMQTu5vSMojxWx9chs9cPh/X/nTbqMElmhvMBh2WFDlgNuhhSO4n4oqKmKKiJMuKiyq096xFLgtcVi2kEowq8IZj8EUSGLwW0euPJV+TetDrj6LIZUFZjhXvX16Iv764/LxmoVBUiVOdPhxo6oeEdhwUSah4YEsdOlIUAjDoBD60ugSfu3IB1qZwgMrpLh9ePtKB1090oaE3mLL1EhFNF3qdwNIiJ9aUZ2Feng2qKpFQJYx63dCgxGyrEdk2I1wWIyS015mEImE16WFPdlmczud8xiMcU/DmqW7sqe+DLxKHLxxHnt2E1eXZWF2WhZIsC7Js2nH8TD02PVNcUeENx2HQCZgMOuh1ArGEFhLUCQFnmo79x6KqEr2BKDq9EXhCMXjDcQghkG83Ic9hht2sh9mgh8Wo1aoTWrfP8Q7IUpPn3Qff/w4O6IorKkIxBcHksXqRyzKs6Y2UEr5IAn0B7bxoWbYNRa7Mvz+JxBUcbvHgcOsA+gKxoUYttd2BoTCnUS9Q5LIgllDRk6H3CsuLnfjadUvwgZXFON7hxT/+7hDaBkaGTTcsycfDt10ytB8JxxRc97NtKQumziQOswGf37AQt18xf8Tg+EyKxBVUtXpQ1eZBVZsXbf0hdPki6AvEtLC8xQCrSQ+jTgeDXvv/iCVURBMqbCY9ilxDzz96AAAgAElEQVQWFLnMKM6yoiTLgtJsK1aXZY0Ic0spsbehH4/uasSW0z28ZkbDOM0GrCx1YXVZFtZX5uJ9lbln/T+RUiKaUGHQiSl9TRuNqkoIgYy/ftDsEYhqxycOswHZNtOkBqkHown0BWIIRLUBI5X5dg6ymbsYcAYAIcRfAHwAwFeklD8/4/afAfg6gAellHeex/oZcJ6j3htwBoCPrCnFhy8sQaM7iCZ3EP5oAooiISFRmm1FZb4dy4tdnOolg6SU2NPQh6f3tWJbTS8MOgGLUQ+dThtVGVfUoU5FsyWQPJUWFthx94dW4P3LC0ccJA8EY6hq86AvEIMvEkdtTwAvHGxDdIqnz8y2GbFpWSG+eM3CoU6PUkocaB7APW/WYHf9pMe8pNyiAjsuqsgZmh5HrxMwJkcJC6G90gsB6IWATieQbTViYYEDlfl2FDjNEz6xO9jVLhWdo72hOGp6/Oj1R+GPxLVuYJEE/JE4InEFdpMBLqsRdrMBpuTPpyQ7dPojcfT6o+jwhtE+EEbrQHhE2Nds0KEy345FBQ6E4wq2VPeMCNRZjXr887WLccdVC2Ey6OAJxXC6y4+qVg8Ot3jQ4A7AYtTCF4oq0dIfQrdv9l2YL3KZMT/XDl8kjk5v5JwdDii1dAK4qCIb+Q4zzEY9ur0R7Gua/NR2Rr3Af3z0AnR6I3inoQ8nO31Dr1lWo16bOrnIAaNOh75gDP3BKIQQMBt0sBr1yLWbkO80Q1ElntrXcs7BHEa9wIXl2diwJB+blhVidVkWhAD80QQae4N4en8rXjrSPqmOZ1OtyGVGeY4NhU4zCpzmYSefYgntwoo3HEMkPtjhEyjPsWFVqQsrSlxwWgzQ6wSkBJr7QqjvDaBtIIRoQh12/BCJqwhGtX1eIBqH3WRAgdOMYpcFS4ucWF7iRLHLgsMtHuyud+NwqwftA+ERJ0yLXGZsWFKgPffLC0dMl9jhCWNXnRu76/twtM0Dd/KCwlxnNuhwzdICXL4oD5ctzEOW1YiEIhFJdt041elDgzsIvQAsRj1MBt3QO+doQoUnFIcnHINBp0OB04xCpxkblxXifZW5YwYxVVWOet/gjBBvn+6GOxCDokqEYgnUT7NwSrHLguXJwGlZtlXrkGMxYHGhAwvzR5/+MJZQsaehD+FYAoqqDcBZVOBAWY4Vep1AXFExEIyh2xdFpzcMdyCGSxbkjDk94ETFEipOd/lQ1eZFlzeM+bl2LCywoyTbCqfFAIfJAG84jtNdftT2+BFLqDAl94OLCx1YVuwcNbCbKd5QHK0DITS4gzjZ4cOJDi/eaehP++CqiSjNsqA8x4byHCtcViMOtQzgWLt36PjLZtKjMl/rirIg3wZfOIH9Tf2oTk73OBqDTiDPYUJxlhWVeTZU5jvgsBjgTXZ5FwAcFgMcZiOM+sGLyELbp2ZZkWU1wh2IotsXQU13AG+f7k7rzCtTrSTLgv/6+GpsXFZwzgsQdT0BvHqsEztqe3G4xYOEKuGyGFCZb0d/KIbW/nNfmBVCO3426nUw6nVIqNrr2eD7YotRCwD70jAQ1KTXQUKe9T24EMCCPDuWFTmxrNiJFSUurChxoiLHNub+ORJX0OOLIhxXoKja+/3WgRDqe4Ko7w2gvjeAht7gUNeq0TjM2vO4pMiB5cVOVOY7EE0o8IUTSKgqFhVo+5SxwpfhmNYhvbkvhHm5NiwvGX1QcFxRoUo5ohOulBK9/ijqe4No94SxpjwLiwsd74bXQ3FUd/uTnaMUDCQHGzX2BrGzzp3WYzSdAC5bmDet3kPT7GTUC1y/sgiXL8zDheXZWF7iHPG/klBUuAMxNLi1/+vWgRB6kschJzp8UzqIvchlxtIi7djqfZW5uGpJ/riPO1RVossXwc46N37/TsukOowREc1FTosBhckZnhbm27GwwIHsM2aeKnZZsKjQMWwQ4EQNDsjr8IRhNemRYzMhy2oc17W+UCyBdxr6EVdUuKxGWI16dHojaOkP4lSnH2+c6BrXwFaTQYerFucPzcxkM2sDjBKqRH8ghr5gFIGoMnTdIJbQZswKRhV0esNo6guhfSAEo14Hp8WIAqcJq0qzcFFFNubl2RBPaIOU4gk5NHh+8Ged6LWGhKLiaLsXu+vcqO8NoscfQa8/mmxicO7zVw6zAWXZViwudGBRgR2ANnDTE44jGNW6owZjytC1h3BMQUJVoara85RtM2rnQR3moXDj4PGDKiW6vBG0DoTQ2h9C68C7M/5MhN2kR5bVCKtJj0hcOy8IADl2E3JsRsQVraFBrz86dN5PrxOjNjYBtN9ZkdMCm0mfPE8ZH3G+MN9hwooSF3JsJrisBthMBi0crqgwG3RYWKA9X9lWE2KKFhofHHyWUCQsRh3syc7IJr0ORoMOwWgCO2rd2FrdA3cghqsW52HDkgJcsShvWCjPF4nj3/5wDH850TWjrp8uKXScs4HN/Dwb/vaSCvz1xeV4al8L/m9z7RRWOP0YdAKXL8rDDRcU46KKbMzPs2ek276UEq8d78IP/3wqLYHz+Xk2LCtyQicEFCnR0hdCdbc/5duh2UkIYFGBA0UuM3LtZlgMOnR6I0PNJkIx7VyUEECW1YhcmwkGvXZ9aXCA1+qyLCwrdiLbZoTTYoQqJWq7A6jp9qNtIAR/RJv1MhjTXueiCRV2s/a6rH0Yk69DBvgj2uvG4HbV5EzBrf1aJ/HBgTtFWRbcsKoYH19XhkLnxGchUlSJHn8EsYQKRZUw6HQozrKMK1ugqhLuYBQ9Pu18riek1RyMJoba9EgJJFQVcUWb6Vun02b4TqhyaDDU4AAGRZVwWrSArc2k167thGIIx5VkpkI7l2k1abPIuqwG5NhMyLYaYTMbYDFor4OBSALecBzhuAJTsoGDXiegqtqApGXFTiwqGP06yZmCydlN/dEEQlGtEZlWr4JgTEk2VEggmtCu6SqqNrBrMASv1SugSm1d/mgC8TNeu94dGKaDO5mjcPujcFqMyLGbUJZtwSULcrF+QS5Wl2VNOO8RS6jo9kVwpNWDqlYPOrzv7nfDMWXo2KTHHx12jkUIINtqhMNigN1kgNmgQyyZu1JVCQit+ZIq5dBgxoFgbMSx769vuwTXrSyaUM00azDgnOzeXAegCcAiKaV6xn1OAJ3QnqhCKeWkrjIz4Dx3jRZwHq8ilxkfWVOK61cW44Iy14Qurg9elBvscjQVVFUiGNMOoELJEAOQDFbqtBdds0EbLW83GYaNQh4IxdE2EEJbcoryLm8YPf4o+oMxeELam/OSLAuKsyyoyLGhMt+GBfl25NhMMOl1MBt1Y3aSGo1MHqy1e8I43enH0TYPjnf4EIwmhqY37/JxipZ0u2xhLpYXuxCJawdsJzu80y7MAwDXrSjC2nnZeOFQ26zshGPS67SRy0atu4A12RXUYTbAqNfBE46hPxjDQCieDB6/G3B2mg3ayb8sC4pdZrgsWhjZbtZOAkqpdREe7AIaiCTQH4phIBhD68D0CgqXZVsRVzLXKYBoNrEa9Ygp6pgn4ik9THodrl6ajw1LClDd7cfuOjea+kKZLmtOKcu24sYLS7CwwI7iLCssBh121bnx1ukenOr0wZU8SWky6IbC7rNhWsA8uwkXz8/BmopsXFCWhfm5NvypqgNP7m0etVun2aCD2TB2AHJJoQM3XFCMdfNzsKY8G9lWIxrcARxq8eBEuxeNfSE0uYPIsZuwcWkBrllWgAV5drgsBuiEwJ6GPrxwsA2vHe86ayBRCJy1k7gQQGWeHavKsrC6zIUil2VoStRIXIHZqIMl+f7GYTbCbtZCnzqhnVCVQLLjpNZd1m42IMuqdXp1Ji/49AVj6PREUNXmwZ76Phxo1ga3ZCUHeIVjCgLRBLyheFo6q9LscWllLr5+3VKE4wkcbB7AyQ4fCpxmzM+zw2zQ4U9HOyfUgX+2sRh1QzMK2Ux6uANRuAMx9PgiaQljjyXfYRoKNBp0AnW9AdT1aB3MztwfmQ06LC9xwW7S9iuKKtGaPGeiFwIXVWTj0spcCAFUtXlxtM0Dz3sCIBW5VlxUkYPTnT7UzuBO5USTJQRgM+phNWkDprWBhdP3tdRk0OF9lblYVZqFRQV2lOfY0OULo6FXmy3KH9GCWv2hGFr6Q5MKWBER0fg4zQZk241wmI1DTS8Gr7nFFC0IKiWSg/4EBLQwhiIlenzREa83Bp1AWY4V83JtWFzowIXlWVhdlg2zQYe6Hu14cE9DH3bVuae80Uuq2Ux65Du0geBFWRYsL3JiVZkLiwocsJm07qkDwRh21LqxvaYXu+rdGZ8lk87PogI7/i3Z0KimO4A7f3sQje7Zdx3tTLpkCIude0fKd5jhMOuHQoNmox42ox5Ggw6RmIJgLAFFlXBZtY77JoNANK6FEH2R+FC37/IcK1aWurCqNAsrirUBzHnvGTAciiWwu64Pj+5q5IBaojTR6wQ2LSvE31xSjvcvLxw2GF9VJXr8UTT1BdHQq80Aqg3e1wbfv3eQixBAicuCilzt/FxFjg16HdDoDqGpTxvkNHgOfKbGERfk2XDtiiJcs7QAl1bmwmLUjiNPdviwu96NbTW92N/UP20GAJkNOqwuy8K6+TlYVGBHkcuCQqcF/cFYsuFDAO2eCLp8YXT7ovCF4xk/VmXAeU5jwFkIcQeAhwE8JKX84ij3D3Z3vk5K+dYkt8GA8xx1PgHnM+kEsKTQifIcqzYqx6BDKKagL6iFgEMxRTu5khyNdOabKpNeB4tRG2mtTdkkEI4pQyFFId6dinzwe2uyW6lBp4MvEsdASNvG4MV6RZWIJbsQTnZ3MDglpQRSEoCyGvUocpnfnRo2OQ26dtJJIpYc5RRXtK57Zws7EBERERHR9GA16sd97G5OhsenO4tRB1WC4SQiIiIiIiIimvEumZ+Dk52+GTGDH81MuXYTil1aM7RoQsH+xoFpNasZ0WyXZzdh/YJc9Ie0BiAdnvCMOA+fKWaDDqtKXajrCUxpc4XZjgHnOW1SAefpM0draixLfq4Z4/5aaAHnpQDOGnAWQoyVYF4+udJopruwPBupHA4gAW26oOQB++DUEunksBhQmm1N6zaIiIiIiIiIiIiIiIiIiGjmuWxhXqZLoDnCbNDjqiX5mS6DaM6JKSocZm0W6Mp8e6bLmREuWZCb6RJmlTxHerNxNPvMtoBzVvKzd4z7B2/PnoJaaJb56nVLMl0CEREREREREREREREREREREREREdGsN9sCzikjpbx4tNuTnZ3XTXE5REREREREREREREREREREREREREREc4Iu0wWk2GCH5qwx7h+83TMFtRAREREREREREREREREREREREREREdEEzbYOztXJz0vHuH9J8nPNeWzj9KFDh04D+LvzWAcREc0Bya7/Y84KQERENFPxNY6IiGYrvsYREdFsxdc4IiKajfj6RkREsxVf44g0sy3gvCX5+QNCCJ2UUh28QwjhBHAlgBCAvZPdgJSSwWYiIiIiIiIiIiIiIiIiIiIiIiIiIqI00WW6gFSSUtYDeAPAAgBffs/d3wdgB/AbKWVwiksjIiIiIiIiIiIiIiIiIiIiIiIiIiKicZhtHZwB4B8B7AZwnxDiWgCnALwPwCYANQDuzmBtREREREREREREREREREREREREREREdBazqoMzMNTF+RIAj0MLNv8LgEUA7gVwmZSyL3PVERERERERERERERERERERERERERER0dnMxg7OkFK2AvhcpusgIiIiIiIiIiIiIiIiIiIiIiIiIiKiiZl1HZyJiIiIiIiIiIiIiIiIiIiIiIiIiIho5hJSykzXQERERERERERERERERERERERERERERASAHZyJiIiIiIiIiIiIiIiIiIiIiIiIiIhoGmHAmYiIiIiIiIiIiIiIiIiIiIiIiIiIiKYNBpyJiIiIiIiIiIiIiIiIiIiIiIiIiIho2mDAmYiIiIiIiIiIiIiIiIiIiIiIiIiIiKYNBpyJiIiIiIiIiIiIiIiIiIiIiIiIiIho2mDAmYiIiIiIiIiIiIiIiIiIiIiIiIiIiKYNBpyJiIiIiIiIiIiIiIiIiIiIiIiIiIho2mDAmYiIiIiIiIiIiIiIiIiIiIiIiIiIiKYNBpyJiIiIiIiIiIiIiIiIiIiIiIiIiIho2mDAmYiIiIiIiIiIiIiIiIiIiIiIiIiIiKYNBpyJiIiIiIiIiIiIiIiIiIiIiIiIiIho2mDAmYiIiIiIiIiIiIiIiIiIiIiIiIiIiKYNBpyJiIiIiIiIiIiIiIiIiIiIiIiIiIho2mDAmYiIiIiIiIiIiIiIiIiIiIiIiIiIiKYNBpyJiIiIiIiIiIiIiIiIiIiIiIiIiIho2mDAmYiIiIiIiIiIiIiIiIiIiIiIiIiIiKYNBpyJiIiIiIiIiIiIiIiIiIiIiIiIiIho2mDAmYiIiIiIiIiIiIiIiIiIiIiIiIiIiKYNBpyJiIiIiIiIiIiIiIiIiIiIiIiIiIho2mDAmYiIiIiIiIiIiIiIiIiIiIiIiIiIiKYNBpyJiIiIiIiIiIiIiIiIiIiIiIiIiIho2mDAmYiIiIiIiIiIiIiIiIiIiIiIiIiIiKYNBpyJiIiIiIiIiIiIiIiIiIiIiIiIiIho2mDAmYiIiIiIiIiIiIiIRhBCNAkhpBBi4yQe+3jysd8b5T6Z/Fhw/lWOu54p32Y6CCHWCyH+JIRwCyHUsZ5jIiIiIiIiIiKimY4BZyIiIiIiIiIiollOCGETQnwpGYprEUKEhBBBIUSjEOJ5IcSnhRDWUR7XNN7w3NmWFUJsTd73+Cj3PX5G8PDMD58Q4ogQ4n+FEOWT+JkXnLGujeezrBDiNiHEA0KId4QQbUKIiBAiIIQ4IYS4Twix5CzrHvNnP2OZJcnfi0w+jwuTtwshxNXJ52CPEKJfCBEXQvQIId4UQnxWCDHqOd73/Ezj+Zh/tueIJk4I8TEhxPcmEw6mqSOEuCj5e/pspms5l+S+ZiuAGwHkAHAD6AYQyGBZREREREREREREaWHIdAFERERERERERESUPkKImwA8BKD4jJuDAFQAC5IfnwDwIyHEZ6SUb091jUlxAP3JrwWAAgBrkh93CCFuklLuzFBtDwEwJ79WAXgBZAFYmfz4ghDic1LKpya6YiHECgBvASgBUAfgWillS/LufwPwn2csrkALMhYAuC758fdCiBullL73rFqBFnw8mxwApuRy7ROtnc7pYwBuT369NYN1ZEongGpoIdzp7CIA3wWwDcDjmS3lnL4AwAZgB4CPSCk9Ga6HiIiIiIiIiIgobdjBmYiIiIiIiIiIaJZKdiR9EVq4uRrAZwDkSykdUkoXgGwAfw0tfFkK4OrMVAoA2C2lLE5+FAFwALgNgAdanc+N1mV6ijwI4FPQwuBmKWUutMDzBgB7k18/JoRYPJGVCiEuhBaqLAFwCsA1Z4SbAcAILfR9D4DLAViklNkA8gB8H1qIeQOAX7933VLK1jOezxEfACoADIaifyelTEykdqJzkVL+q5RyuZTyF5muZRZZlfz8LMPNREREREREREQ02zHgTERERERERERENAsJIdYA+BW0c4CvAlgrpfytlLJvcBkppVdK+YKUchOATwLwZ6bakaSUISnlbwB8JXlTMbSOuJmo5atSyqeklM2DQWApZSLZUfoGaF2VzQBuHe86hRAXA9gCrRvzUQAbpZQd71nsjwAqpZTfkFLuPWPb/VLK7wH4QXK5vxFCzJ/gj/VhAPnJr5+Y4GOJKDMGB3kEMloFERERERERERHRFGDAmYiIiIiIiIiIaHb6T2ih23YAn5JShs+2sJTyGQA/m4rCJuhZAGry64szWchopJReALXJb0vH8xghxOUA3gKQC+AggE1Syp5R1l0lpfS99/YzPH7G1xN9bm5Pfj4spTw6So1OIcRnhRDPCiGOCyE8QoiwEKJOCPGQEGLJuTYghLhMCPGkEKJJCBERQriFEIeEEP8thFg2kWKFEFuFEDJZk1UI8T0hRHWyph4hxNPnqkkIUZDc9jEhREAIEUz+bD8UQuSO8RiZ/Fgwxv0LBpc547aNye8Hn+PvnrGeUZcVQjQlv/8rIcRryZ9JFUJ87T3bu1kI8boQolcIERVCtAkhfieEWDee+oQQVwohXkk+PiSEOCKE+CchxDmvFQghcoUQPxNCNCa33S6EeFj8/+zdd7gcZ3mw8fvZ3dOPdNQtWbIkW7jghjtgwA1sTDXFhB5KgI8v9E4SEkogX2gJAZJAEoJDQgsEAqHFOMRA6DGdBLABg21sY8vq0mm77/fHzOxZrXZPPzpF9++69pqzM/POPFPf2Zln3hOxoc34V+Xzfs1E056siFgTEb8bEZ+IiB9HxJ58O/5PHtukjsGG6SXgvfnXC5u3U0RcNIVpPTUvc23+/YkR8cWI2J73f0TT+PfN99ub8/W5PSKuiYjHR0Q0jXtjHmsRz3sbYrxxKsssSZIkSZIkLRaV+Q5AkiRJkiRJ0uyKiI1kLfQCvD1Pwp1QSilNPNbhlVIaiog7gXXA8sZheSLhhcAXU0oXHf7oICJWAyfkX38xifEvBD4F9ANfAx402e3TwvaGv8uTLRQRaxjbP9q13vwU4B3531VgF1mDGdvyzxMi4hEppWtaTD+APwVe3tB7N9AJnJl/NgBPbSizlbH197SU0lVt4loOfCWfxhBZ8vta4LHApRFxXkrpZy1iui/wCbKkcoDhvOwp+efJEXFpSuknbeY7FcPA7cAA0A3sYxIt7kbES4C3AIlsfdcahpXIEnF/O+9VJWtxfSPwBOBxEfHclNJfjzP9RwMfInsusBPoAO5Btp3vHxGPKVoJb2ETWUL9FmB/HuPRwDOAB0TEWSmlHRMt4yx4JfCS/O9Rsv1qALh7/nlSRDygVdJ+G7eTtYq8HBgB7moaPjydICPi7cDzyLbhQdsyH/5GDj0+VgL3zz8Pj4gnppSKcneQ7UuryLbbbuBAwzBJkiRJkiRpybEFZ0mSJEmSJGnpuQgoWgD95DzGMWMR0UOWwApZUua8i8y6iHgIcDXQR5Zs2i5ZuCh3KfAZsuTmLwKXzSC5GbLk7sIPp1Du8WRJkiPAB9qMcyfwBuA8oDeltJoswfLuwPvJlvkDEdHXouxLGUve/Ctga0ppIKW0nCwp9tmMtXo9Va8lSwS9PI+hH7gAuJks+fP/NReIiC3Av+XD/xo4niyptQ84jWwbHgN8LCImnSjeTkrpqyml9cCH815vSSmtb/y0KHYU8Eay9bUhpbQyX7aP5sNfTpbcnIA/BFbm42wCPkJ2r/+dEXHBOKG9B7gGOC4vuyKfbg14BAcn3DZ7B7ADOD+lVKz3K8iOya3A741Tdjb9Cvh94HSgJ98vu4BzgH8nO1d8oLkF5HbybfGC/OtXm7dTSumr04jxbOC5wKuB1SmlVWT77FcBIuIFZOv6duBZwIqU0gDZ/vg44La8+4qGOM/NYy3ieUFDjOdOI0ZJkiRJkiRpwbMFZ0mSJEmSJGnpuXveHQJmo0Xal0bEsycYZ+0Ew6frdxhL1v7GNKfxsYgYryXWSSW1RsSrgD9uMegXwONTSrePU/w8sqTFLrKE2keklA6MM/5EsZTIkn0Bvp5S+t8pFH9q3v1MSqll668ppQ+16JeAH0fEk8kSch8AXElDYnfeOvRr8q//L6X0+03TuBV49xRibdYFXJpSuqGh35cj4oVkycAPj4jOlFLj9n4DWTLvn6aUmhNxfxgRDwO+RZY0+0jGkooPp27ggyml5xQ9UkqDwM0R0c9YAvEbU0qvbxjnloh4PFmL2PcFXk+W8N3KzWT73VBedh/w5jxJ/dXAKyLibSml/S3KDgEPSCltz8uOAp+MiNeTtTp9JeMnSM+KlNLbW/SrAtdFxBXAt8la5L6A7CWC+dBPtu+/ruiRUtoN7I6IFWTbaBB4YErpew3jHAA+HBG/Imul/GUR8damfVmSJEmSJEk6YtiCsyRJkiRJkrT0rM67O/Kk1JnqI0toHe8za/ca8xaSt0bES4E35b1/SdYKb11K6aKUUqSULppgkisniH3NJEPbS9bq6p0N/W4Enp9Smij5+u5kybnDwP+ZSXJz7o/JWoodZawF2glFxKnAWfnXcVucbiffpz6df71P0+ArgV6y1n5bJYO3m+aN+baMlNJV44z60abk5sInyVo37gLuVvSMiF7gMWStFP9Zm3kPM5bUfOlkY54Db27T/1JgOdm+86bmgXmCb7Gu7xcRrVqIBnhrkdzc5M/IEm6XA5e1Kfs3RXJzk3/Nu8e2ac37sMmX7fP51+b98nCq0mZfAx5NlgB9TWNyc6OU0tfIXppYSXaMS5IkSZIkSUckE5wlSZIkSZIkTeS1DcmnLT9kCcgzcWFEpIhIZMmovyBL+OwBbiVreXa6LZlePEHsx05mIimlt6WU1qeU1pIlfT8Y2AP8W0R8MCI6xin+A+AA0Al8Km/peFryFnuLFn1/L6X0zSkUf0re3c5YknK7+WyKiDdGxHURsTMiqg3b6M/z0Y5uKnavvPufs5DE3cq3WvVMKY0Av8m/rmwYdDbZOg/gBxFxW6sP8NJ8/GPmIObJOAC0THhlLCH9eymlHW3G+RJZYm3j+M2ubdUzb134OxOUbbnegVsa/l7RZpxZFREnRcQ7I+L7EbE7ImoN+2WR7N+8Xx5ON6SU7mwz7Py8e0m7fTHfH4v9cL72R0mSJEmSJGneVeY7AEmSJEmSJEmzrmhpdWVExCy14jzXRoC78r8TsA/4OVmLrH83TmLnvEgp7Qc+GxH/BXwfeBzwTcYSf5t9G3gJWSvUpwDXRMTFU12uiHgIWcvLAbw9pfSWKZQtA0/Mv35gvITxiLgQ+BRZa7OFXWQt/UKWeL6cLNG70VF591eTjWuK9owzrIitMdF8Q94NxmIbT+90gpoF21NKtTbD1ubdW9oMJ6U0GBF3kkDwcckAACAASURBVC3j2jajtS3fMKxd2ZbrPZ9v8XW8BP9ZERGPA97XMK8a2X5ZtEzdT7ZPzmpr0nnScSsvSCl9uKnfHeNMqtgfe5ncvjZf+6MkSZIkSZI072zBWZIkSZIkSVp6/jfvdgEnzmcgU/DVvHXk9SmlDSmlu6WULkspvXmhJTc3SintIUs4Bnj6BON+Hng0MAzcA/j3iFg+2XlFxP2Bj5Ild74XeOEUw72MsQTLf2g3Ut4S9T+RJYteA1wA9KSUVhTbCHhxMfoUYzjcinvguyZqhTz/XDRPcVYnHoXuOY9iAYuItcDfku3/HwbOAbpTSisb9sviBYPZ3i+PavPpaTHueNuy2B//YpL741WzuRCSJEmSJEnSYmKCsyRJkiRJkrT0fJGsFWSAh89nIEeIovXbbRONmFL6NFlrz6PAucBnImLC1mYj4r7AJ8mSXP8ZeOY0WuZ+at79YUrpunHGuzewiaxF7StSSl9OKQ02jdOuNeTb8+6WKcY2V4p4lkfEwDTKF8mq7ZKLpzPNqSpaBN7cboSI6AZWN43f7Ohx5lEMG6/14fn2ILKk+/8BnpBSui6lNNI0zmRa6Z6yWUxALvbHtttSkiRJkiRJUsYEZ0mSJEmSJGmJSSndDHwm//q8ybYSHBELvTXeherYvLt3MiOnlD4OPJksefY+wKciolVLsABExHnAp4Fe4N+AJ6WUJtPib+M0VjCW7N629ebcprz705TS/jbjPKBN/6/n3YvGW6bD6L/JkskDuHwa5Xfm3U1thp87Ttla3p3pcfXtvHt8RGxsM84FQKVp/GYXtuoZEcuAsyYouxAU2+D7KaVa88D8/HXJNKY7W9tpMr6WdxfK8SFJkiRJkiQtWCY4S5IkSZIkSUvTq4AhsqTAD+QtvLYVEb8FvPhwBLaYRERlguFrgKflX7882emmlD4EPJ2spe2LgH+NiK4W078H8DlgOfB54DEtWq2djMeStUJcBd4/wbi78u7xrfabiLgMuLhN2Y8CB4CVwB9NI85ZlVLaA/xL/vV1eTJvSxFRiYj+pt4/yLtXtBi/C3jhOLPfnXdXTDLcdq7Op9UBvKxFHGXgD/OvX04p3dZmOi+JiM4W/V9Itm/szue1UBX75altXsZ4JpNoRb2F2dpOk/ERYB+TOD4iYuVhiEeSJEmSJElasExwliRJkiRJkpaglNJ3geeQJdA+BPhORDwpIlYV40TEQEQ8KiL+E/gw0Db5cyGKiGsjIkXEtXM4m1dGxFURcf/G5NeI6IuIRwFfBdaTtRL8J1OZcErpfcCzybbRZcBHIqKjYR4nkiWcrgS+CFyRUhqa5nI8Je9enVK6dYJxvwLsB1YD74uIDXk8PRHxdLKE4e1tlulO4LX511dGxDsjYnPDMm2IiBdHxEHJnRGxNd+WKSKeOtWFm8ArgbuAE4CvRsTlxXqOzPER8WLgx8A5TWX/Oe8+MyKeViShR8QpZK2kHz3OfH+Udy8v1uF0pJT2MbZvPT8i/qDYF/MWnT8I3JesJeJXjTOpzcDHI2JrXrY3Il4CvCYf/sZxWuxeCK4hO1ZOBd6et0pORCyPiJcBf0mb/XICxXY6OSLuOSuRtpFS2g78Xv71lRHxtxFxQjE8P8buFxF/TXZukSRJkiRJko5YJjhLkiRJkiRJS1RK6T3Ao4DfACcB/whsj4g9EbEb2EmWrHoR8EvgC/MU6kJWIUsOvgbYHRE7I2I7Wauv/wIcn//92JTSdVOdeErpb4AX5F8fRtbadjn//gpgXf736cAvIuK2Np+XtptHnkB57/zrVZOIaSdjSZiPAX4dETvJlvM9wA2MJTG38ibgbfnfzwF+ma+3XcCvgbcCx00Ux2xJKd0IXJ7P+1Tgs8C+iLgTGAR+mse0jSyBttHfAd8AuoC/B/bmy/FD4AzGWu9u5eOMJVbfHBG3RsSNEXHjNBbjLcD7gABeD+yMiLuAm8i2UQ14XkrpS+NM43fIEul/ERE7yFpEfgvZc4JPkG23BSul9BPG9qvnAjvy5dhBFvt/AO+axnSvB75Edqx/PSK2F9spIu41O9EfNL93kLW4nYBnAD+JiL359tybx/Jssla1JUmSJEmSpCOWCc6SJEmSJEnSEpZS+leyZNLnkLU4ezNZIl8FuBH4KPAE4MQJkiOPVH8PvBD4JFlibwDLyRJX/4ssUfHElNLHpjuDPOHxZfnXK8laTS5x8P3blcBR43z6ae+38+7OfDkmE9PbyZLji9acK2QtHL8aOB/YM07ZlFJ6EXABWcvgtwA9wBDwbbLWiN8wmThmS0rpW2RJ/q8gaxl3L7CCbNn+G3g7cGFK6YtN5UaAS4E3kx0vNWAfWaL42cD3xpnnncDFwMeAO4C1wJb8M9X4qymlp5DtH1eTbct+4FayFpzPSyn91QTT+Jc8nk8DVbJWx78HPA94VEppdKpxHW4ppRcDzwK+Q7Y/lfO/X0jWUv10l+FRwF8BvyBbr8V2mpMk45TS64F7AH8DXE92rPeRbc9/B14O3G8u5i1JkiRJkiQtFpFSc4MUkiRJkiRJkiRpsYuIrWRJu6SUYl6DkSRJkiRJkqQpsAVnSZIkSZIkSZIkSZIkSZIkSQuGCc6SJEmSJEmSJEmSJEmSJEmSFgwTnCVJkiRJkiRJkiRJkiRJkiQtGCY4S5IkSZIkSZIkSZIkSZIkSVowIqU03zFIkiRJkiRJkiRJkiRJkiRJEmALzpIkSZIkSZIkSZIkSZIkSZIWEBOcJUmSJEmSJEmSJEmSJEmSJC0YSzLBOSI2RcTfR8SvI2IoIm6MiLdFxMr5jk2SJEmSJEmSJEmSJEmSJElSe5FSmu8YZlVEbAO+CqwDPgH8GDgPuBj4CXCflNL2+YtQkiRJkiRJkiRJkiRJkiRJUjtLsQXnvyJLbn5+SukRKaVXppQuAf4cOBF4w0wmHhHvj4j3z0KckiRJkiRJkiRJkiRJkiRJkposqRac89abbwBuBLallGoNw5YBtwIBrEsp7ZvmPK4766yzzrruuutmIWJJkiRJkiRJkiRJkiRJkiRpyYrpFFpqLThfnHevbkxuBkgp7QG+AvQC9zrcgUmSJEmSJEmSJEmSJEmSJEmaWGW+A5hlJ+bdn7YZfj1wGXAC8B/jTSgi2jXRfNL0QpMkSZIkSZIkSZIkSZIkSZI0kaWW4DyQd3e1GV70X3EYYtES87M79nLXvmFGqjWCoLNSoqtSorNSohRBuRRUSkFHuUS5NNaiegSUIyiXg3IEpQgisv4ptZ5XBATR9J2GskFqKBzRvgX31GYmRZnm4c3Tap5PSomUoE3o9aibhwdQKkXLeCYz3cZpH1y2iDMrW8unU4+fRLWWGK0lqtVEypepGCUrl5WppWzc4u9snmPbqxTZNi6Xgkq5REc56v0q5RLDozVGazWqtUQt5SshoBQHr+9ajfp4AJ2VEh3lEt0dZUaqNQ4MVxmtJVJKVBuWJwG1Wh4j2XQb970ips5Kic5y1kB/LV+WYr3UGrdnvmyt1mmx/1VriZFajWo11fe9Yn8stl00lCnWVfF3qcUMiuUq1gNAuRQtxy361+cRQa1h+au1xEg11ddntZZNm5Qte7Glg2w9ZdM7eB7F9kpk5Yo11DhayveVxn2k2rBeG0PPVnG28Ytt1LhOOhr2nVq+LurrLl/mbJuW6uuwcR8q1kWx/ov1Qb5OxmJosV2nca4oppfajFesm+zv1Pa8Vo+Bg/ehxnVbHKejtRqj+XFbLFexDTvKQRD1cbLtl823lA+vlEoHTb8U2bAAarVs2xX7zGh1LOBS47mbsf2v3LTTFNvjoO1eg5FajdGm/TEbf+x4KOJotV4O+l7UHaWsTKXpGCnOXcUxnq23VC/fqPH4L5Y5W79ZvCPVGhFjdVhRPyWy47ToDo5WGRypMjRSy88r2Rou5XXbSLXGgZEqgyPFcZ0t1+7BEXbuH2H34AhDIzWGq9mnViu2G3RXynR3lunM69BaSty2a3Dc/WkhiYCezjLLuzsY6OlgeU8HKSWGRmvsGxrl5h0HGB6tTTyhFtNd1dfJUcu7qeTnjZRgaLTKgZEqo9VEuRT1bTZSzbZpraGe66qU6OkoUymX2H1ghJ37h+vbqFGpBOVSiXJ+vCzrqjDQ08Gy7g46KyUq+bG3b2iUPYMj7M/rqpFqjZFqjeHRbLsGQaU8dg4rrl+6OrLrpkq5VK/LADryOrBSGjvXF+fYlKC7o0xfV5nuSplSfg4vzpHFOimUS8U5NtuPGs+X9euE4pyfn38b6/1ayo7vSh5Pcf4tlbJzbVF3Fes3pVQ/vsemkYiI+vHbUc6uFSulOGh9pUS9Pm9UXKN0lEp0dWT1eeP0i/lWG64lUiKLt77MY+fZbPnHvieonweKWLJPyuvUWv34h+wapbtSrl/vFuuzuLYq9rnhvFvNz82VconufNvuHhzhrn3D7DowUr9eKUfU64xKKejtqtDbWa7Pq6NcYrSWxTU8WmN4tMpINbsmKXSWS/R1VejrqtDbUa5fm1fKQcTYvpGdA7N1Vs3Pfdk+m12sVUolOvJtlMUX+bEQB+2P1fzaLL+8o7ujTHdHiXJp7J8iVWs1hkazz2jDOt2xf5jte4ez42+0xtBIlaHRWv0aZni0Nuvnu+K81NtZpqejTF9XhdX9Xazt72JVXycd5bFzfrE/kO8/xbVlUQ8W8e86MFKvn0oN4xTrubHeK5WC7kqJ3s4KPZ0lOstlOirZsTGUny9GRmv16/dyBB35NWxWPqtDhkarHBiu5vVL1h0erdW3ceO11tiyj10zlSKy82Bnme6Ocv06oahripiLdVbU+6Wmc0fz74ts+gdfo6X8PFIcV8UxUlwTpLEJZMtffEazY2hwpMruwVH2Do5yYKRa308aY5zJ/jDQk9VRt+4aHLdOioD+rgrLujuy6+2UqNVSfd8ertbya+ZEd6XMUQPdrF/eTU9HuX5tW6yfiLF6qLujfMg1c3GN0nhOLZcOvfYqYijO38X5fDg/fx0YHmXn/hF2HRjhwEi1fl4bHKkummsJyOridcu62bSyh6NX9ABwYDjbD4rfcQkYGc32nT2Do/x65wH2D1fr0yj27cbljoDezuz2X2qoS7LjD0ars38Omkj9d2P+W6+xzi3l9WipFHTm56pKuURKY9e7vZ1levJrx0JRN2X1UnbNOjSaXZ8eGK7OyrHUGH9PZ5muSpmuSomRao2dB0aoVqc+j+KYS8DewdFZi1FaCsqlYO2yLjau6KGzMlZ/37l3mDv2DLHrwDC1qf/MmnX1+q6zQl9nmf7uCv1dFbo7yvV7x43XC8Vvp8GR7PfivuGs/t91YOSgexTSTBXXgEct72bzql62rO5l48oelnV30N9V4cBwtX7ft1wOlnd3sGdwhGot5dfb2XRq+b3P/s4K+4ZHGRqt0d9VoVwKRquJzkqJfcOj9HdWuP43e7nhN3vZNzxav7fWn99f6OuqMDhSZf9wdm1f/A4PyOv7oLezQl9Xmf78t15/V4WuSon9w1V27B9m5/4R7tg7xB17htg3NJpfE1RY09fJMat6OWZVL0OjVTpKJQ6MVNk9OMKOfdk9qZTG7umVS1m3p6PMmv4u1i7rYni0RqWU/V7ZNzyaHZ9DVRJjv/0HR2rsH87679yf/UbaN1St3w/YN5TFeWC4SmelxLplXaxb3kVfV6V+TdxVKdFVKdfPaaO1xN7BUW7fPcjtuwf5zZ4hDjRc3x0u3R2l+rX9Ucu76e0sH/ScodgXinuxteKeCNl9lJ6OMj2dpfr9oEZB5Pedsus8mp4bNN5bLn50F78nUhr7rVXcRyiuZ+vTj7E7q9Gw39Ya7l0M57+tyqWx+1Yd5bF7FM0xN18f1+/D5teUxfV5cY+qFGPPYop1dNBv5OL+cIt7w8W9ncb4x5ZtrHwxj+L30dh9IPJlOPg36sHbYEytYX2WGtZd8du3/uu1/ts1W4fFMpcisnu5Hdl1Ohz6zK3x/v/B67F1TGOxjd27KZ4fFfta/X5fHDx+8Wyx8fdF4/32eiwN67KdxiGN+1mrdTr22ypbZ43zo6F/433H2ji/SbLfMrX6b5rivlhx7+DgeVPfn3s7s3s+HeWD/2l347OrUn7eG7vncfCziKJ/tFlHjfdYG2No3JcaH6w1P3sc7/dm43Ff/BZs3H7FM96iLmpevvoxVxvbp4vhxX23dteLpXyd1FKq35tp3P8gf+YWB//ejKbyxXmv/vwwC5xEorOcnRu7KuX6fcJagp6OMr1dZTpKpex5yUHPl4v7H9l+Viln5+KOStTvMTXuG9Xa2DOa4hna2PPQQ7fF2L33VL+X3lkp0d0xVj8V+8Yh27yWWp6D803f8vgq6ozGezpA/f5j43O5Yplb3TdvjiVbzan+XPegc0+LeqjYvw+eztgztuZ7f+2O2cZjvtV9ruIcXTy/bHyO2Xy+b7xP2Xj8Fdcpjefo4v50cc85Oz+OLXMiu8c7Wh27FzI4Uq2v64PqXcbOncW0RvP9qLezTG9nhWotu682mN+fHMrvzxcqTeefYv8u5lcuRb3fcH7cFMtWPNvpqpTr9yt7OssHP1NvU5cV54fi2KzfQ2qqeGpFfkPDs5DGZzeN9XKrZ/nZvOLg/ToOPS6KbX/QObBNHUjDaK3Kj/eM/eC6pf0z/cbjsZ3m+q05b6IxrnTQ99bHfatzRWpRplWch/RvE2+74Y33OIttNp3ptitTr4+aLxA4eHtN5ld843VDy/m127fqsx1/LuPl5hTzb7d+xssRyeY9Fk/js5F282u8dm+eSOOcVvR20FUpjztvqdFSS3CeNSmls1v1z1t2Puswh6MFYNvafratne8oxoyXqDiV8aY6vDmpb6raXljMeLpZtzzuJdvc6qyU6KQ08YjjKJeyG52zpRwzXyc9zPaFRTRUPlOfduOP3Uo5yK57jtyLn1KLXx7TOZbGOxccPGj+jjEtPbXa2MsTzW66az+f/5/b+drPt7Osq8JRA92s7uvMkhnzhJLsRgncuXeYG36zl5/evodbdw5y594hduwfplIqZTcK8+TeoaaErnIpsgfQXRXK5eIHeXD0im7utq6fY9f0s7y7Uk/QK24wdVZKLOuusLy7g2X5w+tKuf35v1pL3HTXfn6980A98auYV/EDsHio0lGO+sOutcu6ZrVOKAyPjiXWBVny8VzMR9LkDI/W2L5vqJ5EvGdwhL1DVXo6yqzozRIB9g+NsntwlMGRKpVy0FnOknaX5eehznKpfoOpt7PCit6OQx5uaXEarRYJ4emgm4LFzdVSUH8YMlqrsX84S+YsHpqVAtYt76a/K7sCr9USt+4eZPveISqlEp2VqCdpdnWU68kqS0G1ltg7NMqB4Wq9vq2lxI79w9y1b5jrb9/LZ394K9+6ccesJr9OVQQ8+NQNvPABx3P8UcumXH7HvmGGRmss684SgYZHa+w8MMzuAyPZyw19XXRWxj8fjFRr7B0crZ+LEtQTWoqHnaWA3QdG2VFP5hll33CVIHsha0VvByt6O7OXvbor9YdZtUT9BZJiX+3IX0Y5nAZHqvUXYlOi/qLH0GiN3+we5PbdQ4xUa/VjoXhQ19NRZjh/ODharTHQ28Gq3s5Drv1SSuw+MMruwRH2DmVJUeVSZC9YVMoNL8YEB4ar7BnKEr/WD3TXE9D3Do1y265BfnLbHr53807+99bdbFzRw6kbB9i4oocvXX8Hn/vhbdy6a/Cwrjtpsno7y5y+aYAzN69kfV739HVVqDUk9RYvrOwfHuWmu/bzq7v2s3doNE/6K7NldS+nbRzglKOXs2V137jnr1otZcnB+TG3d6jKvqFR7tw7lCUJ7h4CsvNZAn5+x15+ctsefnXXfsY77ZdLQW9HOX9JM3vhsi9/+Wfdsi7WLevi6BU9HLemj+PW9rNuWVfLezPTsW8oO4/sGcxeLB2ppoYX/ccSVlo1UFFPssoTYbryFweHR2sMjmbr5tadg9y88wC37xpkz1A2n2ot0VXJfhP2d1VYnicZLO/Olrm/O0twGBqtUqtRP98v6x67u9f4m7ZaTQyOZi+aFMkzkL0g2JO/mFgke6SU6gkXKY29BLZ/OIt3//AokU+7FMHgSJW9Q1X2N2z30Vqqn2eX92T13qq+Tsol6i883bLzAL/cvp9bdw1SDupJckXiU2e5xKr+Tlb3ddLbWam/PFn8Ns/qi2r+Elp2X6F4Sbg7f5muXApI1PetIrGheDGw2CbFb+/f5ImsewZH60lnjUaq2TofypNui5iKY2VZdyXfH7MXou/cO8Sde4dIiXqS/Zr+8e8p9HSWD7nvu6y7Y9x9dFl3B41XK8Xkl+flTly/jBPXT/16ZiLFy62bVk48bvGQvCizYaBnUvMozjc9+YtUa/q7ph3v4EiVroaXG6Zqz+AId+4dPujFxCLhrkhwakw2LZKlh4uXEvPP0Gg1PycOsX3vECNZpgFdHSWOXd3HtnX9bF7Vy4aBblbPYHklSZIkSZpLMVE2/mISEW8GXgq8NKX01hbD3wk8B/jdlNJfT3Me15111llnXXfddTMLVpIkSXOm1duo+4dH2Z+3olO0FCtJko5sd+4d4vrb99ZbCe8ol7Jkts4K/d0VlnVXqJRK/M+vd/O9m3fy3Zt28sNbds1Kkul5x67itQ8/hbtvWD4LS6IjQUqJH/16N1/72Xa+8YvtfP3nd7F36PC1/NxRjoNabdKRY1l3hbM2r+TcrSs55egBBnrzlrbyFvpLEazuOzT5fyEaqda4a98wd+4dYnCkSmc5+23Y11Wu/xcbSZIkSZIkSbNuWm8CL7UWnH+Sd09oM/z4vPvTwxCLJEmS5kmrVnJ6Oyv1FvIkSZIA1vR3TaqFvvsev4b7Hr+m/v2OPUN851c7+PrP7+Ibv9jOrbsG6/8udqKE097OMq980Ek8+V5bDntLxlrcIoJTNw5w6sYBnnnBcQyNVvnqDdv53A9v4xPfu4XBkTb/f3kW9HdV+N2Lt/Gmz/1k4pG16PR0lFmzrJM1/V31VmHXD3Rz/Lp+Tlq/nGNW9SyZ81VHucRRy7s5ann3fIciSZIkSZIkaQJLrQXnbcANwI3AtpRSrWHYMuBWskzwdSmlfdOchy04S5IkSZIkqaXBkSq37DzArTsHuXnHfm7asZ+79g2zYaCHY1b1cM9jV3P0isn9u3Rpsm66az+v/uSP+MKPfzMn03/6fY7ldy/exj3/5D+o1pbO/eSlpKMc9HVVCCABO/ePjDt+KeDSk4/iWRds4+wtKw9LjJIkSZIkSZKOWLbgnFL6WURcDVwGPAd4R8Pg1wJ9wLunm9wsSZIkSZIkjae7o8y2tf1sW9s/36HoCHLMql7+/qnn8oUf386XfnonP7tjL7+6az/VWqJSCmopa3n8wEh1ytMul4Kn3Wcra/q7uNdxq/jKDdvnYAk0kZPWLyMl2D8ySimCowd6OHpFDycc1c9ZW1Zy2sYBujvK9fH3DI5ww2/2cvOOA6zq62TDQDcDPR3s2D/C9r1DHLW8m61r+uZxiSRJkiRJkiRpfEuqBWeot+L8VWAd8Angf4F7AhcDPwXOTylN+y68LThLkiRJkiRJWox27R/hF9v3cd0vd/DtX+7ght/s5Y69Q+zYP0y728QPOX0Df/mEswD4wDd+xe9//AeHMeIjWwRcdvJRPOfiu3H6phXzHY4kSZIkSZIkTde0WnBecgnOABFxDPA64HJgNXAr8HHgtSmlHTOctgnOkiRJkiRJkpaMkWqND3/rJt569U/YsX/koGGfeM59uMcxWXLtjn3DnPuGaxitLb17ygvNJSet42UPPJG7b1g+36FIkiRJkiRJ0kxNK8G5MttRLAQppZuAp813HJIkSZIkSZK00HWUSzzpXlt42OlH8/YvXM9/33gXt+w8wHFr++vJzQAr+zq597bVfPn6O+cx2qXt9E0DvOohJ3PesavmOxRJkiRJkiRJmldLMsFZkiRJkiRJkjQ1A70d/OFDT65/b/Xf/x52+tEmOM+Bvs4yL7nsRJ56/lZKpWk1ZiJJkiRJkiRJS0ppvgOQJEmSJEmSJC08EYcm2j7wlPV0lE3AnU1bVvdy9Ysv5On3PdbkZkmSJEmSJEnKmeAsSZIkSZIkSZqUgd4OPvLs83n+JXfjHpsGaJEDrSn6vQfdnY0reuY7DEmSJEmSJElaUCrzHYAkSZIkSZIkafE445gVnHHMCl582Yn85LY9/NW1N/Cp799KtZbmO7RF57xjV3H5qevnOwxJkiRJkiRJWnBswVmSJEmSJEmSNC0nrl/GXzzuTP7zJRfx0NM3zHc4i0oE/OFDTp7vMCRJkiRJkiRpQTLBWZIkSZIkSZI0I5tX9/LOJ5zFR599b07duHy+w1kUHnHGRk7bNDDfYUiSJEmSJEnSgmSCsyRJkiRJkiRpVpyzdRUfeta92bq6d75DWdB6Osq87IEnzncYkiRJkiRJkrRgmeAsSZIkSZIkSZo1/V0V3v74M+kox3yHsmA95+JtHL2iZ77DkCRJkiRJkqQFywRnSZIkSZIkSdKsOn3TClsobmPr6l6eecFx8x2GJEmSJEmSJC1oJjhLkiRJkiRJkmbdM+93HBecsHa+w1hwXv3wU+iqlOc7DEmSJEmSJEla0ExwliRJkiRJkiTNuojgjY8+jWVdlfkOZcF4wN2P4uIT1813GJIkSZIkSZK04JngLEmSJEmSJEmaExsGenjlg0+a7zAWhHO2rORPHnnqfIchSZIkSZIkSYuCCc6SJEmSJEmSpDnzhPM2c6/jVs13GNNy5uYVM55GuRS84P7H8+H/c2/WLe+ehagkSZIkSZIkaekzwVmSJEmSJEmSNGcigjc++nR6OsrzHcqUPeG8zTMqf9yaPj78rHvxoktPoFyKWYpKkiRJkiRJkpY+E5wlSZIkSZIkSXNqy+o+nnPxtvkOY0rKpeAhp29gWVdlymVLAc+64Dg+84L7cc7WveE6sQAAIABJREFUxdl6tSRJkiRJkiTNp6nfmZUkSZIkSZIkaYqecb/j+OA3b+KWnQfmO5RJ2bK6l97OCmdsXsGXr79zUmWOHujm4Wds5MqzN3K3dcvmOEJJkiRJkiRJWrpMcJYkSZIkSZIkzbnujjIvv/xEXvCh7853KJNy9/XLAThz88oJE5xX9Hbw2oefwsPvcTQRcTjCkyRJkiRJkqQlrTTfAUiSJEmSJEmSjgxXnLGRszavmO8wJuWk9VkLzBPFe9GJa7n6hRdwxRkbTW6WJEmSJEmSpFligrMkSZIkSZIk6bD5w4eezGLIAz5pw1gLzu3ifcZ9j+Wqp53HuuXdhzEySZIkSZIkSVr6THCWJEmSJEmSJB02Z25eyUUnrJ3vMCZUtOA80NPBcWv6Dhn+1PO38qqHnny4w5IkSZIkSZKkI4IJzpIkSZIkSZKkw+pJ99oy3yGMa1lXhWNW9da/n7V55UHDn3DPzbzm4acc7rAkSZIkSZIk6YhhgrMkSZIkSZIk6bC6+MR1bFzRM99htHVi3npz4awtYwnOF5ywltdfcerhDkmSJEmSJEmSjigmOEuSJEmSJEmSDqtSKXjCPTfPdxhtnbShKcE5b8H56IFu3vbYMyiVYj7CkiRJkiRJkqQjhgnOkiRJkiRJkqTD7rHnHkNneWHeoj5p/fKDvh+/rp9VfZ2884lnsaqvc56ikiRJkiRJkqQjx8K8eyxJkiRJkiRJWtLW9Hdx+anrZzydmIPGlO++4eAE51IpeM9Tzqm35CxJkiRJkiRJmlsmOEuSJEmSJEmS5sWT771lxtM49egB1vTPXqvKEXDS+mWH9D/T5GZJkiRJkiRJOmxMcJYkSZIkSZIkzYtzt67i9E0DM5rG1jV9XHjCulmKCI5Z2UtfV2XWpidJkiRJkiRJmjoTnCVJkiRJkiRJ8+b/XrhtRuW3ru7lkpNmL8H5tBkmXEuSJEmSJEmSZs4EZ0mSJEmSJEnSvHngKes5bm3ftMtvWd3H/U5YQ6UUM46lXAqed8ndZjwdSZIkSZIkSdLMmOAsSZIkSZIkSZo3pVLwfy44btrlt67uZXl3B+dsXTnjWJ5w3mZOWr98xtORJEmSJEmSJM2MCc6SJEmSJEmSpHn1yDM3sX5597TKbl7dC8AlJ62bUQwrejt4yWUnzGgakiRJkiRJkqTZYYKzJEmSJEmSJGledVZKPON+x065XF9nmXXLssTomSY4v/jSE1jR2zmjaUiSJEmSJEmSZocJzpIkSZIkSZKkefeU87dy3rGrplRm8+q++t93W7eMY1b1TGvex63p44n33DKtspIkSZIkSZKk2WeCsyRJkiRJkiRp3nWUS7zrSWezaeXkk5S3ru496PslJ06vFecrz9lEuRTTKitJkiRJkiRJmn0mOEuSJEmSJEmSFoRVfZ285ynn0t9VmdT4WxpacAa44IS1U55nKeCRZ26ccjlJkiRJkiRJ0twxwVmSJEmSJEmStGCcuH4Zf/Swkyc1bnMLzicctWzK87v3ttVsGJh8q9GSJEmSJEmSpLlngrMkSZIkSZIkaUF5xBkbWdPfNeF4m5sSnDeu6KG7Y2q3vR915qYpjS9JkiRJkiRJmnsmOEuSJEmSJEmSFpTOSonHn3fMhONtXd130PdSKQ7pN57ezjIPOm39lOOTJEmSJEmSJM0tE5wlSZIkSZIkSQvOE++5hUop2g7vqpTYMNB9SP9t6/onPY/LT1lPb2dlWvFJkiRJkiRJkuaOCc6SJEmSJEmSpAVn/UA3l51yVNvhm1f1EnFoAvS2tZNPcH7UWZumFZskSZIkSZIkaW6Z4CxJkiRJkiRJWpB++95b2w7bsrqvZf9ta1v3b7ZhoJvzt62eTliSJEmSJEmSpDlmgrMkSZIkSZIkaUG613GrOWn9spbDtqzubdl/si04P/LMjZRKh7YALUmSJEmSJEmafyY4S5IkSZIkSZIWrGddcFzL/lvHSXCOSeQtX3n2ppmEJUmSJEmSJEmaQyY4S5IkSZIkSZIWrEecsZFta/sO6b9l9aH9AHo6yxw90DPuNM/avILjJtnSsyRJkiRJkiTp8DPBWZIkSZIkSZK0YJVKwQsfcMIh/be2SXAGOK5FQnSjK88+ZsZxSZIkSZIkSZLmjgnOkiRJkiRJkqQF7aGnb+Ck9cvq33/3om1sXt3bdvxt47TO3N1R4qH32DCr8UmSJEmSJEmSZteCTXCOiOMj4hUR8YWIuCkihiPi9oj4RERcPEHZp0TENyNib0TsiohrI+Khhyt2SZIkSZIkSdLsiQhedOkJRMCrH3YyL7/8pHHH37aufYLzZSevZ3l3x2yHKEmSJEmSJEmaRQs2wRn4Y+BPgaOAzwBvBb4CPAT4QkQ8v1WhiHgLcBWwAfhb4J+A04B/i4jnzn3YkiRJkiRJkqTZ9sBT1vOPT78nT7vPsROOu21tX9thv3XOMbMZliRJkiRJkiRpDlTmO4BxfA54Y0rpO409I+JC4PPAmyPiIymlWxuGnQ+8BPgZcG5KaUfe/83AdcBbIuJTKaUbD9MySJIkSZIkSZJmyX2PXzOp8e62tnULzpectG7S05AkSZIkSZIkzZ8F24JzSumq5uTmvP8XgWuBTuD8psHPzrtvKJKb8zI3An8JdAFPm4t4JUmSJEmSJEkLw7rl3SzrPrh9j97OMq+74pR5ikiSJEmSJEmSNBULNsF5AiN5d7Sp/yV593Mtyny2aRxJkiRJkiRJ0hJ1XFMrzi++9AQ2reydp2gkSZIkSZIkSVNRmXiUhSUitgD3B/YDX2ro3wdsBPamlG5tUfT6vHvCJOdzXZtBJ00+WkmSJEmSJEnSfNi2to/v3bQTgNM2DvC0+xw7zxFJkiRJkiRJkiZrUSU4R0QX8H6gC3h5SmlHw+CBvLurTfGi/4o5Ck+SJEmSJEmStEBcefYm7rFpBRsGujnjmBWUSzHfIUmSJEmSJEmSJmlOE5wj4kZgyxSKvD+l9KQ20yoD/wjcB/gw8JYZBziOlNLZbeK4DjhrLuctSZIkSZIkSZqZ87et4fxta+Y7DEmSJEmSJEnSNMx1C84/AwanMP6vW/XMk5v/CXgM8M/Ak1JKqWm0ooXmAVor+u+cQjySJEmSJEmSJEmSJEmSJEmSDqM5TXBOKd1/ptOIiA7g/WTJzR8AfjulVG0xr30RcQuwMSI2pJRubRrl+Lz705nGJEmSJEmSJEmSJEmSJEmSJGluzHULzjMSEZ1kLTZfAbwPeFpKqTZOkS8ATwYuB97bNOxBDePMxI+//e1v/xh44gynI0la4iLiOoCU0tnzHYskSbPJOk6StFRZx0mSlirrOEnSUmT9JklaqqzjpExpvgNoJyK6gI+TJTe/h4mTmwHelXf/ICJWNkxrK/AcYIhDE5+nJKX0xJSSyc2SJEmSJEmSJEmSJEmSJEnSHFjILTi/C3gwcCdwC/BHEdE8zrUppWuLLymlr0bEnwEvBr4fER8FOoHHAquA56WUbpz70CVJkiRJkiRJkiRJkiRJkiRNx0JOcD42764B/mic8a5t/JJSeklE/ICsxeZnATXg28CbU0qfmoM4JUmSJEmSJEmSJEmSJEmSJM2SBZvgnFK6aAZlrwKumq1YJEmSJEmSJEmSJEmSJEmSJB0epfkOQJIkSZIkSZIkSZIkSZIkSZIKJjhLkiRJkiRJkiRJkiRJkiRJWjAipTTfMUiSJEmSJEmSJEmSJEmSJEkSYAvOkiRJkiRJkiRJkiRJkiRJkhYQE5wlSZIkSZIkSZIkSZIkSZIkLRgmOEuSJEmSJEmSJEmSJEmSJElaMExwliRJkiRJkiRJkiRJkiRJkrRgmOAsSZIkSZIkSZIkSZIkSZIkacEwwVmSJEmSJEmSJEmSJEmSJEnSgmGCsyRJkiRJkiRJkiRJkiRJkqQFwwRnSdIRKSJujIjU5nPbNKZ3/4j4eETcFhFDEfHriPj3iHhwi3GXRcQbIuLHETEYETvyce8/zvSPjYh35WX2R8TtEfG1iHhWRHRONV5J0tI1G3VcRDx1nGkUn2qbsudHxGci4q6IOBAR34+IF0ZEeZz5PTQiro2IXRGxNyK+ERFPme46kCQtTYupjouIMyLiNRHxlYi4NSKGI+KWiPhgRJw103UhSVo6FlP91qJsRMTnG+ZRmeryS5KWrsVYx0VEV0S8JCK+FRG7I2JfRPw0Iv4hItZOd11IkpaWxVbHRcTyiPj9iPhuROyM7HncDyLij63ftNB5o0GSdCTbBbytRf+9U5lIRLwJeBlwM/BJ4E5gLXA2cBHwmYZxVwL/BZwM/Ah4F9APXAFcExHPSCm9p2n65wL/CfQAnwM+ASwHHga8G3h0RFyeUkpTiVuStKTNtI77LvDaNsPuB1wCfLZ5QERcAfwLMAh8GLiLrL76c+A+wGNalHku8A5gO/BPwDBwJXBVRJyWUnrpJGOWJB0ZFksd9y7gnsB1wMfy+M4AHgdcGRGPTSl9bJIxS5KWvsVSvzV7LnBxXr57krFKko4si6aOi4j1wNXAacBXgL8FqsBm4IHAm4E7Jhm3JGnpWxR1XEQMAN8ETgD+G3hvPugC4FXAUyPinJTS7ZOMWzqswlwoSdKRKCJuBEgpbZ3hdJ4J/A3wD8CzUkrDTcM7UkojDd//Ang+2QPux6aURvP+68guJtcAJ6SUbm4o82ngwcBTU0r/0NC/j+xC9GTgwpTSl2ayLJKkpWG26rhxpv814F7AFSmlTzb0Xw7cAAwA90kp/Xfevxv4AnBv4PEppQ81lNkK/BjYB5ydUrox778S+BawDTg/pfS1uVgWSdLissjquOcBn00p3dA0jyeSvdCzHTi6+TekJOnIs5jqt6bpngh8B3g72Qs8W4CO4n6nJEmLqY6LiBJwLdmLqlemlP6taV4BlFJKLVvSlCQdWRZZHfcy4E3Ae1NKT2+az1XAU4BXp5ReNxfLIs1Uab4DkCRpsYqILuANwK9okdwM0JjcnHtk3v2jxpv9KaXfAH9G1krz05vKHJd3P9nYM6W0D/iP/Kv/NkSSNOci4jSyGyq3AJ9uGnwlWX30oeKGCkBKaZDsDXCA/9tU5ulAF/DOIrk5L7MD+JP867NnK35JktqZ7ToupfSO5uTmvP/7geuB1WStgkmSNGfm4DdcMd0K8I/Az4FXz3LYkiRNaA7quEeQtZb5583JzXnZZHKzJOlwmIM6rsg3OaR+YywHxXwTLViV+Q5AkqR51BURTyL711L7gO8DX5rCDYpLyS703gbUIuIhwKlk/wrkm21am1yfd3/eYljR7/5A49txPwJOAh5C1tIXABHRS/ZvSfYDtmwpSWo00zqunWfl3fe0mNYlefdzLcp9iay+Oj8iulJKQ5Mo89mmcSRJgsVTx42neBHWFi4lSYXFVr+9CjgTuHdKaShr1FKSpJYWSx33hLz7wYg4CngosA64Dbg6pXTLDOOVJC09i6WO+1HefQjw8aYyD82718wgXmlOmeAsSTqSrSdraaTRLyLiaSmlL06i/Ll5d5Ds3zGe2jgwIr5E9m+s7mjofSewATgW+J+m6RVvzp3Y1P9VwPnAVRHxW3m55WQXm5V8Hr+eRLySpCPHTOu4Q0RED/AkoAr8XYtRivrrp80DUkqjEfEL4BSy+u5/J1Hm1ojYB2yKiN6U0v7pxC1JWnIWSx3Xbl73Ak4ma4Hlh9OJV5K0JC2a+i0izgX+APjTxhbDJElqY7HUccUzv/PIGjbqbSg2EhGvSym9fjrxSpKWrMVSx/0d8Hjgd/LWob+S978f2X3KP0gpfWI68UqHQ2m+A5AkaZ68l6yl5PVAH9m/Bn43sBX4bETcYxLTWJd3XwYksgvAZcDpwNXABcBHmsoU/0LktRFRLnpGxFrgRfnXlY0FUko/Jrux8lXgYcAryP6tyHqyFp2/PolYJUlHjtmo41r5LWAF8LmU0k0thg/k3V1tyhf9V0yjzECb4ZKkI8tiquMOERGrgPflX1/kvzeWJOUWTf2WP2z/R7IWwF7XqpAkSQ0WTR3H2DO/vwauIksMWwE8GtgB/HFEPHWa8UqSlp5FU8ellAbJWn5+N9mLPC/KP+cAnwH+dZqxSoeFCc6SpCNSSum1KaUvpJRuTyntTyn9MKX0bODPgB7gNZOYTFGPjgIPTyn9V0ppb0rpB8AjgZuBCyPi3g1l/gi4CbgS+G5EvC0i/pbsocBd+Ti1xplExJlkyc09jCVRH5NP68XANyLCxC9JEjBrdVwrxb/EevdsxClJ0lQt5jouIvqATwDHA29KKTW/DCtJOkItsvrtTWQJX09JKY3M4nQlSUvQIqvjimd+16SUnpNS+kVKaVdK6WPAM/JhvzeL85MkLWKLqY6LiNXAvwOPAB73/9u793jbxnrx458vtkvIJUqFti5yLUmiTXZCKCoHieN6KAdF+B0VZSu6OTkoxSkil0gKFRIhQo5Ch1xy2dgO1SaXTQjf3x/PMxl7mHOtudZeMrf1eb9e8/Ws+YznNsac8zX2HvM7vwNYrD62osSf/DYiVh+r+aSxZoCzJEkzO7qW7+6j7YO1vCYzpzY3ZOZjlH8kQvkVXKf+Xko25qMogcq7Ae8HTgO2qM3+0mkfEXMBPwQWBzZpBFFPy8yvAN+gfEHeyf4sSVIvIznHzSQiVgTeRfnxzjk9mg2XbblT/2Cjrt8+vX6JLkkSDOY5rjnH/JS7+awFHJaZ+410nZKkcWmgzm8RsQ6wO3BwZl430jVJktQwUOe41t8/6dL+HOBJYFkTDkmShjGI57ivA+sAH8vM0zLz/vo4Dfg4sADlx6zSQDLAWZKkmf21lvP30fbmWnb9Eptyyyoov9B7Vv0V3x6ZOTEz587M12TmJ4Cla5P/aTRfDngjcGNm3tdljotq+fY+1itJGt9Gco5r6/xi/NjMfLpHm855cdn2hvqDnWUodz24vc8+r65rnVZ/OCRJUi+DeI7rbF8QOJfyJcLXMnOfUaxRkjQ+Ddr57W1AAAdFRDYfwOtqm3/UulVGsWZJ0vgxaOe4Zp/nfedX53m4Pp2vvV2SpIZBPMd9oJYXtftgvIlmAwY4S5I0szVq+bwvpru4EEhghYjodk5dqZZ39Dn3drU8pVE3Ty0X69Fn8Vo+2ecckqTxayTnuGdFxLzAtsDTwLFDNP1VLTfssu3dwMuAyzPziT77bNRqI0lSL4N4jqNm9jqfcqvHQ8zcLEkaoUE7v11fx+v2mFHbHFef3z+SNUuSxp1BO8cBXFDLldodIuJVlO/pZgDTR7JmSdK4M4jnuE7MyeLP72K8iQafAc6SpHEnIpavtwhu108EvlmfntSonxARy0XEG5rtM/NO4KeUzMt7tsbaAHgf5Zfe5zXq54iIBbrMvS0lwPly4MzGpuvrGEtHxM6tPgsD+9anF/beY0nSeDFW57iWLYBFgHMz8+4h2v2IcoF/q4hYrTHHvMDB9em3W32+BzwB7FHX2OmzCPDZ+vRoJEnj3ux2jqvnsgsoX2ocmJkHDDG+JGmcmp3Ob5l5QWbu3O3BcwHNH691Q80rSRoHZqdzXHUc8Biwe0S8vtFnTuDQ+vT0zHxqiHklSePAbHiOu7SWBzYT99Vz3EH1qfEmGliRmS/2GiRJ+qeKiCnAPsCvgTuBR4A3AO8H5gXOAT6cmU/W9hMpWZjvzMyJrbGWpAQlL0X5R981lNt+fIiS3XmrzDyj0X4B4M/AL4HbgGeAScCawI3Aepn5f605tqcEgEVjjkWATSm/qLsSmNzOFiZJGn/G8hzXGPNSYC1g08z86TDzf4hyceVx4FTgAcr56s21fsts/Sc0Ij4BHEn5Qvw0yq/ENweWBL6emfsiSRr3ZrdzXERcBEym/L/vpPZ41ZmZee1Q80qSXtpmt/PbEONMBV4HTDDwS5IEs+c5rvF93AzgJ7XPZGAV4BZgUmaawVmSxrnZ7RwXEStTgpwXAm7guSzQ7wVWoARMr5mZt/Z9EKR/IgOcJUnjTkSsA+wKvA1YApifkiX5WuBE4MTWP/gmMsQ/OCNiceDzlH80vhp4mPIPxC9n5lWtthMomSjXogRuAfwJ+CFweGY+1mPN7wb2ogRCL0bJdnkzcHrt9/jIjoIk6aXoBTjHLQ/8EZgGTMzMp/tYwyRgf8o5a17gVkoGlCN79Y+ITSh3JViVcqehPwLfzMwT+thtSdI4MLud4xqBXkPZMTOPH25eSdJL1+x2fhtijKkY4CxJaphdz3ERMRn4NPDOuua7gB8DX8rMB4fdcUnSS97seI6LiGWA/YD1KXEqCdxNuRv5VzLznr52XnoRGOAsSZIkSZIkSZIkSZIkSZIkaWDM8WIvQJIkSZIkSZIkSZIkSZIkSZI6DHCWJEmSJEmSJEmSJEmSJEmSNDAMcJYkSZIkSZIkSZIkSZIkSZI0MAxwliRJkiRJkiRJkiRJkiRJkjQwDHCWJEmSJEmSJEmSJEmSJEmSNDAMcJYkSZIkSZIkSZIkSZIkSZI0MAxwliRJkiRJkiRJkiRJkiRJkjQwDHCWJEmSJEmSJEmSJEmSJEmSNDAMcJYkSZIkSZIkSZIkSZIkSZI0MAxwliRJkiRJkiRJkiRJkiRJkjQwDHCWJEmSJEmSJEmSJEmSJEmSXgIiYvOI+EZEXBoRD0dERsRJYzT25DrecI+lZnWuucZiwZIkSZIkSZL0zxQRE4E7gBMyc4cXdTGSJEmSJEmSJA2OA4C3AjOAacByYzj2VOCgHttWBjYDrs/Mu2d1IjM4S5IkSZIkSVIVEetHxNcj4sKIuL9mmrisz76bR8QvImJ6RDweEXdFxFkRscYI5p8yTNaLDYfou2pEnBIR0yLiiYj4c0RcEhHbdWk7d0T8R0RcFxGP1Swel0XElsOsb6GI+EJE/CEiZtR+10fEMRExodFuYp9ZPNZu9Nmhj/ZPt9YzISL2jIjvRcS1EfFkbbfzEPswtY95PjfUcWiNN9y6d221H/GxafRdMiKOi4j/q6/x1Ig4PCIW6bG2OSNim5qp5b76Wt9Sj9eKXdqvUt+Dv4mIe+vxvCcifhARq/Z5PBarfYf87IxiX4Y6Vlf2szZJkiRJkiRpnPgUsCzwcuDfx3LgzJyamVO6PYAna7PvjMVcZnCWJEmSJEmSpOfsDnwQeBy4FVh0uA4RMRdwArA18CfgNOAhYAlgTeDtwEgDME+gZMJou7XHGvYAjgD+BvwcuKeufSVgY+D7jbZzA78AJtc5vkdJhrExcFpErJSZn+8yx3LA+cBrgQuAc4EJwERgS2Af4B+1+YP0zuKxFLATcD9wVaP+2iH6rA2sW+dsmh84vP79Z+C+Ov5QDgcW7lIfwGcp183b8/TjLMo+tF3dej6aY0NEvAG4HHhlnesmYHVgT2DDiJiUmfe3xjuF8tpMA34MPELJorI9sHVEbJSZv2q0Pxp4J/C72n4GsAqwFbB5RHwkM3/cY+0dxwALDNVglPsCcCdwfJf6acOsSZIkSZIkSRo3MvOizt8R0VefiPgo8DHgbcC8lDsongwcmplP9NF/MeDDwN9pXI+eFQY4S5IkSZIkSdJzvgrsTwm4XIpyEXc4B1GCmw8BPp+ZzzQ3NjMbj8DxmXlxPw0jYgPgSOCXwOaZ+cgw8+9OCW6+Alg/Mx+t7RYALgYOiIizM/PqxhgvA84GFgQmZeZMAds1yPvZ7MqZ+SAwpcd6v1z//H7zwnhmXkv3AGEi4or653+3Nj1GCcy+NjPvjYgpwIHdxmjMc3i3+oh4H+Wa+TXNfR+BMzPz+OEajebYVN+iBAR/MjO/0ehzGCUjyyHAro36d1CCm28AVs/MxxrbdgSOo9yqshngfDLwr5k5UyB9RGwDnAT8d0T8LDOfpIso2cI3A3ar6+1lRPvSMLVmgpEkSZIkSZI0RiLiOGBHSiKBMyhJGtYAvgi8NyLWz8ynhhlme2AeyrXNB8diXXOMxSCSJEmSJEmSNJYiYvWIOC0i7omIJyLi3og4PyK27NJ2YkScGhHTI+LxiLg6Ij7Qpd0OEZG13DAiLo6IhyIiO20y84rMvCEzn27377HOJYB9gSsz84B2cHMd8x/P7zmmDqVkxdi6HdzcY/4P1/KQTnBzbTcDOJiSyXi3Vp9dgTcBn2kHN9e+T2VmtuvbarD1DvVpO1i5V5+VKRfT76Fkp27O+2RmnpuZ9/Yz1jA+VstjxmCsERvq2NSMxxtQMm4f1ep6IPAosG1EzN+of30tL2wGN1dn1XLxZmVmfqMd3FzrT6ZkJ38FJQN0t/UvTQm0P5YhMmCPcl8kSZIkSZIkvQAiYgdKcPNPgGUz898yc5/MnERJ7jGZkjRjOLvUcsyur5rBWZIkSZIkSdJAiYhdgG9TMgKfTQmsfCWwGiXw9oeN5q8DrgJuB04EFgU+ApwVEes1b8XXsDmwISUI8+g6xmhtDswNnBoR8wHvB94IPAJclpnX9djHBMjMXvcHXCsiVgPmpASCXpiZ07uMsxLwFuBM4IGIeA/wdiAp2ZAv6hJ0vUQtb+8yb6fuva36reuYp0bERGAjYGHgLuC8zLy/x360bVrn/3Vm3tRnn07g8bH9Bp6PVES8CtgEmAGcMsphVomIvSi3b7yHcuynjaD/UMfmPbU8v/16ZuYjEfEbStDwGsCFddMNtVw3IubLzL83unV+AHDBCNbXCZR/XqaWKPe5PB54CNib8jnsZTT70rFwROxEOU4PAb/rFnDfWNdwnzNJkiRJkiRpvNuTcs1vp9Y1RCgZnPcAtgGO6DVARKwDvBm4PjMvH6uFGeAsSZIkSZIkaWBExArAt4CHgbUz84bW9iVbXSYDUzLzoEabU4DzgP8HdAtw3hjYODPPG4Mlv6OWLwNuApZurfcMYLsuGXSH88XW8yci4lDg861MyZ35/wJcDLy71e9/I2KzVlbe6ZRszMsAN7bad7L+Lt0Jiq2Zhd8K/JWSheNLzHxt+dGI+GRmHtfHfo0oS3INGv9VHC+gAAALZUlEQVRXSrD7d/vpM0o7AROA47tlwe7Tnq3nT0fEd4G9MvPxPvoPdWzeXMtbevT9EyUoeFlqUHBmXh8R/wV8CrgpIn5GCbxfkRLgfypwQB/rIiLWAFagBG5f36XJXpTP4gaZ+XBEDBXgPOJ9aXgrJUN0c23XAdtm5v8OtQ+SJEmSJEmSZhYRL6Ncc5sO7FXyGDzPE8DywwzVubbZ1137+jXHWA4mSZIkSZIkSbPo3ynBs19sBzcDdMmIeydwcKvNLyiZhVfvMcdZYxTcDCWzNJSA5KnAqsAClOyzVwP/QgnYblue7heFr6ME274emI+SXXoX4EFKMOohPeb/N2AiJYP0QpTg0JOAlYGfR8TcjT4/r+X+NYAYgIiYH/hso93CtVyU8pq8Avhy3delgMWAnSmZnb8bEet22Z9n1czP6wP3A2cM1bZhy7qO8zLz7j77jEjNPrxzfTqaC/B3AJ+gBO7OD7yGsu6pwMeBYQO/+zg2C9XyoR5DdOoXblZm5t7ArsDilOzn+1GyN18HnJCZj/axtkWB79enn2pn0a4/SvgScHRm9pMRelT7AhwGTKLsy4KU4P4fUb6A+VVEvLbLWL0+Z5IkSZIkSZJgESAo19wO7PF4DeWad1f1+uG/AH+n3GVxzJjBWZIkSZIkSdIgWaOW5/bZ/tp2wGV1N7Bmjz5XjXhVvXWSSDwAbJKZD9fnv42ITSlZareNiP0z855Op8y8qdtgmfmTVtVdlODh3wNXAvtGxGGZOb01/5zAVpl5RX3+cERsBywHrEa5wPyDuu0IYAvgXcANEXEO5SL2+ynByg9RglCf6TLHMZn5hcb6jq1ZPo6kBM/+qtt+VbvUeU7IzCeGaNc0oozPo7QeJaD895l5dXtjROxACR5vujgzLwbIzEuASxrbHgNOj4grKYHEH42Ir2bmdUOsYTTHZkg1cPsISmDzAZSA9weBVYD/As6NiD0y86ghxpgfOIuS8ftrmXl6a/sEypcW9wL/MRbr7iUz92lVXQ1sERE/ory/96Vkq2726fo5kyRJkiRJkgQ8l2zgmsxcdZRjbA/MQ7m2+eDYLKswg7MkSZIkSZKkQdLJ2nrPkK2e0+uC6VP0vv5534hW1N/8FzaCmwHIzHuB39Z1rDYrk2Tm7ymB2ROYOXC7M/99jeDmTp+kBKdCI5t1Zs4A1qJkY36KElz7EeDXtX7OWv9A7dLMtNsOwG7W9cqYTUTMBexYn/aVJTkiVqQEYU8DzumnzygNd/vEHXh+1pLJww1aM0531v3uXu36PDad12ChHts79c3Pw/aUzNJHZuZXMnNaZs7IzMuATSgZVb4SEV2zr9Tg5p9T3hOHZeZ+XZp9BngbsGN9X/VjNPsylKNr2fMYS5IkSZIkSXq+ek3vBmDFmol5NHap5WjujjckA5wlSZIkSZIkDZJOUONrX8A5cgzHurmWvYIx/1bL+cZgrr/Wcv5Znb8Gun42M5fNzHkyc7HM3I6SaWMB4LrM/Edt+xglI3avefrZx02AVwOXZObNQ7Rr6gQeH9sjS/csi4hXAh8EZgCndGuTmZMzM1qPKX1O0e01a+vn2HTql+2x/U21vKVR94FaXtRunJn3ATdRXus3t7dHxIKULOrrUDI3t7Mnd6xKyTx9cURk5wHcUbdPqnXN981o9mUo/RxjSZIkSZIkSd0dBswNHBcRC7c3RsQiEdE1u3NErA0sD1yfmZeP9cLmGusBJUmSJEmSJGkWXEnJdrwRJQBz0F0AfA5Yqcf2FWt5R4/tfYmICZRgUoDbG5uuBB4FJkbE/Jn5aKtrZ139zr9dLdvBvhdQsgyvRMlKPdI5hsuSPJOImBfYFngaOLafPqO0IyUr9vGZ+cgLMP47a3n7EG36OTadIOUNImKOzHyms6EGI08CHqO8HzrmqeXiPcbs1D/ZrIyIhYDzgDWAQzLzgCHW9Utgepf6BShZwf8M/KyubVb2ZShr1HKoYyxJkiRJkiSNGxHxIeBD9ekStVwzIo6vf0/PzH0BMvO4iHg7sBtwW0T8ArgLWBRYhnLntO8Bu3aZakTXfUfKDM6SJEmSJEmSBsm3gaeAz0XECu2NEbHkP39JQ7oUuBZYKyI+3NwQEbtQslfcClzd2rZcRCzXqlswIrpl050bOBxYmhL0/exYNbvyscC8wMEREY1+KwM7UI7nj1pjvrzLPOsD+wG3Ace0Nh8FPAN8OiIWb/SZFzikPv1Be8za5nXABsD9wBnd2nSxBbAIcG5m3j1c49Gox2rn+rS9vyMZZ7UudXNExGeANSkBwOf16NvXscnM24DzgYnA7q3NB1GyF5/YCnC/tJZ716Dl5ry7AksC9wF/bNQvQglmXwM4cJjgZjLzqMzcuf0APl2b3FrrPjkr+xIRb6lB/jOJiLfw3PvvpC7bn/c5q/WL1W2LDbV/kiRJkiRJ0mxqFWD7+nhfrXt9o27zZuPM3J1yp7krgPWAvYFNgYWAQynXp2dSryVuDvwdOPGF2AkzOEuSJEmSJEkaGJn5x4jYDTgauCYizgL+BLwCeAfwMPCeF2r+iFiL54JeF6jlmxqZLcjMHRp/Z0RsD1wCnBERPwVuoWRu3oiSXXn7zHy6NdWNnSkbda8AboyIq+v2eylZdt9DyZQxHfhoM+Nt9TlKFo29KFk4fgO8CtiMEvi8Vw0qbbopIv5ACZh+nJIdej1KwOsH25mgM/N3EXEQJQD1+og4u/Z7H/Am4HLga3S3MyXZxgmZ+USPNm19Z/6IiE8DnSDWVWq5Y30tAS7LzO926bou8Ebg95n5uz7X1c3/RMT1wHXAPZSL/pMoma0fA7bJzId79B3JsdmNcpyPjIj3Ut4j76S8P24B9m+1/xawDfAW4Jb6mj1Iea3XpWTH3r313vwxJYP6bcAcETGlyzrOzMxrh1nrcEa6L3sDm0TEpcDdwBOU13xDYE7gO3QPsO/2OQPYAziQ8n6eMov7IkmSJEmSJA2UzJzCCK97ZebPKHdj67f934D5RrSwETLAWZIkSZIkSdJAyczv1IDRfYHJlFvpTQf+AHQLVB1Lb6RksGh6Zatuh+bGzPxDRKxKCZjcANiYst6TgS9m5s19zv0A8E1gdUrg8KLAk5Rg068Ch2XmX9qdMvPhiFgb+Awl8/EelKwZlwH/mZnnd5nrZEpw6LuACcCdlADlr2XmA90Wl5lfqK/LXsBHgLnr2g6o8zwvQDci5gR2qk/7uk1hRCwPrAVMA87po8uGwDqtunfVR0e3981Y3T7xPymv2bqU1+wZyi0cj6K8Zrd36zTSY5OZt9Vs0V+g7PPGlCD4I4CD6hcKzfYzImISJTh4M2Brymv2V+B0ymt2VWuaZWr5Bsr7uZuplKzlozbSfQHOBF5OCdZelxK4fz9wLvCdzDx7VtYjSZIkSZIkafBEZr7Ya5AkSZIkSZIkSZIkSZIkSZIkoNz6TpIkSZIkSZIkSZIkSZIkSZIGggHOkiRJkiRJkiRJkiRJkiRJkgaGAc6SJEmSJEmSJEmSJEmSJEmSBoYBzpIkSZIkSZIkSZIkSZIkSZIGhgHOkiRJkiRJkiRJkiRJkiRJkgaGAc6SJEmSJEmSJEmSJEmSJEmSBoYBzpIkSZIkSZIkSZIkSZIkSZIGhgHOkiRJkiRJkiRJkiRJkiRJkgaGAc6SJEmSJEmSJEmSJEmSJEmSBoYBzpIkSZIkSZIkSZIkSZIkSZIGhgHOkiRJkiRJkiRJkiRJkiRJkgaGAc6SJEmSJEmSJEmSJEmSJEmSBoYBzpIkSZIkSZIkSZIkSZIkSZIGhgHOkiRJkiRJkiRJkiRJkiRJkgaGAc6SJEmSJEmSJEmSJEmSJEmSBoYBzpIkSZIkSZIkSZIkSZIkSZIGhgHOkiRJkiRJkiRJkiRJkiRJkgbG/wfkwU4lQDogrAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 351,
       "width": 1436
      },
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# @title Visualize some tracks\n",
    "variant_track = np.zeros_like(reference_prediction[:, 0], dtype=bool)\n",
    "variant_track[variant_track.shape[0] // 2] = True\n",
    "tracks = {'variant': variant_track,\n",
    "          'CAGE/neutrofils ref': reference_prediction[:, 4767],\n",
    "          'CAGE/neutrofils alt-ref': alternate_prediction[:, 4767] - reference_prediction[:, 4767],\n",
    "          'CHIP:H3K27ac:neutrophil ref': reference_prediction[:, 2280],\n",
    "          'CHIP:H3K27ac:neutrophil alt-ref': alternate_prediction[:, 2280] - reference_prediction[:, 2280],\n",
    "          }\n",
    "\n",
    "plot_tracks(tracks, interval.resize(reference_prediction.shape[0] * 128), height=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZMCU9woQv6Ea"
   },
   "source": [
    "## Score variants in a VCF file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c1nJwFS-4P8g"
   },
   "source": [
    "### Report top 20 PCs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-vmj1MA3chRQ",
    "outputId": "0e004c93-a817-4e87-842d-1ea04a26b649"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator RobustScaler from version 0.23.2 when using version 0.22.2.post1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  UserWarning)\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator TruncatedSVD from version 0.23.2 when using version 0.22.2.post1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  UserWarning)\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator RobustScaler from version 0.23.2 when using version 0.22.2.post1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  UserWarning)\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator Pipeline from version 0.23.2 when using version 0.22.2.post1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  UserWarning)\n"
     ]
    }
   ],
   "source": [
    "enformer_score_variants = EnformerScoreVariantsPCANormalized(model_path, transform_path, num_top_features=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 275
    },
    "id": "1oGEYix3dRex",
    "outputId": "c7e89a9e-faa6-4f8d-f018-db6bfa303283"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done 0\n",
      "Done 2\n",
      "Done 4\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chrom</th>\n",
       "      <th>pos</th>\n",
       "      <th>id</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>PC0</th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "      <th>PC5</th>\n",
       "      <th>PC6</th>\n",
       "      <th>PC7</th>\n",
       "      <th>PC8</th>\n",
       "      <th>PC9</th>\n",
       "      <th>PC10</th>\n",
       "      <th>PC11</th>\n",
       "      <th>PC12</th>\n",
       "      <th>PC13</th>\n",
       "      <th>PC14</th>\n",
       "      <th>PC15</th>\n",
       "      <th>PC16</th>\n",
       "      <th>PC17</th>\n",
       "      <th>PC18</th>\n",
       "      <th>PC19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr1</td>\n",
       "      <td>925952</td>\n",
       "      <td>1019397</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>13.865371</td>\n",
       "      <td>9.379375</td>\n",
       "      <td>1.322473</td>\n",
       "      <td>7.198019</td>\n",
       "      <td>11.926774</td>\n",
       "      <td>-4.407538</td>\n",
       "      <td>-5.878580</td>\n",
       "      <td>-10.701156</td>\n",
       "      <td>-3.140507</td>\n",
       "      <td>2.994015</td>\n",
       "      <td>4.716916</td>\n",
       "      <td>1.098637</td>\n",
       "      <td>2.569388</td>\n",
       "      <td>13.693736</td>\n",
       "      <td>8.564518</td>\n",
       "      <td>9.383035</td>\n",
       "      <td>-2.159512</td>\n",
       "      <td>-9.733231</td>\n",
       "      <td>7.727090</td>\n",
       "      <td>-0.669298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr1</td>\n",
       "      <td>930188</td>\n",
       "      <td>846933</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>-61.468933</td>\n",
       "      <td>-5.653942</td>\n",
       "      <td>-2.758731</td>\n",
       "      <td>6.289482</td>\n",
       "      <td>1.844845</td>\n",
       "      <td>3.446712</td>\n",
       "      <td>6.052454</td>\n",
       "      <td>0.632046</td>\n",
       "      <td>2.584915</td>\n",
       "      <td>1.117951</td>\n",
       "      <td>1.942497</td>\n",
       "      <td>-6.513691</td>\n",
       "      <td>-4.948788</td>\n",
       "      <td>-1.172066</td>\n",
       "      <td>-2.903700</td>\n",
       "      <td>0.482835</td>\n",
       "      <td>2.896945</td>\n",
       "      <td>1.757883</td>\n",
       "      <td>3.686084</td>\n",
       "      <td>-6.673547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr1</td>\n",
       "      <td>930200</td>\n",
       "      <td>1043045</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>-61.995975</td>\n",
       "      <td>-10.007704</td>\n",
       "      <td>-0.312641</td>\n",
       "      <td>10.605079</td>\n",
       "      <td>-5.349404</td>\n",
       "      <td>-2.555728</td>\n",
       "      <td>7.008485</td>\n",
       "      <td>9.793589</td>\n",
       "      <td>-14.216670</td>\n",
       "      <td>4.411201</td>\n",
       "      <td>0.295830</td>\n",
       "      <td>-4.968991</td>\n",
       "      <td>-10.770261</td>\n",
       "      <td>-1.512434</td>\n",
       "      <td>0.186349</td>\n",
       "      <td>7.461446</td>\n",
       "      <td>5.153117</td>\n",
       "      <td>4.041459</td>\n",
       "      <td>0.512155</td>\n",
       "      <td>-2.865725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr1</td>\n",
       "      <td>930203</td>\n",
       "      <td>972363</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>21.486368</td>\n",
       "      <td>-9.248886</td>\n",
       "      <td>3.684767</td>\n",
       "      <td>9.606424</td>\n",
       "      <td>0.538447</td>\n",
       "      <td>5.264496</td>\n",
       "      <td>1.418155</td>\n",
       "      <td>-14.005326</td>\n",
       "      <td>12.898662</td>\n",
       "      <td>9.391730</td>\n",
       "      <td>-5.201692</td>\n",
       "      <td>-3.091272</td>\n",
       "      <td>1.975370</td>\n",
       "      <td>-5.240757</td>\n",
       "      <td>-14.367105</td>\n",
       "      <td>-7.802942</td>\n",
       "      <td>0.138479</td>\n",
       "      <td>12.087408</td>\n",
       "      <td>-5.559704</td>\n",
       "      <td>9.171222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr1</td>\n",
       "      <td>930222</td>\n",
       "      <td>998906</td>\n",
       "      <td>GAACTC</td>\n",
       "      <td>TTCTTCTG</td>\n",
       "      <td>13.672197</td>\n",
       "      <td>181.645172</td>\n",
       "      <td>-302.586548</td>\n",
       "      <td>184.414001</td>\n",
       "      <td>146.373199</td>\n",
       "      <td>284.204163</td>\n",
       "      <td>-108.902489</td>\n",
       "      <td>100.794731</td>\n",
       "      <td>-205.568008</td>\n",
       "      <td>302.769409</td>\n",
       "      <td>203.416458</td>\n",
       "      <td>111.947685</td>\n",
       "      <td>-61.380695</td>\n",
       "      <td>222.271515</td>\n",
       "      <td>152.539993</td>\n",
       "      <td>114.129166</td>\n",
       "      <td>-26.604349</td>\n",
       "      <td>-68.656372</td>\n",
       "      <td>-36.595196</td>\n",
       "      <td>38.175354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  chrom     pos       id     ref  ...       PC16       PC17       PC18       PC19\n",
       "0  chr1  925952  1019397       G  ...  -2.159512  -9.733231   7.727090  -0.669298\n",
       "1  chr1  930188   846933       G  ...   2.896945   1.757883   3.686084  -6.673547\n",
       "2  chr1  930200  1043045       G  ...   5.153117   4.041459   0.512155  -2.865725\n",
       "3  chr1  930203   972363       C  ...   0.138479  12.087408  -5.559704   9.171222\n",
       "4  chr1  930222   998906  GAACTC  ... -26.604349 -68.656372 -36.595196  38.175354\n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Score the first 5 variants from ClinVar\n",
    "# Lower-dimensional scores (20 PCs)\n",
    "it = variant_centered_sequences(clinvar_vcf, sequence_length=SEQUENCE_LENGTH,\n",
    "                                gzipped=True, chr_prefix='chr')\n",
    "example_list = []\n",
    "for i, example in enumerate(it):\n",
    "  if i >= 5:\n",
    "    break\n",
    "  variant_scores = enformer_score_variants.predict_on_batch(\n",
    "      {k: v[tf.newaxis] for k,v in example['inputs'].items()})[0]\n",
    "  variant_scores = {f'PC{i}': score for i, score in enumerate(variant_scores)}\n",
    "  example_list.append({**example['metadata'],\n",
    "                       **variant_scores})\n",
    "  if i % 2 == 0:\n",
    "    print(f'Done {i}')\n",
    "df = pd.DataFrame(example_list)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_G5cANX34SLw"
   },
   "source": [
    "### Report all 5,313 features (z-score normalized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "get8hogCySnt",
    "outputId": "efe3d5fd-faf5-4589-de79-b022a5c28ab2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator RobustScaler from version 0.23.2 when using version 0.22.2.post1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  UserWarning)\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator TruncatedSVD from version 0.23.2 when using version 0.22.2.post1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  UserWarning)\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator Pipeline from version 0.23.2 when using version 0.22.2.post1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  UserWarning)\n"
     ]
    }
   ],
   "source": [
    "enformer_score_variants_all = EnformerScoreVariantsNormalized(model_path, transform_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 339
    },
    "id": "Q48earqRyFa6",
    "outputId": "fb26f901-7d3e-4305-9fb8-141046e56210"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done 0\n",
      "Done 2\n",
      "Done 4\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chrom</th>\n",
       "      <th>pos</th>\n",
       "      <th>id</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>DNASE:cerebellum mal</th>\n",
       "      <th>DNASE:frontal cortex</th>\n",
       "      <th>DNASE:chorion</th>\n",
       "      <th>DNASE:Ishikawa treat</th>\n",
       "      <th>DNASE:GM03348</th>\n",
       "      <th>DNASE:GM03348 geneti</th>\n",
       "      <th>DNASE:AG08395</th>\n",
       "      <th>DNASE:AG08396</th>\n",
       "      <th>DNASE:AG20443</th>\n",
       "      <th>DNASE:H54</th>\n",
       "      <th>DNASE:GM10248</th>\n",
       "      <th>DNASE:GM12878</th>\n",
       "      <th>DNASE:GM12891</th>\n",
       "      <th>DNASE:GM12892</th>\n",
       "      <th>DNASE:GM18507</th>\n",
       "      <th>DNASE:GM19238</th>\n",
       "      <th>DNASE:GM19239</th>\n",
       "      <th>DNASE:GM19240</th>\n",
       "      <th>DNASE:H1-hESC</th>\n",
       "      <th>DNASE:H7-hESC</th>\n",
       "      <th>DNASE:H9</th>\n",
       "      <th>DNASE:heart male adu</th>\n",
       "      <th>DNASE:HEK293T</th>\n",
       "      <th>DNASE:HeLa-S3 treate</th>\n",
       "      <th>DNASE:HeLa-S3</th>\n",
       "      <th>DNASE:hepatocyte</th>\n",
       "      <th>DNASE:HepG2</th>\n",
       "      <th>DNASE:HTR-8/SVneo</th>\n",
       "      <th>DNASE:endothelial ce</th>\n",
       "      <th>DNASE:CWRU1 male</th>\n",
       "      <th>DNASE:iPS-NIHi11 mal</th>\n",
       "      <th>DNASE:iPS-NIHi7 fema</th>\n",
       "      <th>DNASE:K562 treated w</th>\n",
       "      <th>DNASE:K562 G2 phase</th>\n",
       "      <th>DNASE:K562 G1 phase</th>\n",
       "      <th>...</th>\n",
       "      <th>CAGE:CD14+CD16- Mono</th>\n",
       "      <th>CAGE:achilles tendon</th>\n",
       "      <th>CAGE:cerebrospinal f</th>\n",
       "      <th>CAGE:cruciate ligame</th>\n",
       "      <th>CAGE:eye - vitreous</th>\n",
       "      <th>CAGE:eye - muscle su</th>\n",
       "      <th>CAGE:eye - muscle la</th>\n",
       "      <th>CAGE:eye - muscle me</th>\n",
       "      <th>CAGE:eye - muscle in</th>\n",
       "      <th>CAGE:Fingernail (inc</th>\n",
       "      <th>CAGE:optic nerve,</th>\n",
       "      <th>CAGE:Skin - palm,</th>\n",
       "      <th>CAGE:tongue epidermi</th>\n",
       "      <th>CAGE:Urethra,</th>\n",
       "      <th>CAGE:CD14+ monocytes</th>\n",
       "      <th>CAGE:Hep-2 cells tre</th>\n",
       "      <th>CAGE:Hep-2 cells moc</th>\n",
       "      <th>CAGE:immature langer</th>\n",
       "      <th>CAGE:migratory lange</th>\n",
       "      <th>CAGE:CD34 cells diff</th>\n",
       "      <th>CAGE:amygdala - adul</th>\n",
       "      <th>CAGE:thalamus - adul</th>\n",
       "      <th>CAGE:hippocampus - a</th>\n",
       "      <th>CAGE:parietal lobe -</th>\n",
       "      <th>CAGE:cerebellum - ad</th>\n",
       "      <th>CAGE:pineal gland -</th>\n",
       "      <th>CAGE:spinal cord - a</th>\n",
       "      <th>CAGE:Olfactory epith</th>\n",
       "      <th>CAGE:gamma delta pos</th>\n",
       "      <th>CAGE:Mast cell, expa</th>\n",
       "      <th>CAGE:adipose,</th>\n",
       "      <th>CAGE:cerebellum, new</th>\n",
       "      <th>CAGE:spinal cord, ne</th>\n",
       "      <th>CAGE:amygdala, newbo</th>\n",
       "      <th>CAGE:hippocampus, ne</th>\n",
       "      <th>CAGE:putamen, newbor</th>\n",
       "      <th>CAGE:thalamus, newbo</th>\n",
       "      <th>CAGE:thymic carcinom</th>\n",
       "      <th>CAGE:Smooth muscle c</th>\n",
       "      <th>CAGE:parietal cortex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr1</td>\n",
       "      <td>925952</td>\n",
       "      <td>1019397</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>-3.953313</td>\n",
       "      <td>-2.132398</td>\n",
       "      <td>-1.133056</td>\n",
       "      <td>-0.018265</td>\n",
       "      <td>-7.324341</td>\n",
       "      <td>-5.928950</td>\n",
       "      <td>-2.696301</td>\n",
       "      <td>-2.234811</td>\n",
       "      <td>-1.869620</td>\n",
       "      <td>16.743450</td>\n",
       "      <td>2.218565</td>\n",
       "      <td>3.167206</td>\n",
       "      <td>1.084149</td>\n",
       "      <td>0.671318</td>\n",
       "      <td>6.513158</td>\n",
       "      <td>0.971147</td>\n",
       "      <td>6.255165</td>\n",
       "      <td>5.352125</td>\n",
       "      <td>1.729454</td>\n",
       "      <td>-3.350782</td>\n",
       "      <td>1.896389</td>\n",
       "      <td>-1.563089</td>\n",
       "      <td>-2.268677</td>\n",
       "      <td>11.647644</td>\n",
       "      <td>14.272476</td>\n",
       "      <td>-7.074303</td>\n",
       "      <td>1.850793</td>\n",
       "      <td>5.803924</td>\n",
       "      <td>0.381484</td>\n",
       "      <td>-0.443644</td>\n",
       "      <td>-0.299062</td>\n",
       "      <td>-0.139204</td>\n",
       "      <td>-8.861604</td>\n",
       "      <td>-8.072408</td>\n",
       "      <td>-6.212577</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.046439</td>\n",
       "      <td>4.083508</td>\n",
       "      <td>3.535412</td>\n",
       "      <td>11.717014</td>\n",
       "      <td>-5.239547</td>\n",
       "      <td>1.081644</td>\n",
       "      <td>2.213174</td>\n",
       "      <td>0.853902</td>\n",
       "      <td>-0.216257</td>\n",
       "      <td>2.654844</td>\n",
       "      <td>0.402727</td>\n",
       "      <td>1.567667</td>\n",
       "      <td>3.049335</td>\n",
       "      <td>1.010953</td>\n",
       "      <td>-0.800231</td>\n",
       "      <td>157.583069</td>\n",
       "      <td>172.648773</td>\n",
       "      <td>0.576450</td>\n",
       "      <td>5.026437</td>\n",
       "      <td>0.464598</td>\n",
       "      <td>2.133233</td>\n",
       "      <td>1.689052</td>\n",
       "      <td>2.324542</td>\n",
       "      <td>1.849092</td>\n",
       "      <td>-1.319847</td>\n",
       "      <td>-25.130344</td>\n",
       "      <td>2.456997</td>\n",
       "      <td>11.093707</td>\n",
       "      <td>-4.529293</td>\n",
       "      <td>5.072914</td>\n",
       "      <td>-6.317924</td>\n",
       "      <td>-2.482907</td>\n",
       "      <td>1.591787</td>\n",
       "      <td>-5.362659</td>\n",
       "      <td>0.643638</td>\n",
       "      <td>-1.936050</td>\n",
       "      <td>2.499822</td>\n",
       "      <td>12.372701</td>\n",
       "      <td>23.365688</td>\n",
       "      <td>2.915666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr1</td>\n",
       "      <td>930188</td>\n",
       "      <td>846933</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>-1.791559</td>\n",
       "      <td>-15.679683</td>\n",
       "      <td>-22.533566</td>\n",
       "      <td>0.523771</td>\n",
       "      <td>-3.826311</td>\n",
       "      <td>-3.219894</td>\n",
       "      <td>-2.668196</td>\n",
       "      <td>-9.468966</td>\n",
       "      <td>-3.158970</td>\n",
       "      <td>-7.704255</td>\n",
       "      <td>-3.150757</td>\n",
       "      <td>-0.523286</td>\n",
       "      <td>-11.126574</td>\n",
       "      <td>-11.116987</td>\n",
       "      <td>-3.674609</td>\n",
       "      <td>-8.464477</td>\n",
       "      <td>-11.757355</td>\n",
       "      <td>-8.595486</td>\n",
       "      <td>-0.536293</td>\n",
       "      <td>0.227047</td>\n",
       "      <td>-0.779173</td>\n",
       "      <td>-15.736004</td>\n",
       "      <td>0.879350</td>\n",
       "      <td>-6.572251</td>\n",
       "      <td>-4.200795</td>\n",
       "      <td>-8.630462</td>\n",
       "      <td>-0.465244</td>\n",
       "      <td>-3.442806</td>\n",
       "      <td>-0.311721</td>\n",
       "      <td>-4.801476</td>\n",
       "      <td>-5.145035</td>\n",
       "      <td>-1.415882</td>\n",
       "      <td>0.362791</td>\n",
       "      <td>-1.997213</td>\n",
       "      <td>-2.307346</td>\n",
       "      <td>...</td>\n",
       "      <td>-33.052082</td>\n",
       "      <td>-2.994223</td>\n",
       "      <td>-8.416894</td>\n",
       "      <td>-5.190420</td>\n",
       "      <td>-4.534584</td>\n",
       "      <td>-5.453042</td>\n",
       "      <td>-3.034095</td>\n",
       "      <td>-7.163062</td>\n",
       "      <td>-2.291493</td>\n",
       "      <td>-8.985706</td>\n",
       "      <td>-4.789318</td>\n",
       "      <td>-4.794244</td>\n",
       "      <td>-1.449592</td>\n",
       "      <td>-1.989321</td>\n",
       "      <td>-22.916094</td>\n",
       "      <td>-35.752350</td>\n",
       "      <td>-35.275730</td>\n",
       "      <td>-13.375345</td>\n",
       "      <td>-28.358622</td>\n",
       "      <td>-31.596754</td>\n",
       "      <td>-11.371334</td>\n",
       "      <td>-9.254264</td>\n",
       "      <td>-9.006978</td>\n",
       "      <td>-9.230368</td>\n",
       "      <td>-3.783889</td>\n",
       "      <td>-0.283448</td>\n",
       "      <td>-12.094806</td>\n",
       "      <td>-59.170837</td>\n",
       "      <td>-66.303123</td>\n",
       "      <td>-34.475914</td>\n",
       "      <td>-21.272675</td>\n",
       "      <td>-4.328580</td>\n",
       "      <td>-16.075752</td>\n",
       "      <td>-6.281566</td>\n",
       "      <td>-10.389462</td>\n",
       "      <td>-6.422119</td>\n",
       "      <td>-9.181828</td>\n",
       "      <td>-14.224935</td>\n",
       "      <td>-46.487968</td>\n",
       "      <td>-11.499000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr1</td>\n",
       "      <td>930200</td>\n",
       "      <td>1043045</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>-0.526763</td>\n",
       "      <td>-14.688834</td>\n",
       "      <td>-17.484657</td>\n",
       "      <td>2.908407</td>\n",
       "      <td>2.734465</td>\n",
       "      <td>3.514852</td>\n",
       "      <td>0.294236</td>\n",
       "      <td>-4.154023</td>\n",
       "      <td>1.062205</td>\n",
       "      <td>-7.452735</td>\n",
       "      <td>-2.387097</td>\n",
       "      <td>-0.988034</td>\n",
       "      <td>-7.148329</td>\n",
       "      <td>-7.479169</td>\n",
       "      <td>-3.070057</td>\n",
       "      <td>-9.537191</td>\n",
       "      <td>-12.243765</td>\n",
       "      <td>-10.340226</td>\n",
       "      <td>0.070186</td>\n",
       "      <td>0.338647</td>\n",
       "      <td>-0.706960</td>\n",
       "      <td>-13.103579</td>\n",
       "      <td>3.004640</td>\n",
       "      <td>-8.224607</td>\n",
       "      <td>-7.412826</td>\n",
       "      <td>-9.011602</td>\n",
       "      <td>0.814177</td>\n",
       "      <td>-2.572933</td>\n",
       "      <td>0.154302</td>\n",
       "      <td>-4.661432</td>\n",
       "      <td>-3.152854</td>\n",
       "      <td>-1.105172</td>\n",
       "      <td>-2.336678</td>\n",
       "      <td>-4.403462</td>\n",
       "      <td>-4.830977</td>\n",
       "      <td>...</td>\n",
       "      <td>-26.200890</td>\n",
       "      <td>-4.082983</td>\n",
       "      <td>-11.688838</td>\n",
       "      <td>-5.358507</td>\n",
       "      <td>-5.728800</td>\n",
       "      <td>-6.592316</td>\n",
       "      <td>-5.169149</td>\n",
       "      <td>-8.596480</td>\n",
       "      <td>-3.361437</td>\n",
       "      <td>-6.800953</td>\n",
       "      <td>-4.874091</td>\n",
       "      <td>-4.676669</td>\n",
       "      <td>-2.054912</td>\n",
       "      <td>-2.659639</td>\n",
       "      <td>-19.059345</td>\n",
       "      <td>-86.237938</td>\n",
       "      <td>-90.280151</td>\n",
       "      <td>-13.633442</td>\n",
       "      <td>-32.443680</td>\n",
       "      <td>-19.412699</td>\n",
       "      <td>-7.266642</td>\n",
       "      <td>-7.957299</td>\n",
       "      <td>-6.725488</td>\n",
       "      <td>-6.803071</td>\n",
       "      <td>-6.814243</td>\n",
       "      <td>1.440345</td>\n",
       "      <td>-8.739142</td>\n",
       "      <td>-70.331505</td>\n",
       "      <td>-39.735146</td>\n",
       "      <td>-31.107134</td>\n",
       "      <td>-18.125492</td>\n",
       "      <td>-3.016214</td>\n",
       "      <td>-8.161813</td>\n",
       "      <td>-12.665734</td>\n",
       "      <td>-9.362435</td>\n",
       "      <td>-5.167178</td>\n",
       "      <td>-3.976753</td>\n",
       "      <td>-12.334543</td>\n",
       "      <td>-38.066742</td>\n",
       "      <td>-8.078856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr1</td>\n",
       "      <td>930203</td>\n",
       "      <td>972363</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>3.887291</td>\n",
       "      <td>-0.802319</td>\n",
       "      <td>-1.669034</td>\n",
       "      <td>4.747784</td>\n",
       "      <td>1.695362</td>\n",
       "      <td>2.217486</td>\n",
       "      <td>-0.282765</td>\n",
       "      <td>-1.139245</td>\n",
       "      <td>0.368520</td>\n",
       "      <td>-3.772724</td>\n",
       "      <td>3.466754</td>\n",
       "      <td>1.096701</td>\n",
       "      <td>2.536208</td>\n",
       "      <td>5.108255</td>\n",
       "      <td>1.874467</td>\n",
       "      <td>3.965143</td>\n",
       "      <td>1.232345</td>\n",
       "      <td>3.818713</td>\n",
       "      <td>0.651470</td>\n",
       "      <td>2.554193</td>\n",
       "      <td>0.991104</td>\n",
       "      <td>1.639103</td>\n",
       "      <td>5.514153</td>\n",
       "      <td>-6.208551</td>\n",
       "      <td>-13.487192</td>\n",
       "      <td>6.362380</td>\n",
       "      <td>0.164872</td>\n",
       "      <td>-4.553425</td>\n",
       "      <td>-0.160736</td>\n",
       "      <td>-1.997855</td>\n",
       "      <td>-2.654027</td>\n",
       "      <td>0.416074</td>\n",
       "      <td>7.220230</td>\n",
       "      <td>4.069194</td>\n",
       "      <td>2.957357</td>\n",
       "      <td>...</td>\n",
       "      <td>33.843147</td>\n",
       "      <td>-0.583508</td>\n",
       "      <td>0.264617</td>\n",
       "      <td>-3.204525</td>\n",
       "      <td>5.382181</td>\n",
       "      <td>1.291355</td>\n",
       "      <td>0.997021</td>\n",
       "      <td>2.093935</td>\n",
       "      <td>-0.294896</td>\n",
       "      <td>4.008999</td>\n",
       "      <td>-0.024318</td>\n",
       "      <td>-0.550827</td>\n",
       "      <td>-1.001716</td>\n",
       "      <td>0.569551</td>\n",
       "      <td>27.447952</td>\n",
       "      <td>-22.858391</td>\n",
       "      <td>-32.015335</td>\n",
       "      <td>12.032137</td>\n",
       "      <td>15.755173</td>\n",
       "      <td>19.405922</td>\n",
       "      <td>2.799939</td>\n",
       "      <td>3.406132</td>\n",
       "      <td>1.644471</td>\n",
       "      <td>2.383641</td>\n",
       "      <td>-1.271341</td>\n",
       "      <td>35.166897</td>\n",
       "      <td>-0.529095</td>\n",
       "      <td>-15.446460</td>\n",
       "      <td>42.712193</td>\n",
       "      <td>22.990261</td>\n",
       "      <td>-2.007191</td>\n",
       "      <td>1.718304</td>\n",
       "      <td>1.981185</td>\n",
       "      <td>5.209938</td>\n",
       "      <td>6.387944</td>\n",
       "      <td>3.160304</td>\n",
       "      <td>0.638121</td>\n",
       "      <td>-0.786704</td>\n",
       "      <td>-4.402274</td>\n",
       "      <td>3.053887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr1</td>\n",
       "      <td>930222</td>\n",
       "      <td>998906</td>\n",
       "      <td>GAACTC</td>\n",
       "      <td>TTCTTCTG</td>\n",
       "      <td>-6.190521</td>\n",
       "      <td>-137.378281</td>\n",
       "      <td>34.811737</td>\n",
       "      <td>-1.299490</td>\n",
       "      <td>-44.415581</td>\n",
       "      <td>-15.935372</td>\n",
       "      <td>-6.835675</td>\n",
       "      <td>-44.718884</td>\n",
       "      <td>12.754320</td>\n",
       "      <td>71.546776</td>\n",
       "      <td>-100.758392</td>\n",
       "      <td>-128.016174</td>\n",
       "      <td>-168.724716</td>\n",
       "      <td>-330.777496</td>\n",
       "      <td>-142.454132</td>\n",
       "      <td>-350.230988</td>\n",
       "      <td>-131.790482</td>\n",
       "      <td>-207.531921</td>\n",
       "      <td>-7.376725</td>\n",
       "      <td>-45.294678</td>\n",
       "      <td>-16.531658</td>\n",
       "      <td>-183.512756</td>\n",
       "      <td>10.864501</td>\n",
       "      <td>18.745724</td>\n",
       "      <td>9.173240</td>\n",
       "      <td>15.418909</td>\n",
       "      <td>13.582534</td>\n",
       "      <td>24.021170</td>\n",
       "      <td>-13.055187</td>\n",
       "      <td>41.811131</td>\n",
       "      <td>57.707191</td>\n",
       "      <td>46.170353</td>\n",
       "      <td>-125.677353</td>\n",
       "      <td>-66.172997</td>\n",
       "      <td>-53.709393</td>\n",
       "      <td>...</td>\n",
       "      <td>45.992233</td>\n",
       "      <td>1.093487</td>\n",
       "      <td>-150.802185</td>\n",
       "      <td>16.499266</td>\n",
       "      <td>24.087925</td>\n",
       "      <td>-66.276947</td>\n",
       "      <td>-92.977158</td>\n",
       "      <td>-139.676208</td>\n",
       "      <td>-51.160301</td>\n",
       "      <td>-36.617786</td>\n",
       "      <td>-4.993864</td>\n",
       "      <td>5.801898</td>\n",
       "      <td>-10.315315</td>\n",
       "      <td>-5.569825</td>\n",
       "      <td>353.592102</td>\n",
       "      <td>544.979797</td>\n",
       "      <td>542.144470</td>\n",
       "      <td>44.670349</td>\n",
       "      <td>-91.620117</td>\n",
       "      <td>-66.408241</td>\n",
       "      <td>65.320213</td>\n",
       "      <td>27.775331</td>\n",
       "      <td>36.321232</td>\n",
       "      <td>27.018156</td>\n",
       "      <td>-37.838741</td>\n",
       "      <td>-20.264828</td>\n",
       "      <td>-15.695430</td>\n",
       "      <td>-72.608513</td>\n",
       "      <td>-119.713341</td>\n",
       "      <td>-22.978678</td>\n",
       "      <td>20.147364</td>\n",
       "      <td>3.596015</td>\n",
       "      <td>20.581316</td>\n",
       "      <td>-82.511292</td>\n",
       "      <td>10.583510</td>\n",
       "      <td>99.163254</td>\n",
       "      <td>87.440636</td>\n",
       "      <td>28.396255</td>\n",
       "      <td>26.421061</td>\n",
       "      <td>27.204330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3378 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  chrom     pos  ... CAGE:Smooth muscle c CAGE:parietal cortex\n",
       "0  chr1  925952  ...            23.365688             2.915666\n",
       "1  chr1  930188  ...           -46.487968           -11.499000\n",
       "2  chr1  930200  ...           -38.066742            -8.078856\n",
       "3  chr1  930203  ...            -4.402274             3.053887\n",
       "4  chr1  930222  ...            26.421061            27.204330\n",
       "\n",
       "[5 rows x 3378 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Score the first 5 variants from ClinVar\n",
    "# All Scores\n",
    "it = variant_centered_sequences(clinvar_vcf, sequence_length=SEQUENCE_LENGTH,\n",
    "                                gzipped=True, chr_prefix='chr')\n",
    "example_list = []\n",
    "for i, example in enumerate(it):\n",
    "  if i >= 5:\n",
    "    break\n",
    "  variant_scores = enformer_score_variants_all.predict_on_batch(\n",
    "      {k: v[tf.newaxis] for k,v in example['inputs'].items()})[0]\n",
    "  variant_scores = {f'{i}_{name[:20]}': score for i, (name, score) in enumerate(zip(df_targets.description, variant_scores))}\n",
    "  example_list.append({**example['metadata'],\n",
    "                       **variant_scores})\n",
    "  if i % 2 == 0:\n",
    "    print(f'Done {i}')\n",
    "df = pd.DataFrame(example_list)\n",
    "df"
   ]
  }
 ],
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