From af3aa09cfecc309a46f40a21ceb7d518bd132817 Mon Sep 17 00:00:00 2001
From: Ziga Avsec <avsec@google.com>
Date: Fri, 26 Mar 2021 15:37:43 +0000
Subject: [PATCH] Update README links and add enformer-training.ipynb.

PiperOrigin-RevId: 365241561
---
 enformer/README.md               |   49 +-
 enformer/enformer-example.ipynb  | 1818 ------------------------------
 enformer/enformer-training.ipynb | 1017 +++++++++++++++++
 enformer/enformer-usage.ipynb    | 1714 ++++++++++++++++++++++++++++
 enformer/enformer.py             |   30 +-
 5 files changed, 2781 insertions(+), 1847 deletions(-)
 delete mode 100644 enformer/enformer-example.ipynb
 create mode 100644 enformer/enformer-training.ipynb
 create mode 100644 enformer/enformer-usage.ipynb

diff --git a/enformer/README.md b/enformer/README.md
index f7325e0..d6b2dc2 100644
--- a/enformer/README.md
+++ b/enformer/README.md
@@ -9,15 +9,9 @@ cite the following publication:
 "Effective gene expression prediction from sequence by integrating long-range
 interactions"
 
-Žiga Avsec1, Vikram Agarwal2,4, Daniel Visentin1,4, Joseph R. Ledsam1,3,
-Agnieszka Grabska-Barwinska1, Kyle R. Taylor1, Yannis Assael1, John Jumper1,
-Pushmeet Kohli1, David R. Kelley2*
-
-1 DeepMind, London, UK
-2 Calico Life Sciences, South San Francisco, CA, USA
-3 Google, Tokyo, Japan
-4 These authors contributed equally.
-* correspondence: avsec@google.com, pushmeet@google.com, drk@calicolabs.com
+Žiga Avsec, Vikram Agarwal, Daniel Visentin, Joseph R. Ledsam,
+Agnieszka Grabska-Barwinska, Kyle R. Taylor, Yannis Assael, John Jumper,
+Pushmeet Kohli, David R. Kelley
 
 ## Setup
 
@@ -46,7 +40,7 @@ python -m enformer_test
 We precomputed variant effect scores for all frequent variants (MAF>0.5%, in any
 population) present in the 1000 genomes project. Variant scores in HDF5 file
 format per chromosome for HG19 reference genome can be found
-[here](TODO).
+[here](https://console.cloud.google.com/storage/browser/dm-enformer/variant-scores/1000-genomes/enformer).
 The HDF5 file has the same format as the output of
 [this](https://github.com/calico/basenji/blob/738321c85f8925ae6ac318a6cd4901a42ea6bc3f/bin/basenji_sad.py#L264)
 script and contains the following arrays:
@@ -66,8 +60,8 @@ script and contains the following arrays:
     model(reference_sequence))`
 
 Furthermore, we provide the top 20 principal components of variant-effect scores
-in the PC20
-folder stored as a tabix-indexed TSV file per chromosome (HG19 reference
+in the [PC20 folder](https://console.cloud.google.com/storage/browser/dm-enformer/variant-scores/1000-genomes/enformer/PC20)
+stored as a tabix-indexed TSV file per chromosome (HG19 reference
 genome). The format of these files has the following columns:
 
 *   #CHROM - chromosome (chr1)
@@ -92,14 +86,14 @@ zeros.
 import tensorflow as tf
 import tensorflow_hub as hub
 
-enformer = hub.Module("https://tfhub.dev/deepmind/enformer/...")
+enformer = hub.Module('https://tfhub.dev/deepmind/enformer/1')
 
 SEQ_LENGTH = 393_216
 
 # Numpy array [batch_size, SEQ_LENGTH, 4] one hot encoded in order 'ACGT'. The
 # `one_hot_encode` function is available in `enformer.py` and outputs can be
 # stacked to form a batch.
-inputs = …
+inputs = tf.zeros((1, SEQ_LENGTH, 4), dtype=tf.float32)
 predictions = enformer.predict_on_batch(inputs)
 predictions['human'].shape  # [batch_size, 896, 5313]
 predictions[mouse].shape  # [batch_size, 896, 1643]
@@ -121,25 +115,34 @@ df_targets = pd.read_csv(targets_txt, sep='\t')
 df_targets.shape  # (5313, 8) With rows match output shape above.
 ```
 
-## Modeling Code
+## Training Code
 
 The model is implemented using [Sonnet](https://github.com/deepmind/sonnet). The
-full sonnet module is defined in `enformer.py` called Enformer. See the Sonnet
-documentation on distributed training
-[here](https://github.com/deepmind/sonnet#distributed-training).
+full sonnet module is defined in `enformer.py` called Enformer. See
+[enformer-training.ipynb](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/enformer/enformer-training.ipynb).
+on how to train the model on Basenji2 data.
 
 ## Colab
 
-Further examples are given in the notebooks `enformer-example.ipynb`
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/enformer/enformer-example.ipynb).
+Further usage and training examples are given in the following colab notebooks:
+
+### `enformer-usage.ipynb` [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/enformer/enformer-usage.ipynb).
 
 This shows how to:
 
-*   Make predictions with Enformer and reproduce Fig. 1d
-*   Compute contribution scores and reproduce parts of Fig. 2a
-*   Predict the effect of a genetic variant and reproduce parts of Fig. 3g
+*   **Make predictions** with pre-trained Enformer and reproduce Fig. 1d
+*   **Compute contribution scores** and reproduce parts of Fig. 2a
+*   **Predict the effect of genetic variants** and reproduce parts of Fig. 3g
 *   Score multiple variants in a VCF
 
+### `enformer-training.ipynb` [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/enformer/enformer-training.ipynb).
+
+This colab shows how to:
+
+* Setup training data by directly accessing the Basenji2 data on GCS
+* Train the model for a few steps on both human and mouse genomes
+* Evaluate the model on human and mouse genomes
+
 ## Disclaimer
 
 This is not an official Google product.
diff --git a/enformer/enformer-example.ipynb b/enformer/enformer-example.ipynb
deleted file mode 100644
index 9955dae..0000000
--- a/enformer/enformer-example.ipynb
+++ /dev/null
@@ -1,1818 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "enformer-example.ipynb",
-      "provenance": [],
-      "collapsed_sections": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "accelerator": "GPU"
-  },
-  "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 Avsec1*, Vikram Agarwal2,4, Daniel Visentin1,4, Joseph R. Ledsam1,3, Agnieszka Grabska-Barwinska1, Kyle R. Taylor1, Yannis Assael1, John Jumper1, Pushmeet Kohli1*, David R. Kelley2*\n",
-        "\n",
-        "- 1 DeepMind, London, UK\n",
-        "- 2 Calico Life Sciences, South San Francisco, CA, USA\n",
-        "- 3 Google, Tokyo, Japan\n",
-        "- 4 These authors contributed equally.\n",
-        "- `*` correspondence: avsec@google.com, pushmeet@google.com, drk@calicolabs.com\n"
-      ]
-    },
-    {
-      "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",
-      "metadata": {
-        "id": "hTGOLrbZxNHK"
-      },
-      "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'"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Eg8hcb45wqMM"
-      },
-      "source": [
-        "!pip install kipoiseq --quiet > /dev/null"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "MCDk7UQPG0Lr"
-      },
-      "source": [
-        "### Imports"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "NRI9KisU11bM"
-      },
-      "source": [
-        "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'"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "g0F1A9AaCrkQ"
-      },
-      "source": [
-        "transform_base_path = 'dna_transformer_2020_1kG_SAD.robustscaler-PCA500-robustscaler.transform.pkl'\n",
-        "model_base_path = '13376309-3.finetuned'\n",
-        "\n",
-        "transform_path = f'/root/models/{transform_base_path}'\n",
-        "model_path = f'/root/models/{model_base_path}'\n",
-        "fasta_file = '/root/data/genome.fa'\n",
-        "\n",
-        "# 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",
-        "clinvar_vcf = '/root/data/clinvar.vcf.gz'"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 142
-        },
-        "id": "OlE6JAVfI08a",
-        "executionInfo": {
-          "status": "ok",
-          "timestamp": 1610491573962,
-          "user_tz": -60,
-          "elapsed": 835,
-          "user": {
-            "displayName": "Žiga Avsec",
-            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjeqpTSqpdiH6-w18IlBq5jUYLVI3qWge6m6wC5=s64",
-            "userId": "06962645976249714923"
-          }
-        },
-        "outputId": "65dae93c-91a9-4f89-e8ad-4189a8b71ab7"
-      },
-      "source": [
-        "df_targets = pd.read_csv(targets_txt, sep='\\t')\n",
-        "df_targets.head(3)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "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  ... sum_stat                                        description\n",
-              "0      0       0  ...     mean  DNASE:cerebellum male adult (27 years) and mal...\n",
-              "1      1       0  ...     mean  DNASE:frontal cortex male adult (27 years) and...\n",
-              "2      2       0  ...     mean                                      DNASE:chorion\n",
-              "\n",
-              "[3 rows x 8 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 5
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Q8ZhswycGux3"
-      },
-      "source": [
-        "### Download files"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "_o7RuTMi1kYI",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "executionInfo": {
-          "status": "ok",
-          "timestamp": 1610491604608,
-          "user_tz": -60,
-          "elapsed": 32,
-          "user": {
-            "displayName": "Žiga Avsec",
-            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjeqpTSqpdiH6-w18IlBq5jUYLVI3qWge6m6wC5=s64",
-            "userId": "06962645976249714923"
-          }
-        },
-        "outputId": "cba1dab0-ba63-47d3-cd31-d30acbb2170a"
-      },
-      "source": [
-        "# Mount google drive. Models will be made available through https://www.tensorflow.org/hub\n",
-        "# and https://storage.googleapis.com/deepmind-enformer soon.\n",
-        "import os.path\n",
-        "from google.colab import drive\n",
-        "drive.mount('/gdrive')"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Mounted at /gdrive\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "rPk4M0jBCI-k"
-      },
-      "source": [
-        "# Download models\n",
-        "!mkdir -p /root/models/\n",
-        "models_dir = '/gdrive/My\\ Drive/models/'\n",
-        "!cp -R {models_dir}/{transform_base_path} /root/models/\n",
-        "!cp -R {models_dir}/{model_base_path} /root/models/"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "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",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "flOUYxP7Fjvh",
-        "executionInfo": {
-          "status": "ok",
-          "timestamp": 1610491763552,
-          "user_tz": -60,
-          "elapsed": 164448,
-          "user": {
-            "displayName": "Žiga Avsec",
-            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjeqpTSqpdiH6-w18IlBq5jUYLVI3qWge6m6wC5=s64",
-            "userId": "06962645976249714923"
-          }
-        },
-        "outputId": "5afcc88a-cf39-4142-e5df-f577a5d6bdd8"
-      },
-      "source": [
-        "\n",
-        "!mkdir -p /root/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 /root/data"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "--2021-01-12 22:47:12--  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  10.4MB/s    in 79s     \n",
-            "\n",
-            "2021-01-12 22:48:31 (11.9 MB/s) - written to stdout [983659424/983659424]\n",
-            "\n",
-            "genome.fa  genome.fa.fai\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "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",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "4PjXFN5OcGbH",
-        "executionInfo": {
-          "status": "ok",
-          "timestamp": 1610491766389,
-          "user_tz": -60,
-          "elapsed": 167174,
-          "user": {
-            "displayName": "Žiga Avsec",
-            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjeqpTSqpdiH6-w18IlBq5jUYLVI3qWge6m6wC5=s64",
-            "userId": "06962645976249714923"
-          }
-        },
-        "outputId": "dd1dafe7-426b-4e73-9c9b-3f2ab6cc7aa6"
-      },
-      "source": [
-        "!wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz -O /root/data/clinvar.vcf.gz"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "--2021-01-12 22:49:23--  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)... 130.14.250.7, 2607:f220:41e:250::10, 2607:f220:41e:250::11, ...\n",
-            "Connecting to ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)|130.14.250.7|:443... connected.\n",
-            "HTTP request sent, awaiting response... 200 OK\n",
-            "Length: 34090716 (33M) [application/x-gzip]\n",
-            "Saving to: ‘/root/data/clinvar.vcf.gz’\n",
-            "\n",
-            "/root/data/clinvar. 100%[===================>]  32.51M  16.1MB/s    in 2.0s    \n",
-            "\n",
-            "2021-01-12 22:49:25 (16.1 MB/s) - ‘/root/data/clinvar.vcf.gz’ saved [34090716/34090716]\n",
-            "\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Omj-KERcwSdB"
-      },
-      "source": [
-        "### Code (double click on the title to show the code)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "cellView": "form",
-        "id": "47E4AEgLx1VT"
-      },
-      "source": [
-        "# @title `Enformer`, `EnformerScoreVariantsNormalized`, `EnformerScoreVariantsPCANormalized`,\n",
-        "SEQUENCE_LENGTH = 393216\n",
-        "\n",
-        "class Enformer:\n",
-        "\n",
-        "  def __init__(self, saved_model_path):\n",
-        "    self._model = tf.saved_model.load(saved_model_path).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, saved_model_path, organism='human'):\n",
-        "    self._model = Enformer(saved_model_path)\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, saved_model_path, transform_pkl_path,\n",
-        "               organism='human'):\n",
-        "    assert organism == 'human', 'Transforms only compatible with organism=human'\n",
-        "    self._model = EnformerScoreVariantsRaw(saved_model_path, organism)\n",
-        "    transform_pipeline = joblib.load(transform_pkl_path)\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, saved_model_path, transform_pkl_path,\n",
-        "               organism='human', num_top_features=500):\n",
-        "    self._model = EnformerScoreVariantsRaw(saved_model_path, organism)\n",
-        "    self._transform = joblib.load(transform_pkl_path)\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."
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "cellView": "form",
-        "id": "uLYRbOXDyA36"
-      },
-      "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}}"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "cellView": "form",
-        "id": "Up1oCMFiPucp"
-      },
-      "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()"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "zEwfoz3cwOzt"
-      },
-      "source": [
-        "## Make predictions for a genetic sequenece"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "WC-pgC35DgnL"
-      },
-      "source": [
-        "model = Enformer(model_path)\n",
-        "\n",
-        "fasta_extractor = FastaStringExtractor(fasta_file)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "8u8Gt8WWyG53"
-      },
-      "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]"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 441
-        },
-        "id": "x7RrBbPFJdf8",
-        "executionInfo": {
-          "status": "ok",
-          "timestamp": 1610403486072,
-          "user_tz": -60,
-          "elapsed": 29386,
-          "user": {
-            "displayName": "Žiga Avsec",
-            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjeqpTSqpdiH6-w18IlBq5jUYLVI3qWge6m6wC5=s64",
-            "userId": "06962645976249714923"
-          }
-        },
-        "outputId": "c25e94f8-ee03-4b4d-8f3e-b9610a0f4527"
-      },
-      "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)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 1440x432 with 4 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gM2KwV8gwMNj"
-      },
-      "source": [
-        "## Contribution scores example"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "o4c2W_MjNBzp"
-      },
-      "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]"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 225
-        },
-        "id": "nzHZiE40Wnyk",
-        "executionInfo": {
-          "status": "ok",
-          "timestamp": 1610403508617,
-          "user_tz": -60,
-          "elapsed": 51920,
-          "user": {
-            "displayName": "Žiga Avsec",
-            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjeqpTSqpdiH6-w18IlBq5jUYLVI3qWge6m6wC5=s64",
-            "userId": "06962645976249714923"
-          }
-        },
-        "outputId": "68439c7d-1e26-417f-b6f2-a858e3c9fece"
-      },
-      "source": [
-        "tracks = {'CAGE predictions': predictions[:, 4828],\n",
-        "          'Enformer gradient*input': np.minimum(pooled_contribution_scores, 0.03)}\n",
-        "plot_tracks(tracks, target_interval);"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 1440x216 with 2 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "iiDN_ScSv3sI"
-      },
-      "source": [
-        "## Variant scoring example"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "hIaN9GKDVwLK"
-      },
-      "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]"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 368
-        },
-        "id": "Bczbku-FUoGr",
-        "executionInfo": {
-          "status": "ok",
-          "timestamp": 1610491873418,
-          "user_tz": -60,
-          "elapsed": 1993,
-          "user": {
-            "displayName": "Žiga Avsec",
-            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjeqpTSqpdiH6-w18IlBq5jUYLVI3qWge6m6wC5=s64",
-            "userId": "06962645976249714923"
-          }
-        },
-        "outputId": "95294fac-824d-4a82-864d-2b246b7d7973"
-      },
-      "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)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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SUg/RVJJS4t9fOI5vv3QCj+9uynQ5KbWtphc3/XwnHt7RiE5vJCXrfP5QGz7x8Nvo8Ufw6K7GlKyTpi8GnImIiIiIiIimDgPORERERDRlfr2neUq3F4wpw6aaJiIiIiKimUNKiZ++Xo2vPH0Ekfjo4eLf7W0Z+vr+LXXwhOIp2fapdwWcq9omHlRWVImvPn0EoVgiJTURTQUpJf7n1dN4cp/2v/Xfr54e8f8wU7X0hfClJw8jHU2pDzYP4PqfbkdNN8Ovs119b/DcCxERERERERFRSjDgTERERERTom0ghLdOd0/5dp/e3zJiemkiIiIiIpreInEFX37qCO57q+6sy+1p6ENDbwCt/SE8tqspZds/2Tn8PcTRSQScAaDRHcRXnjqCcExJRVlEaSWlxE/fqMGD2xuGboslVHzpycOIxGf233AolsDnfnMA3nBqBkGMJp3rpumjuS+IxAQ7+hMRERERERHR5DDgTERERERT4ndvt0BNQ5ekc1El8J9/PgmZjhZNRERERESUcttrevGRX+zCS1Ud41r+yX0t+PHr1YilMHBW3eWDcsYbmKNt3kmv6/WT3fj4Q3vQ44+kojSilAhGE6jvfafbsKpKfPdPJ/GzUQYV1PYE8IstZx9sMJ1JKfHN54/hNAc/UwrEFYnWgXCmyyAiIiIiIiKaExhwJiIiIqK0i8QVPLWv5dwLpsmOWje21vRmbPtERERERHRudT0BfOqRt3Hbo/smFET8/dstePHI+MLQ4xWJq2h0BwFonV9rus8vGFnV5sVHfr5rWKCUKFM6PGH8zS/34NqfbMMdT+zHnvo+/L/nj+Lx3U1jPubx3U3wRWZmh+IndjeNe8AE0XjU90zPfXmvP4pANJHpMoiI6Az9wVimSyA6b0omuhcRERElGTJdABERERHNfn8+2omBUGYvhP7gz6dw1eJ8GPUc40dERERENN28drwL//LMEQRjyoQfO5nHjMepTh8WFzpwosOXktloOrwR/NPvD+OFL14Bs0F//iskmoQjrR589tcH0OuPAgA2n+rB5lM953ycP5LAb/Y044ubFqe7xJQ62DyA//zzqUyXQbNMfW8A16Eo02UM8YbieGBbPR7b1QgJ4OolBdi0vADdviiOtHpgN+lx/9+tgxAi06USEc0qqiqRUCVMhtGvOext6MO3XzyB176ygftgmtE+/tAeqBJ47/JC3LyuDCVZ1nE9rq4ngMWFjjRXR0REsx3THURERESUVgeb+/G/f6nOdBmo6wng8785iGPnMbU0ERERERGllqpK/OT1atz524NpCypP1qlOHwCgqtWT0nX+3+balK2PaCKqWj249aG9Q+HmiXpkZyNCsZnTHdYdiOKLvzuEBDvOUYpNh278qiqxr7Ef33npBDb86C38cls9ogkVsYSKzae6cfcfj+O+N2uxvaYXrx7vYhdzIqIUk1Liey+fxLdfOjHq/TXdfnzu1wdQ3e3H4RS+nyCaagea+rG/aQAHmwfwv3+pxh1PHEAsoZ7zcbGEitsf3YfXjndNQZVERDSbsYMzEREREaWFokr8Yksd7n2zdtpMX/XW6R68dboHVy8twF3vX4YLyrIyXRIRERER0ZzlDkTx1aePYEetO9OljGow4Hw0xYMkH9xWj2uXF+KSBbkpXS/R2TS5g/j7x/cjHJ/8QIL+YAxP7mvFP1xVmcLKJkdKCSkBnW5kN8SEouKFIx24781adPkiGaiOZruG3uCUb1NKid31fTjcMoCTnT4caBpAzwQGK3z/5ZPYuLQQWTZjGqskIppdXj7agV5/FJX5dqwocaHIZRm6757NtXh8dxMAYG1FNv52fcXQfe2eMD796D74ItrAsD8casO6eTlTWjtRqvxyW8Ow7090+HDvmzX4xvuXn/VxfzjUhnZPGP/2x2NYNz8bhU7LWZcnIiIaCwPORETTlDcUx76mfly/cvpMdUdENF5N7iDueu4o9jX1Z7qUUW2v6cWuOjfuuKoSX7luKawmTg9NREREExdNKHjhcDsEBN5/QTGyrEZIKXGk1YM3T/WgKMuC1WVZWFHihNnA4w2iM73d0IcvPXUY3b7JdZKdCqc6/QCAo22p7bimSuBrz1ThlS9vgMPMU/SUfu5AFLc/tg99wdh5r+uh7fX45GXzMvq61uEJ4z9ePI6+YAz/d8tFmJ9nB6AFm1+q6sDP3qpDo3vqA6g0d6Sqg/POWjfqewP4xHvmwagfe9LdYDSBf3/hOP54uH3S23IHYvif107jv29ePel1EBHNJQeb+/Hlp44Ma95y8fwcfGhNKXzhOO57851ZWf79xeNYXuLEkkInfrWjAQ9sq0fojNlp/lTViW/duJLnBWjGqevxY/Op7hG3P7C1HpuWvTNo1x2IwmkxDP2NxxIqfr6lDoA2SPJfnz+GX91+CYQYOTiRiIjoXISU06Ob3kwhhDi4bt26dQcPHsx0KUQ0S3nDcTyysxGP7WyEP5rAP793Mb52/dJhB/wJRUVtTwBH2zy4tDIPlfn2DFZMRPQOVZV4Yk8TfvjaaUTi556iajooz7FiZYkLoZiCmKLijqsq8b5VxZkui4iIiKYxRZV4fHcTHt7eMNQZ0mTQYcPifNT3BtDUFxq2/IoSF57+/GVwWdgxj2g6zvRyNlu+vhGbfrw1Leu+8cIS/OzWtbzIS2kViSv4+EN7cSSFU6PPz7Nh3bwcrCnPQmm2FUa9DnqdgF4nIARg0OlQkmVBabYV+lE6LI+HlBLtnjBOdvjQ6A7CZtIjy2ZCpyeM+96sRTAZGrKb9Pj2Taug1wn87K3aEa/BROly6FvXI9dumtRjVVXigW31+Mnr1VAlsLjQgbs/sAIl2RbUdgfQ6A7CaTGgIscGs1GHb794Ag0pCu0/+dnLcPmivJSsi4hothoIxvDB+3agwzv+mSCKXRYIAXSO8ZhffnIdbrigJFUlEk2Ju56rwjMH2ka9ryLXim/esAJ/PNyOrdU9WDsvGw9+6hLk2k14al8LvvmHY8OW/+HHVuOW9fOmomwiIpq+JnWSiAHnCWLAmYjSqbbbj79/Yj9a+8PDbv+Hqypx9wdWYG9DH57c34rNJ7uHppO0GHX45g3LcdvlC0adkpGIaKqc7vLh3/94HAeaBzJdynm7bkUhvn3TKlTk2jJdChEREU0zCUXFvzxbhRePdEzocVcsysNjn1nPjk00p7V7wvjqU0em7Uwvo/nCxkV4YGt92tb/vQ+vwm2XL0jb+mluk1LirueO4tmDo4cS0s2k16E814pcmwnZNhMKnGYsLXJgWbHW4TDfYRoR8O/2RfDUvlY8tb9lzIAQ0XTw7J2XY32ya+FEeENx/MuzR7D5VE8aqjo3l8WAZ+68HMuLXRnZPhHRdKeqEnf8+gDeOp3a/fT1K4vw8G2XpHSdg8IxBQa9OOtsAEQT1e2LYMMPtyCmjL+Z0bxcGx667WLc8cQBtA0Mzzu4LAa89fWNyHeYU10qERHNHAw4TwUGnIkoXbbV9OKffncI/mhi1Ptz7Sb0n2UaycsX5uE/P3oBFhU40lUiEdGovOE47t9Sh1/tbJwRHdjGy2LU4fNXL8Kd1yyC1cQgEhEREWnh5q89U4WXqiYWbh5005pS3HvLRRycSnPO8XYvntrfgj8eah/qujpTFDjN6PVH07Z+o17guTuvwJqK7LRtg+aux3c14jt/OpnpMsbkMBuwIN8Gp9mIhKoimlBxssOHxCw6t0Cz12S6EJ7o8OILvz2Elv7MdhovdJrx/Beu4MB+IqJRPLqzEd97OfXHTwadwL67rxt39/9uXwTZNuO4Bkl/49kqlGZb8dXrl55vmURQVYnXTnThJ69Xo7534jNIGHRizOP5m9eW4ae3XHS+JRIR0czFgPNUYMCZiNJhy+ke3PHrA+cdDDTqBT539UJ8cdNi2EyGFFVHRDSSNxzHa8c78erxLuyqcyOuzN5jytIsC756/VLctKYUFiODzkRERHNVMJrAXc8dxZ+PdZ7Xer7x/mX44qbFKaqKaHo72eHDt148joOzYJaXdCrLtuKuG5bhvcsL4bQYM10OzRI7anvx6cf2z6qByETTyeeuXoh/+8CKcS0rpcTT+1vx7ZdOIJoYfxfEdJqfZ8MPPrIaS4ocKHSaR3RTJyKai+p6/PjgfTvTtq/+179ajs9fs+icy/UHY7jpZzsRjiv42Loy3HrpPCwco8HVcwfb8PVnq6DXCTx35+VYOy8n1WXTDFTV6sHOOjfqewNwB2L48JpS3LimZCgwH44p6PFHkOcww27SI6aoqOkK4Fi7F7/f14zj7b601fb7z74HVyzKT9v6iYhoWmPAeSow4ExEqeaLxPG+n25Hly91Uy4WOs346Loy3Ly2HMuKnSlbLxHNbVJKvN3Yj6f3t+KVY53T5oLMVMm1m3DL+gp8dsPCcXdZOJfBY3FeRCKiQTXdfjyxuwmKKlHosiDHZoQvnEB/MAoJ4OL5OVi/IBel2dZMl0o0a0gp0dofxsGWfliNBiwrdmJerg36M7osH2oZwNeePoKmvvPvtmfUC7z4xauwspTTgtPsFYkruPfNWjy0vYHhygkwGXTYsDgfS4qcKHaZUVngwIbF+Snr+h5NKKju8uNYuxcuixEfXF3CjvKz1DP7W3H3C8dm9WBkoky7dnkhHvn0+nMu19IXwt0vHMOOWvcUVDU5DrMBF5Zn4fKFebhicR7WVuTw9YGI5py4ouJjD+zG0TZv2raRYzNi+12bzjqoMaGouO3Rfdhd3zd0mxDATReW4kvXLsbiwneu+9Z0+/Ghn+9EJK5dq1mQZ8Ofv7QBdjObYM1VgWgCP3rtNH6ztxnvjoLlO8y4ZmkBqrt9ONXpH3qvbjXqkVDVKXvvsLDAjle/vGFc3cmJiGjWYcB5KjDgTESp9q9/OIon97Wmbf3z82woclmQazPBYTFALwR0OuCCsizcvLYcVhPfPEyGlBJ7G/pR1eaBlNrJhaVFDqxfkMtuSzTrSCmxtaYX926uxZFWT6bLybg15Vl46nOXT3r/2eEJ4/mDbTjc6sGRVg8GQjHYjHq4rEZ8/8MX4LqVRSmumIhmgoPN/fjFlnq8dbpnXMtbjXrodQJ6ncCF5Vn46NoyvH9VMS9gEE3Qi0fa8V+vnEK3LzrsdrNBh8WFDiwrcsJi0uOpfS1IZUZzebETL/7TlbyYQ7OCoko0uoNDwdmqVg+OtXsRiCYyXdqssKzIia9evwTXryzGyQ4ftlT34HSXD1ajAXazHlajHiaDDka9DooqEUkoiMZV9Aai6PFF0OuPIhhTEIkrCMWUYYHzFSUu3HXDMmxcWsBBl7OEokr8z6un8PCOxkyXQjTrLcizYes3NiGhqHhsVxPaBkK4fFEeLq3MQyCSwOkuHw63evDYrsah4NlMsbDAjk9fsQA3ryuHg+8xiWiOuHdzLe7ZXJP27XzluiX4ynVLx7z/+y+fxCM7Rz+WEwK4dEGudt3XbsL2ml40uIPDlrn10gr8980XprRmmt7iiooTHT7sa+zDY7ua0OlNXVO1dFk7Lxufv3oRrl9ZNKzBABERzXoMOE8FBpyJaDKiCWXUC9e769z4xK/ezkBFmhybEZ+8bD6uWJSPeXk2FLssfBORJKXEiQ4f8hwmFLssEEJAUSVa+kPYU9+HJ3Y3obrbP+Jxep3AyhIXsm1GGHQCBU4zPrthIZYUsZM2zQzBaAKd3jDaPRHU9wRQ2xPA4ZYBnO4a+fc+l71vZREe+OTFE9pndnjCuH9rHZ7e3zrmSHidAL5140p85srKVJU6JQZD8G0DYRQ4zCjOsuCCUhcMel2mSyOa9o61efGTN6qxtbr3vNdlN+lx9wdX4tZLKxhSIhqHR3Y24vsvn8zY9u+8ZhG++VfLM7Z9ovFIKOqIY7qWvhD+5sHdUFTAoBMYCMXm3OwumWAx6tIWkCtwmnFpZS7eU5mLKxblY1GBfc4cS3T7Ijje7oUQwJJCJ8qyrTOia6k7EMVbp3vQ2h9Ca38IXb4I3IEYev1ReMPxTJdHNCfodQLP3nk5/uPF42mdxj2TXBYDvrhpMW6/YgEsRg7MI6LZ63SXDzfetxOJKZh9xmE2YPtdm0bMEtkfjOG+N2vx+O6m897Gdz+0CrdfseC810Pnp90Thl4I5DlMMOp1UFUJfySBQCwBAUAnBLKsxkk304kmFNy/pR4P72hAKKaktvgpMi/XhvULchFTVETjCgx6AavRAJtJDwmJhCKhSok8hxklWRZU5Nqwbl4OsqxsNkZENEMx4DwVGHAmoomo6/Hjvjfr8KejHXCaDSjLsaE8x4p5uTbMy7XhkZ2NaOk//ymOU8Vi1OHWS+fhC9csQqHLkulyhjnYPIAXj7RDlRI6IWAx6pFtMyLHZsLiQgdWl2Wl7CTr2w19+MnrNdjX1A9AO5Fb6LKgpT+E2CQu2goBfHhNKb6wcTGWFQ8POodjCixG3Zy5cDhTqaoWeK/vDeCCMhcW5jtGXPCUUmJfYz96A1EYdDpYTXqsKnUh32HOUNXn1uuP4kevncaOWjeiCQWRuIpwfGaeBMmEf7iqEt+6ceW4lq3r8eOj9++GPzK+Lnb/cFUl/v2DK2bEvqGm24/vv3xyxFSrWVYj3ru8ENevLMKmZYWcMWCak1KiLxiDXghk24wz4m9vJuvwhPHKsU68cqwTh1pS3xn/uhWF+J+PXTitX4OIMskbjuP+LXV4cHtDRuvQCeBXt1+C9y7n7A00ffT4I3j1WBf2N/XjaJsXiirx8j9fhZzkxXcpJT792H5sqzn/gTk0fZVkWXDN0gJ8dG0ZLq3MnVXHhqFYAttrevH6iW7sqHOj1z+8g7/FqHXwX1LoxJIiByrz7JifZ0dlvj3j72mklKjrCeDRXU14/lDbpM5RERFNRmmWBbddsQBOiwE6IVDkMuOKRfkjzsdLKdHcF8KhlgHodQL5DjPKc6yYn2fPUOVENNNF4gp6/VH0+CPo8UUxEIpDSeZbFhc4cNnCsx+rNrqDePZAK051+nDvrWvhGmUGViklbn14L/Y29Kft53i3z26oxN0f1K4tDARj+O3eZjy4vSFls+AIATz4yYvxvlXFALRBfc8dbMP6Bbm4eH4Om12lSbcvgkPNA9hV78b2GvewDIDDbEAwlsC741lCAJV5diwvceKS+bm4emnBuAacHmjqxzf/cAx1PYF0/CjTmhDAyhIXrk6+Z106RpOx1v4QDjT343i7DzXdfkgJGPUCRr0OFqM2G5LNrIfLYoTLasRVi/NHXMcnIqKUY8B5kBCiHMD3ANwAIA9AJ4AXAHxXSjlwnutmwHmOeuVYJ7ZW96CmO4ArFuXhG+9fNqtO7tPoqrv8+POxTiwrcuKSBTkoGmfot7U/hB+/Xo2XqjpGvFGZCcwGHW5ZX4G/vaQCq0pdaflbjyVUdHkj8EXiUFQJRUpIKaGoGAox6wTgi8TxyM5G7KrrO+v6jHqBC8qycOv6efjw2tIJTfXc44tg86keHO/w4mibJ60dN5YVOfHBC0uQazfhzVPd2FXfhxtXl+B/PnYhTAZ2Op0u4oqK6i4/qto8ONg8gO01brgD71z0dJoNWF2ehTUV2VhTnoWmvhCe2teCpr6RAxYW5NlwUUU2KvMdqCywozLPjsoC+7DpHaWU8IbjcAei6AvEkG0zoTTbAucoJ9tSwR+J45kDbfi/N2rg57TR52XDknx85boluHh+7pjLDARj+Mj9u9A8yt/H2dx1wzL848bF51viqCJxBW0DITgtxnG/tr2bOxDFPW/U4Ml9LThXYwurUY9rVxTiikX5sJv1sBj1cFoMyLObkecwIddmmhFd0mab+t4A/nioHbvr3ajrCcCXDOAb9QLFWRZcvjAPm5Zpv7cs2+j7o0A0gVOdPjT0BtA+EEanN4J8pxlLixxYkGdHJK7CE4rBE44jGlcQU1RE4ioC0QT8kQSybUasrcjG2nk5yLWboEqtI4MvnIAnFIM7EENTXxB1PQH0+KMocppRmm3FFYvzsLzYNZVPV0ocbO7HL7c14I2T3WnfltNswHUri3DDBcW4eklBxgM5ND7ecBz7G/txpNWDo+1e9AWi8EXiiMRV5NiMyLObUegyoyzbivIcG5YUjRzoF1e0AUvxhAq9TiDbNrwjUCSuoL43AJvJALtJjzyHecZf2JJS4lCLB4dbBtDuCaPDEx4KfemEgMtqHOrscqC5Hyc7fOd87ZoqFqMOv//sZVg3LyfTpdAs4Y/Ehy7eNfeFUOA0ozLfjtJsC1Sp7SMicQX+SAK+cByhmIJoQrvtYPMAdte7R/x/bFxWgEdvXw+dTuDlox34p98fzswPRxmxIM+Gj6wtw5WL83FhedaEzreci5QSqsSUvA6FYwoe3tGAX26rn1R3M6Ne4LKFebh+ZRFuWFWc1sYAUkrU9wZxosOLup4A6noCaOoLoaUviOAM7cxGRLPP4HvO+Xk2dHkj6PBGcKrTN2LgCACsKc/CrZfOw41rSoedEyWimS0YTSAYSyDfbh46txuJK2j3hBGIJBBTVMQVFVajHg6zASaDbmjGCV84DjV5AdWg18FhNsBhNqDDG9bOibR50NofPufMFGsqsvGPGxfh+hVFw84ve0Nx3PV8Ff5y4p1zcOsX5OCJv78UNtPw/dArxzrxj787lKqnZVxMBh3u/sAKvHW6B7vq3GnpHG0x6vDo7euxr6kfD25rGGpuk+8w4WPryvH5axaN6CI9V0UTChp6g6jp9sMXScCkF9DrdAjHFfgjcQQiCYTjWpOgWEKFhPb7SigSoZiCUCyB5r4Q2j3hlNRTmmXB2nk5WF2eheXFTijJzs/uQBSHWz041DyATm8kJduaDVaUuHDlojxUFtixIM+Omm4/XjzSgSOtE28sct2KQtx5zSKsm5fDa1ZEROnBgDMACCEWAdgNoBDAiwBOA7gUwCYA1QCulFKePSV39vUz4DxH/cPj+/Hm6Z6h7//uPfPw/Q9fwAObWSShqOgPaW+sW/vDeGp/y4ipwotcZizMd2BhgR3zcm0ozrKg2GWBw2KA2aCHEMDv9rbgN3ubEFdmx/51WZET711RCJNeByEAm0mPihwbKnJtWFbshFE//lBujz+CX+1oxItH2tHjj6Yt/F3oNOO2y+dj47JCrCxxjfl/KqXE0/tb8YNXTo27q2q6XLU4Hw98cl3aAq0zSSyhoqkviJa+EHLsJlTkWJHvMA/7PTa6g3hsVyNOd/lRnm1Fea4Nt6yvQFm2dULbisS1kxa1PX5Ud/lR1xNAozuI5r4QYkp6OyEVucywmw3whuLwhLWg/7vZTHoIAIqUsBr1WF2ejYsqsrGyxInSbCtKs60wG3QIxxSE4wryHOYRFwmC0QRa+kNo7guhttuPHbVuHGoZmJKp1uaSqxbn42MXl2Hj0sKh7naA9vf8qUfextuNk+v+cO/HL8KHLyobcbuiSm0Kswkch3jDcTyxuwlP729Fhzc8tA9eVerCe5drIda187LP2Q3fH4njN3ubcf+W+pR1lLCb9Fha7MTSQifiqopefxQDoRgqcmxYWuTEvFwbQnEFvnAcwWgCCVWbmiymvNN5vMhpwQVlLqwsdWFBnn1aT52qqBLdvggC0QRsJj3sJsOkuibHFRWhqDIUClYlhk6uAlqoD9DCHKGYAk8ohga3FhY+0DyAqgmc4HOaDSjOsmh/31IbjNQfjKGxL5iRwVxCAB9bV46vXb8UpRPc90+1dk8Yr5/owktVHTichm7N4+EwG3DTmhL87SUVuKgimwM1p6FuXwS/2tGA37/dMuHgkkEnsKLEBb1OoMMTRm9g+HH2kkIHNi4rwOJCB7bV9GJrde+wYFe2zYgrF+fj6iX5WFWahYUF9hEX+wBtqtS6ngAqcq0odllG/TvyhuM41elDbbcfTX0hlGZbsaLEiaVFzrQMZun0hvFyVSeePtA6ozvWZNuMeO7Oy7G4cOJdYgLRBCJxBXl209DvxBuOo7bbD51Om+rUaTZopw0lIISA3ax1qRntdxhLBuMHw4YDwRhOdfpQ1xuAP5JAOKYgrqow63UwGZIdb0x62Ex65Nq16UKLnBaYjcPfJ0qp7btNeh10OgEpJQZCcXT7IgjFFG2bydqybSa4LAYYJvBec7xUVcIXicOg18Gk1yUH1cThi8ThshhR4DTP2H1kfzCGR3c24ondTWkZxPiN9y/Dpy6fj2t/sm3U4BLNDWaDDkuKHDAb9DAk9xNxRUVMUVGSZcVFFdp71iKXBS6rFlIJRhV4wzH4IgkMXovo9ceSr0k96PVHUeSyoCzHivcuL8RfX1x+XrNQKKrEqU4fDjT1Q0I7DookVDywpQ4dKQoBGHQCH1hdgs9cuQBrUzhA5XSXDy8d6cBrJ7rQ0BtM2XqJiKYLvU5gaZETa8qzMC/PBlWVSKgSRr1uaFBittWIbJsRLosREtrrTEKRsJr0sCe7LE7ncz7jEY4peONUN/bU98EXicMXjiPPbsLq8mysLstCSZYFWTbtOH6mHpueKa6o8IbjMOgETAYd9DqBWEILCeqEgDNNx/5jUVWJ3kAUnd4IPKEYvOE4hBDIt5uQ5zDDbtbDbNDDYtRq1Qmt2+d4B2SpyfPug+9/Bwd0xRUVoZiCYPJYvchlGdb0RkoJXySBvoB2XrQs24YiV+bfn0TiCg63eHC4dQB9gdhQo5ba7sBQmNOoFyhyWRBLqOjJ0HuF5cVOfOW6JXjfymIc7/DiH393CG0DI8OmG5bk4+HbLhnaj4RjCq776baUBVNnEofZgM9uWIjbr5g/YnB8JkXiCqpaPahq86CqzYu2/hC6fBH0BWJaWN5igNWkh1Gng0Gv/X/EEiqiCRU2kx6U3NHnAAAgAElEQVRFLguKXGYUZ1lRkmVBabYVq8uyRoS5pZTY29CPR3c1YsvpHl4zo2GcZgNWlrqwuiwL6ytz8Z7K3LP+n0gpEU2oMOjElL6mjUZVJYRAxl8/aPYIRLXjE4fZgGybaVKD1IPRBPoCMQSi2oCRynw7B9nMXQw4A4AQ4i8A3gfgS1LKn51x+08BfBXAg1LKO89j/Qw4z1HvDjgDwIfWlOKDF5ag0R1EkzsIfzQBRZGQkCjNtqIy347lxS5O9ZJBUkrsaejDU/tasa2mFwadgMWoh06njaqMK+pQp6LZEkieSgsL7Lj7Ayvw3uWFIw6SB4IxVLV50BeIwReJo7YngOcPtiE6xdNnZtuM2LSsEJ+/ZuFQp0cpJQ40D+CeN2qwu37SY15SblGBHRdV5AxNj6PXCRiTo4SF0F7phQD0QkCnE8i2GrGwwIHKfDsKnOYJn9gd7GqXis7R3lAcNT1+9Pqj8EfiWjewSAL+SByRuAK7yQCX1Qi72QBT8udTkh06/ZE4ev1RdHjDaB8Io3UgPCLsazboUJlvx6ICB8JxBVuqe0YE6qxGPf752sW446qFMBl08IRiON3lR1WrB4dbPGhwB2AxauELRZVo6Q+h2zf7LswXucyYn2uHLxJHpzdyzg4HlFo6AVxUkY18hxlmox7d3gj2NU1+ajujXuB7H74And4I3m7ow8lO39BrltWo16ZOLnLAqNOhLxhDfzAKIQTMBh2sRj1y7SbkO81QVIkn97WcczCHUS9wYXk2NizJx6ZlhVhdlgUhAH80gcbeIJ7a34oXj7RPquPZVCtymVGeY0Oh04wCp3nYyadYQruw4g3HEIkPdvgEynNsWFXqwooSF5wWA/Q6ASmB5r4Q6nsDaBsIIZpQhx0/ROIqglFtnxeIxmE3GVDgNKPYZcHSIieWlzhR7LLgcIsHu+vdONzqQftAeMQJ0yKXGRuWFGjP/fLCEdMldnjC2FXnxu76Phxt88CdvKAw15kNOlyztACXL8rDZQvzkGU1IqFIRJJdN051+tDgDkIvAItRD5NBN/TOOZpQ4QnF4QnHYNDpUOA0o9BpxsZlhXhPZe6YQUxVlaPeNzgjxFunu+EOxKCoEqFYAvXTLJxS7LJgeTJwWpZt1TrkWAxYXOjAwvzRpz+MJVTsaehDOJaAomoDcBYVOFCWY4VeJxBXVAwEY+j2RdHpDcMdiOGSBTljTg84UbGEitNdPlS1edHlDWN+rh0LC+woybbCaTHAYTLAG47jdJcftT1+xBIqTMn94OJCB5YVO0cN7GaKNxRH60AIDe4gTnb4cKLDi7cb+tM+uGoiSrMsKM+xoTzHCpfViEMtAzjW7h06/rKZ9KjM17qiLMi3wRdOYH9TP6qT0z2OxqATyHOYUJxlRWWeDZX5DjgsBniTXd4FAIfFAIfZCKN+8CKy0PapWVZkWY1wB6Lo9kVQ0x3AW6e70zrzylQrybLgvz66GhuXFZzzAkRdTwCvHOvEjtpeHG7xIKFKuCwGVObb0R+KobX/3BdmhdCOn416HYx6HRKq9no2+L7YYtQCwL40DAQ16XWQkGd9Dy4EsCDPjmVFTiwrdmJFiQsrSpyoyLGNuX+OxBX0+KIIxxUoqvZ+v3UghPqeIOp7A6jvDaChNzjUtWo0DrP2PC4pcmB5sROV+Q5EEwp84QQSqopFBdo+ZazwZTimdUhv7gthXq4Ny0tGHxQcV1SoUo7ohCulRK8/ivreINo9Yawpz8LiQsc74fVQHNXd/mTnKAUDycFGjb1B7Kxzp/UYTSeAyxbmTav30DQ7GfUC168swuUL83BheTaWlzhH/K8kFBXuQAwNbu3/unUghJ7kcciJDt+UDmIvcpmxtEg7tnpPZS6uWpI/7uMOVZXo8kWws86N37/dMqkOY0REc5HTYkBhcoanhfl2LCxwIPuMmaeKXRYsKnQMGwQ4UYMD8jo8YVhNeuTYTMiyGsd1rS8US+Dthn7EFRUuqxFWox6d3gha+oM41enH6ye6xjWw1WTQ4arF+UMzM9nM2gCjhCrRH4ihLxhFIKoMXTeIJbQZs4JRBZ3eMJr6QmgfCMGo18FpMaLAacKq0ixcVJGNeXk2xBPaIKV4Qg4Nnh/8WSd6rSGhqDja7sXuOjfqe4Po8UfQ648mmxic+/yVw2xAWbYViwsdWFRgB6AN3PSE4whGte6owZgydO0hHFOQUFWoqvY8ZduM2nlQh3ko3Dh4/KBKiS5vBK0DIbT2h9A68M6MPxNhN+mRZTXCatIjEtfOCwJAjt2EHJsRcUVraNDrjw6d99PrxKiNTQDtd1bktMBm0ifPU8ZHnC/Md5iwosSFHJsJLqsBNpNBC4crKswGHRYWaM9XttWEmKKFxgcHnyUUCYtRB3uyM7JJr4PRoEMwmsCOWje2VvfAHYjhqsV52LCkAFcsyhsWyvNF4vi3PxzDX050zajrp0sKHedsYDM/z4a/vaQCf31xOZ7c14L/21w7hRVOPwadwOWL8nDDBcW4qCIb8/PsGem2L6XEq8e78IM/n0pL4Hx+ng3LipzQCQFFSrT0hVDd7U/5dmh2EgJYVOBAkcuMXLsZFoMOnd7IULOJUEw7FyUEkGU1ItdmgkGvXV8aHOC1uiwLy4qdyLYZ4bQYoUqJ2u4Aarr9aBsIwR/RZr0MxrTXuWhChd2svS5rH8bk65AB/oj2ujG4XTU5U3Brv9ZJfHDgTlGWBTesKsZH15Wh0DnxWYgUVaLHH0EsoUJRJQw6HYqzLOPKFqiqhDsYRY9PO5/rCWk1B6OJoTY9UgIJVUVc0Wb61um0Gb4TqhwaDDU4gEFRJZwWLWBrM+m1azuhGMJxJZmp0M5lWk3aLLIuqwE5NhOyrUbYzAZYDNrrYCCSgDccRziuwJRs4KDXCaiqNiBpWbETiwpGv05ypmBydlN/NIFQVGtEptWrIBhTkg0VEogmtGu6iqoN7BoMwWv1CqhSW5c/mkD8jNeudwaG6eBO5ijc/iicFiNy7CaUZVtwyYJcrF+Qi9VlWRPOe8QSKrp9ERxp9aCq1YMO7zv73XBMGTo26fFHh51jEQLIthrhsBhgNxlgNugQS+auVFUCQmu+pEo5NJhxIBgbcez7q9suwXUriyZUM80aDDgnuzfXAWgCsEhKqZ5xnxNAJ7QnqlBKOamrzAw4z12jBZzHq8hlxofWlOL6lcW4oMw1oYvrgxflBrscTQVVlQjGtAOoUDLEACSDlTrtRdds0EbL202GYaOQB0JxtA2E0JacorzLG0aPP4r+YAyekPbmvCTLguIsCypybKjMt2FBvh05NhNMeh3MRt2YnaRGI5MHa+2eME53+nG0zYPjHT4Eo4mh6c27fJyiJd0uW5iL5cUuROLaAdvJDu+0C/MAwHUrirB2XjaeP9Q2KzvhmPQ6beSyUesuYE12BXWYDTDqdfCEY+gPxjAQiieDx+8EnJ1mg3byL8uCYpcZLosWRrabtZOAUmpdhAe7gAYiCfSHYhgIxtA6ML2CwmXZVsSVzHUKIJpNrEY9Yoo65ol4Sg+TXoerl+Zjw5ICVHf7sbvOjaa+UKbLmlPKsq248cISLCywozjLCotBh111brx5ugenOn1wJU9Smgy6obD7bJgWMM9uwsXzc7CmIhsXlGVhfq4Nf6rqwK/3No/ardNs0MFsGDsAuaTQgRsuKMa6+TlYU56NbKsRDe4ADrV4cKLdi8a+EJrcQeTYTdi4tADXLCvAgjw7XBYDdEJgT0Mfnj/YhlePd501kCgEztpJXAigMs+OVWVZWF3mQpHLMjQlaiSuwGzUwZJ8f+MwG2E3a6FPndBOqEog2XFS6y5rNxuQZdU6vTqTF3z6gjF0eiKoavNgT30fDjRrg1uykgO8wjEFgWgC3lA8LZ1Vafa4tDIXX71uKcLxBA42D+Bkhw8FTjPm59lhNujwp6OdE+rAP9tYjLqhGYVsJj3cgSjcgRh6fJG0hLHHku8wDQUaDTqBut4A6nq0DmZn7o/MBh2Wl7hgN2n7FUWVaE2eM9ELgYsqsnFpZS6EAKravDja5oHnXQGQilwrLqrIwelOH2pncKdyoskSArAZ9bCatAHT2sDC6ftaajLo8J7KXKwqzcKiAjvKc2zo8oXR0KvNFuWPaEGt/lAMLf2hSQWsiIhofJxmA7LtRjjMxqGmF4PX3GKKFgSVEslBfwICWhhDkRI9vuiI1xuDTqAsx4p5uTYsLnTgwvIsrC7LhtmgQ12Pdjy4p6EPu+rcU97oJdVsJj3yHdpA8KIsC5YXObGqzIVFBQ7YTFr31IFgDDtq3dhe04td9e6Mz5JJ52dRgR3/lmxoVNMdwJ2/PYhG9+y7jnYmXTKExc69I+U7zHCY9UOhQbNRD5tRD6NBh0hMQTCWgKJKuKxax32TQSAa10KIvkh8qNt3eY4VK0tdWFWahRXF2gDmvHcNGA7FEthd14dHdzVyQC1Rmuh1ApuWFeJvLinHe5cXDhuMr6oSPf4omvqCaOjVZgDVBu9rg+/fPchFCKDEZUFFrnZ+riLHBr0OaHSH0NSnDXIaPAc+U+OIC/JsuHZFEa5ZWoBLK3NhMWrHkSc7fNhd78a2ml7sb+qfNgOAzAYdVpdlYd38HCwqsKPIZUGh04L+YCzZ8CGAdk8EXb4wun1R+MLxjB+rMuA8pzHgLIS4A8DDAB6SUn5+lPsHuztfJ6V8c5LbYMB5jjqfgPOZdAJYUuhEeY5VG5Vj0CEUU9AX1ELAoZiinVxJjkY6802VSa+DxaiNtNambBIIx5ShkKIQ70xFPvi9Ndmt1KDTwReJYyCkbWPwYr2iSsSSXQgnuzsYnJJSAikJQFmNehS5zO9MDZucBl076SQRS45yiita172zhR2IiIiIiGh6sBr14z52NyfD49OdxaiDKsFwEhERERERERHNeJfMz8HJTt+MmMGPZqZcuwnFLq0ZWjShYH/jwLSa1Yxotsuzm7B+QS76Q1oDkA5PeEach88Us0GHVaUu1PUEprS5wmzHgPOcNqmA8/SZozU1liU/14xxfy20gPNSAGcNOAshxkowL59caTTTXViejVQOB5CANl1Q8oB9cGqJdHJYDCjNtqZ1G0RERERERERERERERERENPNctjAv0yXQHGE26HHVkvxMl0E058QUFQ6zNgt0Zb490+XMCJcsyM10CbNKniO92TiafWZbwDkr+dk7xv2Dt2dPQS00y3z5uiWZLoGIiIiIiIiIiIiIiIiIiIiIiIiIaNabbQHnlJFSXjza7cnOzuumuBwiIiIiIiIiIiIiIiIiIiIiIiIiIqI5QZfpAlJssENz1hj3D97umYJaiIiIiIiIiIiIiIiIiIiIiIiIiIiIaIJmWwfn6uTnpWPcvyT5ueY8tnH60KFDpwH83Xmsg4iI5oBk1/8xZwUgIiKaqfgaR0REsxVf44iIaLbiaxwREc1GfH0jIqLZiq9xRJrZFnDekvz8PiGETkqpDt4hhHACuBJACMDeyW5ASslgMxERERERERERERERERERERERERERUZroMl1AKkkp6wG8DmABgC++6+7vArAD+I2UMjjFpREREREREREREREREREREREREREREdE4zLYOzgDwjwB2A7hPCHEtgFMA3gNgE4AaAHdnsDYiIiIiIiIiIiIiIiIiIiIiIiIiIiI6i1nVwRkY6uJ8CYDHoQWb/wXAIgD3ArhMStmXueqIiIiIiIiIiIiIiIiIiIiIiIiIiIjobGZjB2dIKVsBfCbTdRAREREREREREREREREREREREREREdHEzLoOzkRERERERERERERERERERERERERERDRzCSllpmsgIiIiIiIiIiIiIiIiIiIiIiIiIiIiAsAOzkRERERERERERERERERERERERERERDSNMOBMRERERERERERERERERERERERERERE0wYDzkRERERERERERERERERERERERERERDRtMOBMRERERERERERERERERERERERERERE0wYDzkRERERERERERERERERERERERERERDRtMOBMRERERERERERERERERERERERERERE0wYDzkRERERERERERERERERERERERERERDRtMOBMRERERERERERERERERERERERERERE0wYDzkRERERERERERERERERERERERERERDRtMOBMRERERERERERERERERERERERERERE0wYDzkRERERERERERERERERERERERERERDRtMOBMRERERERERERERERERERERERERERE0wYDzkRERERERERERERERERERERERERERDRtMOBMRERERERERERERERERERERERERERE0wYDzkRERERERERERERERERERERERERERDRtMOBMRERERERERERERERERERERERERERE0wYDzkRERERERERERERERERERERERERERDRtMOBMRERERERERERERERERERERERERERE0wYDzkRERERERERERERERERERERERERERDRtMOBMRERERERERERERERERERERERERERE0wYDzkRERERERERERERERERERERERERERDRtMOBMRERERERERERERERERERERERERERE0wYDzkRERERERERERERERERERERERERERDRtMOBMREREREREREREREQjCCGahBBSCLFxEo99PPnY74xyn0x+LDj/Ksddz5RvMx2EEOuFEH8SQriFEOpYzzEREREREREREdFMx4AzERERERERERHRLCeEsAkhvpAMxbUIIUJCiKAQolEI8ZwQ4pNCCOsoj2sab3jubMsKIbYm73t8lPsePyN4eOaHTwhxRAjxv0KI8kn8zAvOWNfG81lWCHGbEOIBIcTbQog2IURECBEQQpwQQtwnhFhylnWP+bOfscyS5O9FJp/HhcnbhRDi6uRzsEcI0S+EiAsheoQQbwghPi2EGPUc77t+pvF8zD/bc0QTJ4T4iBDiO5MJB9PUEUJclPw9fTrTtZxLcl+zFcCNAHIAuAF0AwhksCwiIiIiIiIiIqK0MGS6ACIiIiIiIiIiIkofIcRNAB4CUHzGzUEAKoAFyY+PAfihEOJTUsq3prrGpDiA/uTXAkABgDXJjzuEEDdJKXdmqLaHAJiTX6sAvACyAKxMfnxOCPEZKeWTE12xEGIFgDcBlACoA3CtlLIlefe/AfjPMxZXoAUZCwBcl/z4eyHEjVJK37tWrUALPp5NDgBTcrn2idZO5/QRALcnv96awToypRNANbQQ7nR2EYBvA9gG4PHMlnJOnwNgA7ADwIeklJ4M10NERERERERERJQ27OBMREREREREREQ0SyU7kr4ALdxcDeBTAPKllA4ppQtANoC/hha+LAVwdWYqBQDsllIWJz+KADgA3AbAA63OZ0frMj1FHgTwCWhhcLOUMhda4HkDgL3Jrx8TQiyeyEqFEBdCC1WWADgF4Jozws0AYIQW+r4HwOUALFLKbAB5AL4LLcS8AcCv3r1uKWXrGc/niA8AFQAGQ9G/k1ImJlI70blIKf9VSrlcSvnzTNcyi6xKfn6G4WYiIiIiIiIiIprtGHAmIiIiIiIiIiKahYQQawD8Eto5wFcArJVS/lZK2Te4jJTSK6V8Xkq5CcDHAfgzU+1IUsqQlPI3AL6UvKkYWkfcTNTyZSnlk1LK5sEgsJQykewofQO0rspmALeOd51CiIsBbIHWjfkogI1Syo53LfZHAJVSyq9JKfeese1+KeV3AHw/udzfCCHmT/DH+iCA/OTXT0zwsUSUGYODPAIZrYKIiIiIiIiIiGgKMOBMREREREREREQ0O/0ntNBtO4BPSCnDZ1tYSvk0gJ9ORWET9AwANfn1xZksZDRSSi+A2uS3peN5jBDicgBvAsgFcBDAJillzyjrrpJS+t59+xkeP+PriT43tyc/H5ZSHh2lRqcQ4tNCiGeEEMeFEB4hRFgIUSeEeEgIseRcGxBCXCaE+LUQokkIERFCuIUQh4QQ/y2EWDaRYoUQW4UQMlmTVQjxHSFEdbKmHiHEU+eqSQhRkNz2MSFEQAgRTP5sPxBC5I7xGJn8WDDG/QsGlznjto3J7wef42+fsZ5RlxVCNCW//yshxKvJn0kVQnzlXdu7WQjxmhCiVwgRFUK0CSF+J4RYN576hBBXCiFeTj4+JIQ4IoT4JyHEOa8VCCFyhRA/FUI0JrfdLoR4WAhRMsbyjye3/Z1zrXu8hBD54v+zd99hklzlocbfr7snz85s1q52tbvSooASyoAAJZAQUQRhshEYuFyTo7GNjcHga5KNAdtgGyNjkwwGg0kGGQswIllkbIJAAklIQlpt3p3Ufe4fVdXTM9s9eXbCvr/n6aemq+pUfRVPddVXZyJ+OyI+HhE/ioi9+Xb8nzy2KR2DDdNLwHvyrxeO304RcdE0pnVVXuba/PuTI+KLEbEj7/+ocePfP99vb8nX546IuCYinhgRMW7cm/JYi3je0xDjTdNZZkmSJEmSJGmpqCx0AJIkSZIkSZLmVkRsImuhF+BteRLupFJKafKxDq+U0mBE3AWsB/oah+WJhBcCX0wpXXT4o4OIWAOckH+9cQrjXwh8EugFvgo8ZKrbp4kdDX+Xp1ooItYyun+0ar35acDb87+rwG6yBjO2558nRcSjUkrXNJl+AH8KvKKh9x6gHTgz/2wErmoos43R9ff0lNLVLeLqA76ST2OQLPl9HfB44NKIOC+l9LMmMd0f+DhZUjnAUF72lPzz1Ii4NKX04xbznY4h4A6gH+gE9jOFFncj4qXAm4FEtr5rDcNKZIm4v5n3qpK1uL4JeBLwhIh4XkrpryeY/mOBD5I9F9gFtAH3ItvOD4yIxxWthDexmSyhfitwII/xaOCZwIMi4qyU0s7JlnEOvBJ4af73CNl+1Q/cM/88JSIe1Cxpv4U7yFpF7gOGgbvHDR+aSZAR8Tbg+WTbcMy2zIe/gUOPj1XAA/PPIyPiySmlotydZPvSarLttgc42DBMkiRJkiRJWnZswVmSJEmSJElafi4CihZAP7GAccxaRHSRJbBClpS54CKzPiIeBnwO6CFLNm2VLFyUuxT4NFly8xeBy2aR3AxZcnfhB9Mo90SyJMlh4P0txrkLeD1wHtCdUlpDlmB5T+B9ZMv8/ojoaVL2ZYwmb/4VsC2l1J9S6iNLin0Oo61eT9dryBJBL89j6AUuAG4hS/78f+MLRMRW4N/y4X8NHE+W1NoDnEa2DY8BPhoRU04UbyWldF1KaQPwobzXm1NKGxo/TYodBbyBbH1tTCmtypftI/nwV5AlNyfgD4BV+TibgQ+T3et/R0RcMEFo7wauAY7Ly67Mp1sDHsXYhNvx3g7sBM5PKRXr/QqyY3Ib8LsTlJ1LvwR+Dzgd6Mr3yw7gHODfyc4V7x/fAnIr+bZ4Yf71uvHbKaV03QxiPBt4HvBqYE1KaTXZPnsdQES8kGxd3wE8G1iZUuon2x+fANyed3+nIc5z81iLeF7YEOO5M4hRkiRJkiRJWvRswVmSJEmSJElafu6ZdweBuWiR9mUR8ZxJxlk3yfCZ+i1Gk7W/PsNpfDQiJmqJdUpJrRHxKuCPmwy6EXhiSumOCYqfR5a02EGWUPuolNLBCcafLJYSWbIvwNdSSv87jeJX5d1Pp5Satv6aUvpgk34J+FFEPJUsIfdBwJU0JHbnrUP/Uf71/6WUfm/cNG4D3jWNWMfrAC5NKd3Q0O/LEfEismTgR0ZEe0qpcXu/niyZ909TSuMTcX8QEY8AvkmWNPtoRpOKD6dO4AMppecWPVJKA8AtEdHLaALxG1JKr2sY59aIeCJZi9j3B15HlvDdzC1k+91gXnY/8KY8Sf3VwO9ExFtTSgealB0EHpRS2pGXHQE+ERGvI2t1+komTpCeEymltzXpVwWuj4grgG+Rtch9AdlLBAuhl2zff23RI6W0B9gTESvJttEA8OCU0ncbxjkIfCgifknWSvnLI+It4/ZlSZIkSZIk6YhhC86SJEmSJEnS8rMm7+7Mk1Jnq4csoXWiz5zda8xbSN4WES8D3pj3/gVZK7x1KaWLUkqRUrpokkmumiT2tVMMbR9Zq6t3NfS7CXhBSmmy5Ot7kiXnDgH/ZzbJzbk/JmspdoTRFmgnFRGnAmflXydscbqVfJ/6VP71fuMGXwl0k7X22ywZvNU0b8q3ZaSUrp5g1I+MS24ufIKsdeMO4B5Fz4joBh5H1krxn7WY9xCjSc2XTjXmefCmFv0vBfrI9p03jh+YJ/gW6/oBEdGshWiAtxTJzeP8GVnCbR9wWYuyf1MkN4/zr3n32BateR82+bJ9Pv86fr88nKq02NeAx5IlQF/TmNzcKKX0VbKXJlaRHeOSJEmSJEnSEckEZ0mSJEmSJEmTeU1D8mnTD1kC8mxcGBEpIhJZMuqNZAmfXcBtZC3PzrQl04snif3YqUwkpfTWlNKGlNI6sqTvhwJ7gX+LiA9ERNsExb8PHATagU/mLR3PSN5ib9Gi7++mlL4xjeJPy7s7GE1SbjWfzRHxhoi4PiJ2RUS1YRv9eT7a0eOK3Sfv/uccJHE3881mPVNKw8Cv86+rGgadTbbOA/h+RNze7AO8LB//mHmIeSoOAk0TXhlNSP9uSmlni3G+RJZY2zj+eNc265m3LvztSco2Xe/ArQ1/r2wxzpyKiJMi4h0R8b2I2BMRtYb9skj2H79fHk43pJTuajHs/Lx7Sat9Md8fi/1wofZHSZIkSZIkacFVFjoASZIkSZIkSXOuaGl1VUTEHLXiPN+GgbvzvxOwH/g5WYusfzdBYueCSCkdAD4TEf8FfA94AvANRhN/x/sW8FKyVqhPAa6JiIunu1wR8TCylpcDeFtK6c3TKFsGnpx/ff9ECeMRcSHwSbLWZgu7yVr6hSzxvI8s0bvRUXn3l1ONa5r2TjCsiK0x0Xxj3g1GY5tI90yCmgM7Ukq1FsPW5d1bWwwnpfcPOjAAACAASURBVDQQEXeRLeO6FqO1LN8wrFXZpus9n2/xdaIE/zkREU8A3tswrxrZflm0TN1Ltk/OaWvSedJxMy9MKX1oXL87J5hUsT92M7V9baH2R0mSJEmSJGnB2YKzJEmSJEmStPz8b97tAE5cyECm4bq8deQNKaWNKaV7pJQuSym9abElNzdKKe0lSzgGeMYk434eeCwwBNwL+PeI6JvqvCLigcBHyJI73wO8aJrhXsZoguU/tBopb4n6n8iSRa8BLgC6Ukori20EvKQYfZoxHG7FPfDdk7VCnn8uWqA4q5OPQue8R7GIRcQ64G/J9v8PAecAnSmlVQ37ZfGCwVzvl0e1+HQ1GXeibVnsj38xxf3x6rlcCEmSJEmSJGkpMcFZkiRJkiRJWn6+SNYKMsAjFzKQI0TR+u32yUZMKX2KrLXnEeBc4NMRMWlrsxFxf+ATZEmu/ww8awYtc1+Vd3+QUrp+gvHuC2wma1H7ipTSl1NKA+PGadUa8h15d+s0Y5svRTx9EdE/g/JFsmqr5OKZTHO6ihaBt7QaISI6gTXjxh/v6AnmUQybqPXhhfYQsqT7/wGelFK6PqU0PG6cqbTSPW1zmIBc7I8tt6UkSZIkSZKkjAnOkiRJkiRJ0jKTUroF+HT+9flTbSU4IhZ7a7yL1bF5d99URk4pfQx4Klny7P2AT0ZEs5ZgAYiI84BPAd3AvwFPSSlNpcXfxmmsZDTZvWXrzbnNefcnKaUDLcZ5UIv+X8u7F020TIfRf5Mlkwdw+QzK78q7m1sMP3eCsrW8O9vj6lt59/iI2NRinAuAyrjxx7uwWc+IWAGcNUnZxaDYBt9LKdXGD8zPX5fMYLpztZ2m4qt5d7EcH5IkSZIkSdKiZYKzJEmSJEmStDy9ChgkSwp8f97Ca0sR8RvASw5HYEtJRFQmGb4WeHr+9ctTnW5K6YPAM8ha2r4I+NeI6Ggy/XsBnwX6gM8Dj2vSau1UPJ6sFeIq8L5Jxt2dd49vtt9ExGXAxS3KfgQ4CKwC/nAGcc6plNJe4F/yr6/Nk3mbiohKRPSO6/39vHtFk/E7gBdNMPs9eXflFMNt5XP5tNqAlzeJowz8Qf71yyml21tM56UR0d6k/4vI9o09+bwWq2K/PLXFyxjPYgqtqDcxV9tpKj4M7GcKx0dErDoM8UiSJEmSJEmLlgnOkiRJkiRJ0jKUUvoO8FyyBNqHAd+OiKdExOpinIjoj4jHRMR/Ah8CWiZ/LkYRcW1EpIi4dh5n88qIuDoiHtiY/BoRPRHxGOA6YANZK8F/Mp0Jp5TeCzyHbBtdBnw4Itoa5nEiWcLpKuCLwBUppcEZLsfT8u7nUkq3TTLuV4ADwBrgvRGxMY+nKyKeQZYwvKPFMt0FvCb/+sqIeEdEbGlYpo0R8ZKIGJPcGRHb8m2ZIuKq6S7cJF4J3A2cAFwXEZcX6zkyx0fES4AfAeeMK/vPefdZEfH0Igk9Ik4hayX96Anm+8O8e3mxDmcipbSf0X3rBRHx+8W+mLfo/AHg/mQtEb9qgkltAT4WEdvyst0R8VLgj/Lhb5igxe7F4BqyY+VU4G15q+RERF9EvBz4S1rsl5MottPJEXHvOYm0hZTSDuB386+vjIi/jYgTiuH5MfaAiPhrsnOLJEmSJEmSdMQywVmSJEmSJElaplJK7wYeA/waOAn4R2BHROyNiD3ALrJk1YuAXwBfWKBQF7MKWXLwNcCeiNgVETvIWn39F+D4/O/Hp5Sun+7EU0p/A7ww//oIsta2y/n33wHW53+fDtwYEbe3+Lys1TzyBMr75l+vnkJMuxhNwnwc8KuI2EW2nO8GbmA0ibmZNwJvzf9+LvCLfL3tBn4FvAU4brI45kpK6Sbg8nzepwKfAfZHxF3AAPCTPKbtZAm0jf4O+DrQAfw9sC9fjh8AZzDaenczH2M0sfqWiLgtIm6KiJtmsBhvBt4LBPA6YFdE3A3cTLaNasDzU0pfmmAav0WWSH9jROwkaxH5zWTPCT5Ott0WrZTSjxndr54H7MyXYydZ7P8BvHMG0/0p8CWyY/1rEbGj2E4RcZ+5iX7M/N5O1uJ2Ap4J/Dgi9uXbc18ey3PIWtWWJEmSJEmSjlgmOEuSJEmSJEnLWErpX8mSSZ9L1uLsLWSJfBXgJuAjwJOAEydJjjxS/T3wIuATZIm9AfSRJa7+F1mi4okppY/OdAZ5wuPL869XkrWaXGLs/dtVwFETfHpp7Tfz7q58OaYS09vIkuOL1pwrZC0cvxo4H9g7QdmUUnoxcAFZy+C3Al3AIPAtstaIXz+VOOZKSumbZEn+v0PWMu4+YCXZsv038DbgwpTSF8eVGwYuBd5EdrzUgP1kieJnA9+dYJ53ARcDHwXuBNYBW/PPdOOvppSeRrZ/fI5sW/YCt5G14HxeSumvJpnGv+TxfAqokrU6/l3g+cBjUkoj043rcEspvQR4NvBtsv2pnP/9IrKW6me6DI8B/gq4kWy9FttpXpKMU0qvA+4F/A3wU7JjvYdse/478ArgAfMxb0mSJEmSJGmpiJTGN0ghSZIkSZIkSZKWuojYRpa0S0opFjQYSZIkSZIkSZoGW3CWJEmSJEmSJEmSJEmSJEmStGiY4CxJkiRJkiRJkiRJkiRJkiRp0TDBWZIkSZIkSZIkSZIkSZIkSdKiYYKzJEmSJEmSJEmSJEmSJEmSpEUjUkoLHYMkSZIkSZIkSZIkSZIkSZIkAbbgLEmSJEmSJEmSJEmSJEmSJGkRMcFZkiRJkiRJkiRJkiRJkiRJ0qKxLBOcI2JzRPx9RPwqIgYj4qaIeGtErFro2CRJkiRJkiRJkiRJkiRJkiS1FimlhY5hTkXEduA6YD3wceBHwHnAxcCPgfullHYsXISSJEmSJEmSJEmSJEmSJEmSWlmOLTj/FVly8wtSSo9KKb0ypXQJ8OfAicDrZzPxiHhfRLxvDuKUJEmSJEmSJEmSJEmSJEmSNM6yasE5b735BuAmYHtKqdYwbAVwGxDA+pTS/hnO4/qzzjrrrOuvv34OIpYkSZIkSZIkSZIkSZIkSZKWrZhJoeXWgvPFefdzjcnNACmlvcBXgG7gPoc7MEmSJEmSJEmSJEmSJEmSJEmTqyx0AHPsxLz7kxbDfwpcBpwA/MdEE4qIVk00nzSz0CRJkiRJkiRJkiRJkiRJkiRNZrklOPfn3d0thhf9Vx6GWLTM/OzOfdy9f4jhao0gaK+U6KiUaK+UKEVQLgWVUtBWLlEujW1RvVwaHV6KIAIiIKXm84qAaGiVPftOQ9kgNRSOaN2Ce2oxk6LM+OHjpzV+PiklUoIWodejHj88gFIpmsYzlek2Tnts2SLOrGwtn049fhLVWmKklqhWEylfpmKUrFxWppaycYu/s3mObq9SZNuwXAoq5RJt5aj3q5RLDI3UGKnVqNYStZSvhIBSjF3ftRr18QDaKyXayiU628oMV2scHKoyUkuklKg2LE8CarU8RrLpNu57RUztlRLt5ayB/lq+LMV6qTVuz3zZmq3TYv+r1hLDtRrVaqrve8X+WGy7aChTrKvi71KTGRTLVawHyI6RZuMW/evziKDWsPzVWmK4murrs1rLpk3Klr3Y0kHUj8Nxh2d9eyWycsUaahwt5ftK4z5SbVivjaFnqzjb+MU2alwnbQ37Ti1fF/V1R+P5olRfh437ULEuivVfrA/ydTIaQ5PtOoNzRTG91GK8Yt1kf6eW57V6DIzdhxrXbXGcjtRqjOTHbbFcxTZsKwdB1MfJtl8231I+vFIqjZl+KbJhAdRq2bYr9pmR6mjApYado9gWxTHWqNgeY7Z7DYZrNUbG7Y/Z+KPHQxFHs/Uy5ntAOZ93qaH+GLtNUv0Yz9Zbqpdv1Hj8F8ucrd8s3uFqjYjROqyonxLZcVp0B0aqDAxXGRyu5eeVbA1n+2hk56/hKgPDxXGdLdeegWF2HRhmz8Awg8M1hqrZp1Yrtht0Vsp0tpdpz+vQWkrcvntgwv1pMYmArvYyfZ1t9He10dfVRkqJwZEa+wdHuGXnQYZGapNPqMl0V/e0c1RfJ5X8vJESDI5UOThcZaSaKJeivs2Gq9k2rTXUcx2VEl1tZSrlEnsODrPrwFB9GzUqlaBcKlHOj5cVHRX6u9pY0dlGe6VEJT/29g+OsHdgmAN5XTVcrTFcrTE0km3XIKiUR89hxfVLR1t23VQpl+p1GUBbXgdWSqPn+uIcmxJ0tpXp6SjTWSlTys/hxTmyWCeFcqk4x2b7UeP5sn6dUJzz8/NvY71fS9nxXcnjKc6/pVJ2ri3qrmL9ppTqx/foNBIRUT9+28rZtWKlFGPWV0rU6/NGxTVKW6lER1tWnzdOv5hvteFaIiWyeOvLPHqezZZ/9HuC+nmgiCX7pLxOrdWPf8iuUTor5fr1brE+i2urYp8byrvV/NxcKZfozLftnoFh7t4/xO6Dw/XrlXJEvc6olILujgrd7eX6vNrKJUZqWVxDIzWGRqoMV7NrkkJ7uURPR4WejgrdbeX6tXmlHESM7hvZOTBbZ9X83Jfts9nFWqVUoi3fRll8kR8LMWZ/rObXZvnlHZ1tZTrbSpRLo/8UqVqrMTiSfUYa1unOA0Ps2DeUHX8jNQaHqwyO1OrXMEMjtTk/3xXnpe72Ml1tZXo6Kqzp7WBdbwere9ppK4+e84v9gXz/Ka4ti3qwiH/3weF6/VRqGKdYz431XqkUdFZKdLdX6Gov0V4u01bJjo3B/HwxPFKrX7+XI2jLr2Gz8lkdMjhS5eBQNa9fsu7QSK2+jRuvtUaXffSaqRSRnQfby3S2levXCUVdU8RcrLOi3i+NO3eM/32RTX/sNVrKzyPFcVUcI8U1QRqdQLb8xWckO4YGhqvsGRhh38AIB4er9f2kMcbZ7A/9XVkdddvugQnrpAjo7aiworMtu95OiVot1fftoWotv2ZOdFbKHNXfyYa+TrrayvVr22L9RIzWQ51t5UOumYtrlMZzarl06LVXEUNx/i7O50P5+evg0Ai7Dgyz++AwB4er9fPawHB1yVxLQFYXr1/RyeZVXRy9sguAg0PZflD8jkvA8Ei27+wdGOFXuw5yYKhan0axbzcudwR0t2e3/1JDXZIdfzBSnftz0GTqvxvz33qNdW4pr0dLpaA9P1dVyiVSGr3e7W4v05VfOxaKuimrl7Jr1sGR7Pr04FB1To6lxvi72st0VMp0VEoMV2vsOjhMtTr9eRTHXAL2DYzMWYzSclAuBetWdLBpZRftldH6+659Q9y5d5DdB4eoTf9n1pyr13ftZbrbK3k9WqGzrVy/d9x4vVD8dhoYzn4v7h/K6v/dB4fH3KOQZqu4Bjyqr5Mtq7vZuqabTau6WNHZRm9HhYND1fp933I56OtsY+/AMNVayq+3s+nU8nufve0V9g+NMDhSo7ejQrkUjFQT7ZUS+4dG6G2v8NNf7+OGX+9j/9BI/d5ab35/oaejwsBwlQND2bV98Ts8IK/vg+72Cj0dZXrz33q9HRU6KiUODFXZeWCIXQeGuXPfIHfuHWT/4Eh+TVBhbU87x6zuZvOqLgZHarSXSxwcrrJnYJid+7N7UimN3tMrl7JuV1uZtb0drFvRwdBIjUop+72yf2gkOz4HqyRGf/sPDNc4MJT133Ug+420f7Bavx+wfzCL8+BQlfZKifUrOljf10FPR6V+TdxRKdFRKdfPaSO1xL6BEe7YM8Adewb49d5BDjZc3x0unW2l+rX9UX2ddLeXxzxnKPaF4l5srbgnQnYfpautTFd7qX4/qFEQ+X2n7DqPcc8NGu8tFz+6i98TKY3+1iruIxTXs/Xpx+id1WjYb2sN9y6G8t9W5dLofau28ug9ivExj78+rt+Hza8pi+vz4h5VKUafxRTraMxv5OL+cJN7w8W9ncb4R5dttHwxj+L30eh9IPJlGPsbdew2GFVrWJ+lhnVX/Pat/3qt/3bN1mGxzKWI7F5uW3adDoc+c2u8/z92PTaPaTS20Xs3xfOjYl+r3++LseMXzxYbf1803m+vx9KwLltpHNK4nzVbp6O/rbJ11jg/Gvo33nesTfCbJPstU6v/pinuixX3DsbOm/r+3N2e3fNpK4/9p92Nz65K+Xlv9J7H2GcRRf9osY4a77E2xtC4LzU+WBv/7HGi35uNx33xW7Bx+xXPeIu6aPzy1Y+52ug+XQwv7ru1ul4s5eukllL93kzj/lesnyB/ppQ/V4lx5YvzXv35YRY4iUR7OTs3dlTK9fuEtQRdbWW6O8q0lUrZ85Ixz5eL+x/ZflYpZ+fitkrU7zE17hvV2ugzmuIZ2ujz0EO3xei991S/l95eKdHZNlo/Fct+yDavpabn4HzTNz2+ijqj8Z4OUL//2PhcrljmZvfNx8eSreZUf6475tzTpB4q9u+x0xl9xjb+3l+rY7bxmG92n6s4RxfPLxufY44/3zfep2w8/orrlMZzdHF/urjnnJ0fR5c5kd3jHamO3gsZGK7W1/WYepfRc2cxrZF8P+rOf9NUa9l9tYH8/uRgfn++UBl3/in272J+5VLU+w1VU33/KJeoP9vpqJTp68quE7vaymP22VZ1WXF+KAVjzhulcRVPrchvaHgW0vjsprFebvYsP5tXjN2v49Djotj2Y86BLepAGkZrVn6iZ+xj65bWz/Qbj8dWxtdv4/MmGuNKY743P+6bnStSkzLN4jykf4t4Ww1vvMdZbLOZTLdVmXp9NP4CgbHbayq/4huvG5rOr9W+VZ/txHOZKDenmH+r9TNRjkg279F4Gp+NtJpf47V740Qaz9UAK7vb6KiUJ5y31Gi5JTjPmZTS2c365y07n3WYw9EisH1dL9vXLXQUoyZKVJzOeNMdPj6pb7paXljMerpZtzzhJdv8aq+UaKc0+YgTKJeyG51zpRyzXyddzPWFRTRUPtOfduOP3Uo5yK57jtyLn1KTXx4zOZYmOheMHbRwx5iWn1pt9OWJ8W6++wCf/587+OrPd7Cio8JR/Z2s6WnPkhnzhJLsRgnctW+IG369j5/csZfbdg1w175Bdh4YolIqZTcK8+TewXEJXeVS0NOePTgrl4sf5MHRKzu5x/pejl3bS19npZ6gV9xgaq+UWNFZoa+zjRWd2QO3Srn1+b9aS9x89wF+tetgPfGrmFfxA7B4qNJWjvrDrnUrOua0TigMjYwm1gVZ8vF8zEfS1AyN1Nixf7CeRLx3YJh9g1W62sqs7M4SAQ4MjrBnYISB4SqVctBezpJoVuTnofZyqX6Dqbu9wsrutkMebmlpGqkWCeFpzE3B4uZqKag/DBmp1TgwlCVzFg/NSgHr+zrp7ciuwGu1xG17Btixb5BKqUR7JepJmh1t5XqyynJQrSX2DY5wcKhar29rKbHzwBB37x/iJ7fv5bM/vJ1v3rRzTpNfpysCHnrqRl70oOM5/qgV0y6/c/8QgyM1VnRmiUBDIzV2HRxiz8Hh7OWGng7aKxOfD4arNfYNjNTPRQnqCS3Fw85SwJ6DI+ysJ/OMsH+oSpC9kLWyu42V3e3Zy16dlfrDrFqi/gJJsa+25S+jHE4Dw9X6C7EpUX/RY3Ckxq/3DHDHnkGGq7X6sVA8qOtqKzOUPxwcqdbo725jdXf7Idd+KSX2HBxhz8Aw+wazpKhyKbIXLCrlhhdjgoNDVfYOZolfG/o76wno+wZHuH33AD++fS/fvWUX/3vbHjat7OLUTf1sWtnFl356J5/9we3ctnvgsK47aaq628ucvrmfM7esYkNe9/R0VKg1JPUWL6wcGBrh5rsP8Mu7D7BvcCRP+iuzdU03p23q55Sj+9i6pmfC81etlrLk4PyY2zdYZf/gCHftG8ySBPcMAtn5LAE/v3MfP759L7+8+wATnfbLpaC7rZy/pJm9cNmTv/yzfkUH61d0cPTKLo5b28Nx63pZv6Kj6b2Zmdg/mJ1H9g5kL5YOV1PDi/6jCSvNGqioJ1nliTAd+YuDQyM1BkaydXPbrgFu2XWQO3YPsHcwm0+1luioZL8Jezsq9OUvRfV1Zsvc25klOAyOVKnVqJ/vV3SO3t1r/E1brSYGRrIXTYrkGcheEOzKX0wskj1SSvVEhpRGXwI7MJTFe2BohMinXYpgYLjKvsEqBxq2+0gt1c+zfV1Zvbe6p51yifoLT7fuOsgvdhzgtt0DlIN6klyR+NReLrG6t501Pe10t1fqL08Wv82z+qKav4SW3VcoXhLuzJPby6WARH3fKhIbihcDi21S/Pb+dZ7IundgpJ501mi4mq3zwTzptoipOFZWdFby/TF7IfqufYPctW+QlKA3v0+xtnfiewpd7eVD7vuu6GybcB9d0dlG49VKMfm+vNyJG1Zw4obpX89Mpni5dfOqycctlrkos7G/a0rzKM43XfmLVGt7O2Yc78BwlY6Glxuma+/AMHftGxrzYmKRcFckODUmmxbJ0kPFS4n5Z3Ckmp8TB9mxb5DhLNOAjrYSx67pYfv6Xras7mZjfydrZrG8kiRJkiTNp5gsG38piYg3AS8DXpZSekuT4e8Angv8dkrpr2c4j+vPOuuss66//vrZBStJkqR50+xt1ANDIxzIW9EpWoqVJElHtrv2DfLTO/bVWwlvK5eyZLb2Cr2dWSuclVKJ//nVHr57yy6+c/MufnDr7jlJMj3v2NW85pGncM+NfXOwJDoSpJT44a/28NWf7eDrN+7gaz+/m32Dh6/l57ZyjGm1SUeOFZ0VztqyinO3reKUo/vp726rt7RVvESypufQ5P/FaLha4+79Q9y1b5CB4Srt5ey3YU9Huf5fbCRJkiRJkiTNuRm9CbzcWnD+cd49ocXw4/PuTw5DLJIkSVogzVrJ6W6v1FvIkyRJAljb2zGlFvruf/xa7n/82vr3O/cO8u1f7uRrP7+br9+4g9t2D9T/XexkCafd7WVe+ZCTeOp9th72loy1tEUEp27q59RN/TzrguMYHKly3Q07+OwPbufj372VgeEW/395DvR2VPjti7fzxs/+ePKRteR0tZVZu6Kdtb0d9VZhN/R3cvz6Xk7a0Mcxq7uWzfmqrVziqL5OjurrXOhQJEmSJEmSJE1iubXgvB24AbgJ2J5SqjUMWwHcRpYJvj6ltH+G87AFZ0mSJEmSJDU1MFzl1l0HuW3XALfsPMDNOw9w9/4hNvZ3cczqLu597BqOXjm1f5cuTdXNdx/g1Z/4IV/40a/nZfrPuN+x/PbF27n3n/wH1dryuZ+8nLSVg56OCgEkYNeB4QnHLwVcevJRPPuC7Zy9ddVhiVGSJEmSJEnSEcsWnFNKP4uIzwGXAc8F3t4w+DVAD/CumSY3S5IkSZIkSRPpbCuzfV0v29f1LnQoOoIcs7qbv7/qXL7wozv40k/u4md37uOXdx+gWktUSkEtZS2PHxyuTnva5VLw9PttY21vB/c5bjVfuWHHPCyBJnPShhWkBAeGRyhFcHR/F0ev7OKEo3o5a+sqTtvUT2dbuT7+3oFhbvj1Pm7ZeZDVPe1s7O+kv6uNnQeG2bFvkKP6Otm2tmcBl0iSJEmSJEmSJrasWnCGeivO1wHrgY8D/wvcG7gY+AlwfkppxnfhbcFZkiRJkiRJ0lK0+8AwN+7Yz/W/2Mm3frGTG369jzv3DbLzwBCtbhM/7PSN/OWTzgLg/V//Jb/3se8fxoiPbBFw2clH8dyL78Hpm1cudDiSJEmSJEmSNFMzasF52SU4A0TEMcBrgcuBNcBtwMeA16SUds5y2iY4S5IkSZIkSVo2hqs1PvTNm3nL537MzgPDY4Z9/Ln3417HZMm1O/cPce7rr2GktvzuKS82l5y0npc/+ETuubFvoUORJEmSJEmSpNmaUYJzZa6jWAxSSjcDT1/oOCRJkiRJkiRpsWsrl3jKfbbyiNOP5m1f+Cn/fdPd3LrrIMet660nNwOs6mnnvtvX8OWf3rWA0S5vp2/u51UPO5nzjl290KFIkiRJkiRJ0oJalgnOkiRJkiRJkqTp6e9u4w8efnL9e7P//vfw0zea4DwPetrLvPSyE7nq/G2USjNqzESSJEmSJEmSlpXSQgcgSZIkSZIkSVp8Ig5NtL38lI20lU3AnUtb13TzuZdcyDPuf6zJzZIkSZIkSZKUM8FZkiRJkiRJkjQl/d1tfPg55/OCS+7BvTb30yQHWtP0uw+5J5tWdi10GJIkSZIkSZK0qFQWOgBJkiRJkiRJ0tJxxjErOeOYlbzkshP58e17+atrb+CT37uNai0tdGhLznnHrubyUzcsdBiSJEmSJEmStOjYgrMkSZIkSZIkaUZO3LCCv3jCmfznSy/i4advXOhwlpQI+IOHnbzQYUiSJEmSJEnSomSCsyRJkiRJkiRpVras6eYdTzqLjzznvpy6qW+hw1kSHnXGJk7b3L/QYUiSJEmSJEnSomSCsyRJkiRJkiRpTpyzbTUffPZ92bame6FDWdS62sq8/MEnLnQYkiRJkiRJkrRomeAsSZIkSZIkSZozvR0V3vbEM2krx0KHsmg99+LtHL2ya6HDkCRJkiRJkqRFywRnSZIkSZIkSdKcOn3zSlsobmHbmm6edcFxCx2GJEmSJEmSJC1qJjhLkiRJkiRJkubcsx5wHBecsG6hw1h0Xv2IU+iolBc6DEmSJEmSJEla1ExwliRJkiRJkiTNuYjgDY89jRUdlYUOZdF40D3Xc/FJ6xc6DEmSJEmSJEla9ExwliRJkiRJkiTNi439XbzyoSctdBiLwjlbV/H6R5+20GFIkiRJkiRJ0pJggrMkSZIkSZIkad486bwt3Oe41QsdxoycuWXlrKdRLgUvfODxfOj/3Jej+jrnICpJkiRJkiRJWv5McJYkSZIkSZIkzZuI4A2PPZ2utvJChzJtTzpvy6zKH7e2hw89+z68+NITKJdijqKSJEmSJEmSpOXPBGdJkiRJkiRJ0rzauqaH5168faHDmJZyKXjY6RtZ0VGZdtlSwLMvOI5Pv/ABnLNtabZeLUmSJTqEEQAAIABJREFUJEmSJEkLafp3ZiVJkiRJkiRJmqZnPuA4PvCNm7l118GFDmVKtq7ppru9whlbVvLln941pTJH93fyyDM2ceXZm7jH+hXzHKEkSZIkSZIkLV8mOEuSJEmSJEmS5l1nW5lXXH4iL/zgdxY6lCm554Y+AM7csmrSBOeV3W285pGn8Mh7HU1EHI7wJEmSJEmSJGlZKy10AJIkSZIkSZKkI8MVZ2zirC0rFzqMKTlpQ9YC82TxXnTiOj73ogu44oxNJjdLkiRJkiRJ0hwxwVmSJEmSJEmSdNj8wcNPZinkAZ+0cbQF51bxPvP+x3L1089jfV/nYYxMkiRJkiRJkpY/E5wlSZIkSZIkSYfNmVtWcdEJ6xY6jEkVLTj3d7Vx3NqeQ4Zfdf42XvXwkw93WJIkSZIkSZJ0RDDBWZIkSZIkSZJ0WD3lPlsXOoQJreiocMzq7vr3s7asGjP8Sffewh898pTDHZYkSZIkSZIkHTFMcJYkSZIkSZIkHVYXn7ieTSu7FjqMlk7MW28unLV1NMH5ghPW8borTj3cIUmSJEmSJEnSEcUEZ0mSJEmSJEnSYVUqBU+695aFDqOlkzaOS3DOW3A+ur+Ttz7+DEqlWIiwJEmSJEmSJOmIYYKzJEmSJEmSJOmwe/y5x9BeXpy3qE/a0Dfm+/Hre1nd0847nnwWq3vaFygqSZIkSZIkSTpyLM67x5IkSZIkSZKkZW1tbweXn7ph1tOJeWhM+Z4bxyY4l0rBu592Tr0lZ0mSJEmSJEnS/DLBWZIkSZIkSZK0IJ56362znsapR/eztnfuWlWOgJM2rDik/5kmN0uSJEmSJEnSYWOCsyRJkiRJkiRpQZy7bTWnb+6f1TS2re3hwhPWz1FEcMyqbno6KnM2PUmSJEmSJEnS9JngLEmSJEmSJElaMP/3wu2zKr9tTTeXnDR3Cc6nzTLhWpIkSZIkSZI0eyY4S5IkSZIkSZIWzINP2cBx63pmXH7rmh4ecMJaKqWYdSzlUvD8S+4x6+lIkiRJkiRJkmbHBGdJkiRJkiRJ0oIplYL/c8FxMy6/bU03fZ1tnLNt1axjedJ5WzhpQ9+spyNJkiRJkiRJmh0TnCVJkiRJkiRJC+rRZ25mQ1/njMpuWdMNwCUnrZ9VDCu723jpZSfMahqSJEmSJEmSpLlhgrMkSZIkSZIkaUG1V0o88wHHTrtcT3uZ9SuyxOjZJji/5NITWNndPqtpSJIkSZIkSZLmhgnOkiRJkiRJkqQF97Tzt3HesaunVWbLmp763/dYv4JjVnfNaN7Hre3hyffeOqOykiRJkiRJkqS5Z4KzJEmSJEmSJGnBtZVLvPMpZ7N51dSTlLet6R7z/ZITZ9aK85XnbKZcihmVlSRJkiRJkiTNPROcJUmSJEmSJEmLwuqedv7uaefQ016e0vhbG1pwBrjghHXTnmcp4NFnbpp2OUmSJEmSJEnS/DHBWZIkSZIkSZK0aJy0oY9XP/KUKY07vgXnE45aMe353Xf7Gjb2T73VaEmSJEmSJEnS/DPBWZIkSZIkSZK0qDzqjE2s7e2YdLwt4xKcN63sorNtere9H3Pm5mmNL0mSJEmSJEmafyY4S5IkSZIkSZIWlfZKiSeed8yk421b0zPme6kUh/SbSHd7mYectmHa8UmSJEmSJEmS5pcJzpIkSZIkSZKkRefJ995KpRQth3dUSmzs7zyk//b1vVOex+WnbKC7vTKj+CRJkiRJkiRJ88cEZ0mSJEmSJEnSorOhv5PLTjmq5fAtq7uJODQBevu6qSc4P+aszTOKTZIkSZIkSZI0v0xwliRJkiRJkiQtSr95320th21d09O0//Z1zfuPt7G/k/O3r5lJWJIkSZIkSZKkeWaCsyRJkiRJkiRpUbrPcWs4acOKpsO2rulu2n+qLTg/+sxNlEqHtgAtSZIkSZIkSVp4JjhLkiRJkiRJkhatZ19wXNP+2yZIcI4p5C1fefbm2YQlSZIkSZIkSZpHJjhLkiRJkiRJkhatR52xie3reg7pv3XNof0AutrLHN3fNeE0z9qykuOm2NKzJEmSJEmSJOnwM8FZkiRJkiRJkrRolUrBix50wiH9t7VIcAY4rklCdKMrzz5m1nFJkiRJkiRJkuaPCc6SJEmSJEmSpEXt4adv5KQNK+rff/ui7WxZ091y/O0TtM7c2Vbi4ffaOKfxSZIkSZIkSZLm1qJNcI6I4yPidyLiCxFxc0QMRcQdEfHxiLh4krJPi4hvRMS+iNgdEddGxMMPV+ySJEmSJEmSpLkTEbz40hOIgFc/4mRecflJE46/fX3rBOfLTt5AX2fbXIcoSZIkSZIkSZpDizbBGfhj4E+Bo4BPA28BvgI8DPhCRLygWaGIeDNwNbAR+Fvgn4DTgH+LiOfNf9iSJEmSJEmSpLn24FM28I/PuDdPv9+xk467fV1Py2G/cc4xcxmWJEmSJEmSJGkeVBY6gAl8FnhDSunbjT0j4kLg88CbIuLDKaXbGoadD7wU+BlwbkppZ97/TcD1wJsj4pMppZsO0zJIkiRJkiRJkubI/Y9fO6Xx7rGueQvOl5y0fsrTkCRJkiRJkiQtnEXbgnNK6erxyc15/y8C1wLtwPnjBj8n776+SG7Oy9wE/CXQATx9PuKVJEmSJEmSJC0O6/s6WdE5tn2P7vYyr73ilAWKSJIkSZIkSZI0HYs2wXkSw3l3ZFz/S/LuZ5uU+cy4cSRJkiRJkiRJy9Rx41pxfsmlJ7B5VfcCRSNJkiRJkiRJmo7K5KMsLhGxFXggcAD4UkP/HmATsC+ldFuToj/NuydMcT7Xtxh00tSjlSRJkiRJkiQthO3revjuzbsAOG1TP0+/37ELHJEkSZIkSZIkaaqWVIJzRHQA7wM6gFeklHY2DO7Pu7tbFC/6r5yn8CRJkiRJkiRJi8SVZ2/mXptXsrG/kzOOWUm5FAsdkiRJkiRJkiRpiuY1wTkibgK2TqPI+1JKT2kxrTLwj8D9gA8Bb551gBNIKZ3dIo7rgbPmc96SJEmSJEmSpNk5f/tazt++dqHDkCRJkiRJkiTNwHy34PwzYGAa4/+qWc88ufmfgMcB/ww8JaWUxo1WtNDcT3NF/13TiEeSJEmSJEmSJEmSJEmSJEnSYTSvCc4ppQfOdhoR0Qa8jyy5+f3Ab6aUqk3mtT8ibgU2RcTGlNJt40Y5Pu/+ZLYxSZIkSZIkSZIkSZIkSZIkSZof892C86xERDtZi81XAO8Fnp5Sqk1Q5AvAU4HLgfeMG/aQhnFm40ff+ta3fgQ8eZbTkSQtcxFxPUBK6eyFjkWSpLlkHSdJWq6s4yRJy5V1nCRpObJ+kyQtV9ZxUqa00AG0EhEdwMfIkpvfzeTJzQDvzLu/HxGrGqa1DXguMMihic/TklJ6ckrJ5GZJkiRJkiRJkiRJkiRJkiRpHizmFpzfCTwUuAu4FfjDiBg/zrUppWuLLyml6yLiz4CXAN+LiI8A7cDjgdXA81NKN81/6JIkSZIkSZIkSZIkSZIkSZJmYjEnOB+bd9cCfzjBeNc2fkkpvTQivk/WYvOzgRrwLeBNKaVPzkOckiRJkiRJkiRJkiRJkiRJkubIok1wTildNIuyVwNXz1UskiRJkiRJkiRJkiRJkiRJkg6P0kIHIEmSJEmSJEmSJEmSJEmSJEkFE5wlSZIkSZIkSZIkSZIkSZIkLRqRUlroGCRJkiRJkiRJkiRJkiRJkiQJsAVnSZIkSZIkSZIkSZIkSZIkSYuICc6SJEmSJEmSJEmSJEmSJEmSFg0TnCVJkiRJkiRJkiRJkiRJkiQtGiY4S5IkSZIkSZIkSZIkSZIkSVo0THCWJEmSJEmSJEmSJEmSJEmStGiY4CxJkiRJkiRJkiRJkiRJkiRp0TDBWZIkSZIkSZIkSZIkSZIkSdKiYYKzJOmIFBE3RURq8bl9BtN7YER8LCJuj4jBiPhVRPx7RDy0ybgrIuL1EfGjiBiIiJ35uA+cYPrHRsQ78zIHIuKOiPhqRDw7ItqnG68kafmaizouIq6aYBrFp9qi7PkR8emIuDsiDkbE9yLiRRFRnmB+D4+IayNid0Tsi4ivR8TTZroOJEnL01Kq4yLijIj4o4j4SkTcFhFDEXFrRHwgIs6a7bqQJC0fS6l+a1I2IuLzDfOoTHf5JUnL11Ks4yKiIyJeGhHfjIg9EbE/In4SEf8QEetmui4kScvLUqvjIqIvIn4vIr4TEbsiex73/Yj4Y+s3LXbeaJAkHcl2A29t0n/fdCYSEW8EXg7cAnwCuAtYB5wNXAR8umHcVcB/AScDPwTeCfQCVwDXRMQzU0rvHjf9c4H/BLqAzwIfB/qARwDvAh4bEZenlNJ04pYkLWuzreO+A7ymxbAHAJcAnxk/ICKuAP4FGAA+BNxNVl/9OXA/4HFNyjwPeDuwA/gnYAi4Erg6Ik5LKb1sijFLko4MS6WOeydwb+B64KN5fGcATwCujIjHp5Q+OsWYJUnL31Kp38Z7HnBxXr5zirFKko4sS6aOi4gNwOeA04CvAH8LVIEtwIOBNwF3TjFuSdLytyTquIjoB74BnAD8N/CefNAFwKuAqyLinJTSHVOMWzqswlwoSdKRKCJuAkgpbZvldJ4F/A3wD8CzU0pD44a3pZSGG77/BfACsgfcj08pjeT915NdTK4FTkgp3dJQ5lPAQ4GrUkr/0NC/h+xC9GTgwpTSl2azLJKk5WGu6rgJpv9V4D7AFSmlTzT07wNuAPqB+6WU/jvv3wl8Abgv8MSU0gcbymwDfgTsB85OKd2U918FfBPYDpyfUvrqfCyLJGlpWWJ13POBz6SUbhg3jyeTvdCzAzh6/G9ISdKRZynVb+OmeyLwbeBtZC/wbAXaivudkiQtpTouIkrAtWQvql6ZUvq3cfMKoJRSatqSpiTpyLLE6riXA28E3pNSesa4+VwNPA14dUrptfOxLNJslRY6AEmSlqqI6ABeD/ySJsnNAI3JzblH590/bLzZn1L6NfBnZK00P2NcmePy7icae6aU9gP/kX/134ZIkuZdRJxGdkPlVuBT4wZfSVYffbC4oQKQUhogewMc4P+OK/MMoAN4R5HcnJfZCfxJ/vU5cxW/JEmtzHUdl1J6+/jk5rz/+4CfAmvIWgWTJGnezMNvuGK6FeAfgZ8Dr57jsCVJmtQ81HGPImst88/HJzfnZZPJzZKkw2Ee6rgi3+SQ+o3RHBTzTbRoVRY6AEmSFlBHRDyF7F9L7Qe+B3xpGjcoLiW70HsrUIuIhwGnkv0rkG+0aG1yQ979eZNhRb8HAo1vx/0QOAl4GFlLXwBERDfZvyU5ANiypSSp0WzruFaenXff3WRal+TdzzYp9yWy+ur8iOhIKQ1Oocxnxo0jSRIsnTpuIsWLsLZwKUkqLLX67VXAmcB9U0qDWaOWkiQ1tVTquCfl3Q9ExFHAw4H1wO3A51JKt84yXknS8rNU6rgf5t2HAR8bV+bhefeaWcQrzSsTnCVJR7INZC2NNLoxIp6eUvriFMqfm3cHyP4d46mNAyPiS2T/xurOht53ARuBY4H/GTe94s25E8f1fxVwPnB1RPxGXq6P7GKzks/jV1OIV5J05JhtHXeIiOgCngJUgb9rMkpRf/1k/ICU0khE3AicQlbf/e8UytwWEfuBzRHRnVI6MJO4JUnLzlKp41rN6z7AyWQtsPxgJvFKkpalJVO/RcS5wO8Df9rYYpgkSS0slTqueOZ3HlnDRt0NxYYj4rUppdfNJF5J0rK1VOq4vwOeCPxW3jr0V/L+DyC7T/n7KaWPzyRe6XAoLXQAkiQtkPeQtZS8Aegh+9fA7wK2AZ+JiHtNYRrr8+7LgUR2AbgCOB34HHAB8OFxZYp/IfKaiCgXPSNiHfDi/OuqxgIppR+R3Vi5DngE8Dtk/1ZkA1mLzl+bQqySpCPHXNRxzfwGsBL4bErp5ibD+/Pu7hbli/4rZ1Cmv8VwSdKRZSnVcYeIiNXAe/OvL/bfG0uSckumfssftv8jWQtgr21WSJKkBkumjmP0md9fA1eTJYatBB4L7AT+OCKummG8kqTlZ8nUcSmlAbKWn99F9iLPi/PPOcCngX+dYazSYWGCsyTpiJRSek1K6QsppTtSSgdSSj9IKT0H+DOgC/ijKUymqEdHgEemlP4rpbQvpfR94NHALcCFEXHfhjJ/CNwMXAl8JyLeGhF/S/ZQ4O58nFrjTCLiTLLk5i5Gk6iPyaf1EuDrEWHilyQJmLM6rpniX2K9ay7ilCRpupZyHRcRPcDHgeOBN6aUxr8MK0k6Qi2x+u2NZAlfT0spDc/hdCVJy9ASq+OKZ37XpJSem1K6MaW0O6X0UeCZ+bDfncP5SZKWsKVUx0XEGuDfgUcBTwDW5p8nkOWffD0izpur+UlzzQRnSZLGemfevWAK4+7Ku9/+/+3de7xtY7nA8d+D7RJyiVKhrYtcS5Jok50QispBxXE9lIMinKOiUHRzclCKU0QukRQqJEKE46jokEsuG9uh2uSyyRae88f7TsYee8615lp7ydzW7/v5zM+75jve2xhzzs/Ye8xnPiMzpzQ3ZObjlH8kQvkVXKf+Pko25mMogcq7A+8FzgC2qs3+3GkfEfMAPwCWBDZrBFFPzcwvA1+nfEHeyf4sSVIvIznHzSQiVgbeQfnxznk9mg2XbblT/1Cjrt8+vX6JLkkSDOY5rjnHgpS7+awDHJGZ+490nZKkcWmgzm8RsR6wB3BoZl4/0jVJktQwUOe41t8/7tL+POBJYHkTDkmShjGI57ivAesBH83MMzLzgfo4A/gYsBDlx6zSQDLAWZKkmf2llgv20faWWnb9Eptyyyoov9B7Vv0V356ZOTEz583MV2Xmx4Fla5P/aTRfAXg9cFNm3t9ljktq+dY+1itJGt9Gco5r6/xi/PjMfLpHm855cfn2hvqDneUodz24o88+r6xrnVp/OCRJUi+DeI7rbF8YOJ/yJcJXM3PfUaxRkjQ+Ddr57S1AAIdERDYfwGtqm7/XutVGsWZJ0vgxaOe4Zp9ZvvOr8zxSny7Q3i5JUsMgnuPeV8tL2n0w3kRzAAOcJUma2Vq1nOWL6S4uBhJYKSK6nVNXqeWdfc69fS1Pa9TNV8slevRZspZP9jmHJGn8Gsk57lkRMT+wHfA0cPwQTX9Zy427bHsn8BLgysyc0WefTVptJEnqZRDPcdTMXhdSbvV4mJmbJUkjNGjntxvqeN0e02ubE+rzB0ayZknSuDNo5ziAi2q5SrtDRLyC8j3ddGDaSNYsSRp3BvEc14k5WXLWLsabaPAZ4CxJGnciYsV6i+B2/UTgG/XpKY36CRGxQkS8rtk+M+8CfkLJvLxXa6yNgPdQful9QaN+rohYqMvc21ECnK8Ezm5suqGOsWxE7NLqsyiwX316ce89liSNF2N1jmvZClgMOD8z7xmi3Q8pF/g/HBFrNOaYHzi0Pv1Wq893gRnAnnWNnT6LAZ+pT49FkjTuzWnnuHouu4jypcZBmXngEONLksapOen8lpkXZeYu3R48F9D8sVo31LySpHFgTjrHVScAjwN7RMRrG33mBg6vT8/MzKeGmFeSNA7Mgee4y2t5UDNxXz3HHVKfGm+igRWZ+UKvQZKkf6iIOBjYF/gVcBfwKPA64L3A/MB5wAcz88nafiIlC/NdmTmxNdbSlKDkZSj/6Psd5bYfH6Bkd/5wZp7VaL8Q8CfgF8DtwDPAJGBt4CZgg8z8v9YcO1ACwKIxx2LA5pRf1F0NTG5nC5MkjT9jeY5rjHk5sA6weWb+ZJj5P0C5uPIEcDrwIOV89cZav3W2/hMaER8HjqZ8IX4G5VfiWwJLA1/LzP2QJI17c9o5LiIuASZT/t93Snu86uzMvG6oeSVJL25z2vltiHGmAK8BJhj4JUmCOfMc1/g+bjrw49pnMrAacCswKTPN4CxJ49ycdo6LiFUpQc6LADfyXBbodwMrUQKm187M2/o+CNI/kAHOkqRxJyLWA3YD3gIsBSxIyZJ8HXAycHLrH3wTGeIfnBGxJPA5yj8aXwk8QvkH4pcy85pW2wmUTJTrUAK3AP4I/AA4MjMf77HmdwJ7UwKhl6Bku7wFOLP2e2JkR0GS9GL0PJzjVgT+AEwFJmbm032sYRJwAOWcNT9wGyUDytG9+kfEZpS7EqxOudPQH4BvZOZJfey2JGkcmNPOcY1Ar6HslJknDjevJOnFa047vw0xxhQMcJYkNcyp57iImAx8Cnh7XfPdwI+AL2bmQ8PuuCTpRW9OPMdFxHLA/sCGlDiVBO6h3I38y5l5b187L70ADHCWJEmSJEmSJEmSJEmSJEmSNDDmeqEXIEmSJEmSJEmSJEmSJEmSJEkdBjhLkiRJkiRJkiRJkiRJkiRJGhgGOEuSJEmSJEmSJEmSJEmSJEkaGAY4S5IkSZIkSZIkSZIkSZIkSRoYBjhLkiRJkiRJkiRJkiRJkiRJGhgGOEuSJEmSJEmSJEmSJEmSJEkaGAY4S5IkSZIkSZIkSZIkSZIkSRoYBjhLkiRJkiRJkiRJkiRJkiRJGhgGOEuSJEmSJEmSJEmSJEmSJEkaGAY4S5IkSZIkSZIkSZIkSZIkSRoYBjhLkiRJkiRJkiRJkiRJkiRJLwIRsWVEfD0iLo+IRyIiI+KUMRp7ch1vuMcyszvXPGOxYEmSJEmSJEn6R4qIicCdwEmZueMLuhhJkiRJkiRJkgbHgcCbgenAVGCFMRx7CnBIj22rAlsAN2TmPbM7kRmcJUmSJEmSJKmKiA0j4msRcXFEPFAzTVzRZ98tI+LnETEtIp6IiLsj4pyIWGsE8x88TNaLjYfou3pEnBYRUyNiRkT8KSIui4jtu7SdNyL+PSKuj4jHaxaPKyJi62HWt0hEfD4ifh8R02u/GyLiuIiY0Gg3sc8sHus2+uzYR/unW+uZEBF7RcR3I+K6iHiytttliH2Y0sc8nx3qOLTGG27du7Xaj/jYNPouHREnRMT/1dd4SkQcGRGL9Vjb3BGxbc3Ucn99rW+tx2vlLu1Xq+/BX0fEffV43hsR34+I1fs8HkvUvkN+dkaxL0Mdq6v7WZskSZIkSZI0TnwSWB54KfCvYzlwZk7JzIO7PYAna7Nvj8VcZnCWJEmSJEmSpOfsAbwfeAK4DVh8uA4RMQ9wErAN8EfgDOBhYClgbeCtwEgDME+iZMJou63HGvYEjgL+CvwMuLeufRVgU+B7jbbzAj8HJtc5vktJhrEpcEZErJKZn+syxwrAhcCrgYuA84EJwERga2Bf4O+1+UP0zuKxDLAz8ABwTaP+uiH6rAusX+dsWhA4sv79J+D+Ov5QjgQW7VIfwGco183b8/TjHMo+tF3bej6aY0NEvA64Enh5netmYE1gL2DjiJiUmQ+0xjuN8tpMBX4EPErJorIDsE1EbJKZv2y0PxZ4O/Cb2n46sBrwYWDLiPhQZv6ox9o7jgMWGqrBKPcF4C7gxC71U4dZkyRJkiRJkjRuZOYlnb8joq8+EfER4KPAW4D5KXdQPBU4PDNn9NF/CeCDwN9oXI+eHQY4S5IkSZIkSdJzvgIcQAm4XIZyEXc4h1CCmw8DPpeZzzQ3NjMbj8CJmXlpPw0jYiPgaOAXwJaZ+egw8+9BCW6+CtgwMx+r7RYCLgUOjIhzM/PaxhgvAc4FFgYmZeZMAds1yPvZ7MqZ+RBwcI/1fqn++b3mhfHMvI7uAcJExFX1z/9qbXqcEph9XWbeFxEHAwd1G6Mxz5Hd6iPiPZRr5r9r7vsInJ2ZJw7XaDTHpvomJSD4E5n59UafIygZWQ4DdmvUv40S3HwjsGZmPt7YthNwAuVWlc0A51OBf87MmQLpI2Jb4BTgvyLip5n5JF1EyRa+BbB7XW8vI9qXhik1E4wkSZIkSZKkMRIRJwA7URIJnEVJ0rAW8AXg3RGxYWY+NcwwOwDzUa5tPjQW65prLAaRJEmSJEmSpLEUEWtGxBkRcW9EzIiI+yLiwojYukvbiRFxekRMi4gnIuLaiHhfl3Y7RkTWcuOIuDQiHo6I7LTJzKsy88bMfLrdv8c6lwL2A67OzAPbwc11zL/P2nNMHU7JirFNO7i5x/wfrOVhneDm2m46cCglk/HurT67AW8APt0Obq59n8rMbNe31WDrHevTdrByrz6rUi6m30vJTt2c98nMPD8z7+tnrGF8tJbHjcFYIzbUsakZjzeiZNw+ptX1IOAxYLuIWLBR/9paXtwMbq7OqeWSzcrM/Ho7uLnWn0rJTv4ySgbobutflhJofzxDZMAe5b5IkiRJkiRJeh5ExI6U4OYfA8tn5r9k5r6ZOYmS3GMyJWnGcHat5ZhdXzWDsyRJkiRJkqSBEhG7At+iZAQ+lxJY+XJgDUrg7Q8azV8DXAPcAZwMLA58CDgnIjZo3oqvYUtgY0oQ5rF1jNHaEpgXOD0iFgDeC7weeBS4IjOv77GPCZCZve4PuE5ErAHMTQkEvTgzp3UZZxXgTcDZwIMR8S7grUBSsiFf0iXoeqla3tFl3k7du1v129QxT4+IicAmwKLA3cAFmflAj/1o27zO/6vMvLnPPp3A4+P7DTwfqYh4BbAZMB04bZTDrBYRe1Nu33gv5dhPHUH/oY7Nu2p5Yfv1zMxHI+LXlKDhtYCL66Yba7l+RCyQmX9rdOv8AOCiEayvEyg/S6aWKPe5PBF4GNiH8jnsZTT70rFoROxMOU4PA7/pFnDfWNdwnzNJkiRJkiRpvNuLcs1v59Y1RCgZnPcEtgWO6jVARKwHvBG4ITOvHKuFGeAsSZIkSZIkaWBExErAN4FHgHUz88bW9qVbXSYDB2fmIY02pwEXAP8GdAtw3hTYNDMvGIMlv62WLwFuBpZtrfcsYPsuGXSH84XW8xkRcTjwuVam5M78fwYuBd7Z6ve/EbFFKyvvNEo25uWAm1rtO1l/l+0ExdbMwm8G/kLJwvFFZr62/FhEfCIzT+hjv0aUJbkGjf8zJdiv+u1nAAALYklEQVT9O/30GaWdgQnAid2yYPdpr9bzpyPiO8DemflEH/2HOjZvrOWtPfr+kRIUvDw1KDgzb4iI/wQ+CdwcET+lBN6vTAnwPx04sI91ERFrAStRArdv6NJkb8pncaPMfCQihgpwHvG+NLyZkiG6ubbrge0y83+H2gdJkiRJkiRJM4uIl1CuuU0D9i55DGYxA1hxmKE61zb7umtfv+Yay8EkSZIkSZIkaTb9KyV49gvt4GaALhlx7wIObbX5OSWz8Jo95jhnjIKboWSWhhKQPAVYHViIkn32WuCfKAHbbSvS/aLw9ZRg29cCC1CyS+8KPEQJRj2sx/z/AkykZJBehBIcegqwKvCziJi30edntTygBhADEBELAp9ptFu0lotTXpOXAV+q+7oMsASwCyWz83ciYv0u+/Osmvl5Q+AB4Kyh2jZsXddxQWbe02efEanZh3epT0dzAf5O4OOUwN0FgVdR1j0F+BgwbOB3H8dmkVo+3GOITv2izcrM3AfYDViSkv18f0r25uuBkzLzsT7Wtjjwvfr0k+0s2vVHCV8Ejs3MfjJCj2pfgCOASZR9WZgS3P9Dyhcwv4yIV3cZq9fnTJIkSZIkSRIsBgTlmttBPR6volzz7qpeP/wn4G+UuyyOGTM4S5IkSZIkSRoka9Xy/D7bX9cOuKzuAdbu0eeaEa+qt04SiQeBzTLzkfr8vyNic0qW2u0i4oDMvLfTKTNv7jZYZv64VXU3JXj4t8DVwH4RcURmTmvNPzfw4cy8qj5/JCK2B1YA1qBcYP5+3XYUsBXwDuDGiDiPchH7vZRg5YcpQajPdJnjuMz8fGN9x9csH0dTgmd/2W2/ql3rPCdl5owh2jWNKOPzKG1ACSj/bWZe294YETtSgsebLs3MSwEy8zLgssa2x4EzI+JqSiDxRyLiK5l5/RBrGM2xGVIN3D6KEth8ICXg/SFgNeA/gfMjYs/MPGaIMRYEzqFk/P5qZp7Z2j6B8qXFfcC/j8W6e8nMfVtV1wJbRcQPKe/v/SjZqpt9un7OJEmSJEmSJAHPJRv4XWauPsoxdgDmo1zbfGhsllWYwVmSJEmSJEnSIOlkbb13yFbP6XXB9Cl6X/+8f0Qr6m/+ixvBzQBk5n3Af9d1rDE7k2TmbymB2ROYOXC7M//9jeDmTp+kBKdCI5t1Zk4H1qFkY36KElz7IeBXtX7uWv9g7dLMtNsOwG7W9cqYTUTMA+xUn/aVJTkiVqYEYU8FzuunzygNd/vEHZk1a8nk4QatGac7635nr3Z9HpvOa7BIj+2d+ubnYQdKZumjM/PLmTk1M6dn5hXAZpSMKl+OiK7ZV2pw888o74kjMnP/Ls0+DbwF2Km+r/oxmn0ZyrG17HmMJUmSJEmSJM2qXtO7EVi5ZmIejV1rOZq74w3JAGdJkiRJkiRJg6QT1Pjq53GOHMOxbqllr2DMv9ZygTGY6y+1XHB256+Brp/JzOUzc77MXCIzt6dk2lgIuD4z/17bPk7JiN1rnn72cTPglcBlmXnLEO2aOoHHx/fI0j3bIuLlwPuB6cBp3dpk5uTMjNbj4D6n6PaatfVzbDr1y/fY/oZa3tqoe18tL2k3zsz7gZspr/Ub29sjYmFKFvX1KJmb29mTO1anZJ6+NCKy8wDurNsn1brm+2Y0+zKUfo6xJEmSJEmSpO6OAOYFToiIRdsbI2KxiOia3Tki1gVWBG7IzCvHemHzjPWAkiRJkiRJkjQbrqZkO96EEoA56C4CPgus0mP7yrW8s8f2vkTEBEowKcAdjU1XA48BEyNiwcx8rNW1s65+59++lu1g34soWYZXoWSlHukcw2VJnklEzA9sBzwNHN9Pn1HaiZIV+8TMfPR5GP/ttbxjiDb9HJtOkPJGETFXZj7T2VCDkScBj1PeDx3z1XLJHmN26p9sVkbEIsAFwFrAYZl54BDr+gUwrUv9QpSs4H8CflrXNjv7MpS1ajnUMZYkSZIkSZLGjYj4APCB+nSpWq4dESfWv6dl5n4AmXlCRLwV2B24PSJ+DtwNLA4sR7lz2neB3bpMNaLrviNlBmdJkiRJkiRJg+RbwFPAZyNipfbGiFj6H7+kIV0OXAesExEfbG6IiF0p2StuA65tbVshIlZo1S0cEd2y6c4LHAksSwn6fnasml35eGB+4NCIiEa/VYEdKcfzh60xX9plng2B/YHbgeNam48BngE+FRFLNvrMDxxWn36/PWZt8xpgI+AB4KxubbrYClgMOD8z7xmu8WjUY7VLfdre35GMs0aXurki4tPA2pQA4At69O3r2GTm7cCFwERgj9bmQyjZi09uBbhfXst9atByc97dgKWB+4E/NOoXowSzrwUcNExwM5l5TGbu0n4An6pNbqt1n5idfYmIN9Ug/5lExJt47v13Spfts3zOav0SddsSQ+2fJEmSJEmSNIdaDdihPt5T617bqNuy2Tgz96Dcae4qYANgH2BzYBHgcMr16ZnUa4lbAn8DTn4+dsIMzpIkSZIkSZIGRmb+ISJ2B44FfhcR5wB/BF4GvA14BHjX8zV/RKzDc0GvC9XyDY3MFmTmjo2/MyJ2AC4DzoqInwC3UjI3b0LJrrxDZj7dmuqmzpSNupcBN0XEtXX7fZQsu++iZMqYBnykmfG2+iwli8belCwcvwZeAWxBCXzeuwaVNt0cEb+nBEw/QckOvQEl4PX97UzQmfmbiDiEEoB6Q0ScW/u9B3gDcCXwVbrbhZJs46TMnNGjTVvfmT8i4lNAJ4h1tVruVF9LgCsy8ztduq4PvB74bWb+ps91dfM/EXEDcD1wL+Wi/yRKZuvHgW0z85EefUdybHanHOejI+LdlPfI2ynvj1uBA1rtvwlsC7wJuLW+Zg9RXuv1Kdmx92i9N39EyaB+OzBXRBzcZR1nZ+Z1w6x1OCPdl32AzSLicuAeYAblNd8YmBv4Nt0D7Lt9zgD2BA6ivJ8Pns19kSRJkiRJkgZKZh7MCK97ZeZPKXdj67f9X4EFRrSwETLAWZIkSZIkSdJAycxv14DR/YDJlFvpTQN+D3QLVB1Lr6dksGh6eatux+bGzPx9RKxOCZjcCNiUst5TgS9k5i19zv0g8A1gTUrg8OLAk5Rg068AR2Tmn9udMvORiFgX+DQl8/GelKwZVwD/kZkXdpnrVEpw6DuACcBdlADlr2bmg90Wl5mfr6/L3sCHgHnr2g6s88wSoBsRcwM716d93aYwIlYE1gGmAuf10WVjYL1W3Tvqo6Pb+2asbp/4H5TXbH3Ka/YM5RaOx1Beszu6dRrpscnM22u26M9T9nlTShD8UcAh9QuFZvvpETGJEhy8BbAN5TX7C3Am5TW7pjXNcrV8HeX93M0UStbyURvpvgBnAy+lBGuvTwncfwA4H/h2Zp47O+uRJEmSJEmSNHgiM1/oNUiSJEmSJEmSJEmSJEmSJEkSUG59J0mSJEmSJEmSJEmSJEmSJEkDwQBnSZIkSZIkSZIkSZIkSZIkSQPDAGdJkiRJkiRJkiRJkiRJkiRJA8MAZ0mSJEmSJEmSJEmSJEmSJEkDwwBnSZIkSZIkSZIkSZIkSZIkSQPDAGdJkiRJkiRJkiRJkiRJkiRJA8MAZ0mSJEmSJEmSJEmSJEmSJEkDwwBnSZIkSZIkSZIkSZIkSZIkSQPDAGdJkiRJkiRJkiRJkiRJkiRJA8MAZ0mSJEmSJEmSJEmSJEmSJEkDwwBnSZIkSZIkSZIkSZIkSZIkSQPDAGdJkiRJkiRJkiRJkiRJkiRJA8MAZ0mSJEmSJEmSJEmSJEmSJEkDwwBnSZIkSZIkSZIkSZIkSZIkSQPDAGdJkiRJkiRJkiRJkiRJkiRJA8MAZ0mSJEmSJEmSJEmSJEmSJEkDwwBnSZIkSZIkSZIkSZIkSZIkSQPj/wEbdVMuQJ8RUgAAAABJRU5ErkJggg==\n",
-            "text/plain": [
-              "<Figure size 1440x360 with 5 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "image/png": {
-              "width": 1436,
-              "height": 351
-            },
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "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",
-      "metadata": {
-        "id": "-vmj1MA3chRQ"
-      },
-      "source": [
-        "enformer_score_variants = EnformerScoreVariantsPCANormalized(model_path, transform_path, num_top_features=20)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 275
-        },
-        "id": "1oGEYix3dRex",
-        "executionInfo": {
-          "status": "ok",
-          "timestamp": 1610403541225,
-          "user_tz": -60,
-          "elapsed": 84507,
-          "user": {
-            "displayName": "Žiga Avsec",
-            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjeqpTSqpdiH6-w18IlBq5jUYLVI3qWge6m6wC5=s64",
-            "userId": "06962645976249714923"
-          }
-        },
-        "outputId": "85c13ade-4e8c-42af-ec36-984f847fe513"
-      },
-      "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"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Done 0\n",
-            "Done 2\n",
-            "Done 4\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "execute_result",
-          "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>930188</td>\n",
-              "      <td>846933</td>\n",
-              "      <td>G</td>\n",
-              "      <td>A</td>\n",
-              "      <td>-61.471287</td>\n",
-              "      <td>-5.655146</td>\n",
-              "      <td>-2.758397</td>\n",
-              "      <td>6.290497</td>\n",
-              "      <td>1.846261</td>\n",
-              "      <td>3.449631</td>\n",
-              "      <td>6.052878</td>\n",
-              "      <td>0.634326</td>\n",
-              "      <td>2.584449</td>\n",
-              "      <td>1.118316</td>\n",
-              "      <td>1.944067</td>\n",
-              "      <td>-6.512556</td>\n",
-              "      <td>-4.948595</td>\n",
-              "      <td>-1.168765</td>\n",
-              "      <td>-2.903484</td>\n",
-              "      <td>0.484869</td>\n",
-              "      <td>2.897006</td>\n",
-              "      <td>1.758686</td>\n",
-              "      <td>3.685951</td>\n",
-              "      <td>-6.672137</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>chr1</td>\n",
-              "      <td>930203</td>\n",
-              "      <td>972363</td>\n",
-              "      <td>C</td>\n",
-              "      <td>T</td>\n",
-              "      <td>21.482735</td>\n",
-              "      <td>-9.249768</td>\n",
-              "      <td>3.685621</td>\n",
-              "      <td>9.606244</td>\n",
-              "      <td>0.538989</td>\n",
-              "      <td>5.262571</td>\n",
-              "      <td>1.418416</td>\n",
-              "      <td>-14.005647</td>\n",
-              "      <td>12.897153</td>\n",
-              "      <td>9.390783</td>\n",
-              "      <td>-5.201140</td>\n",
-              "      <td>-3.090504</td>\n",
-              "      <td>1.975024</td>\n",
-              "      <td>-5.241168</td>\n",
-              "      <td>-14.366508</td>\n",
-              "      <td>-7.802508</td>\n",
-              "      <td>0.141153</td>\n",
-              "      <td>12.087668</td>\n",
-              "      <td>-5.559410</td>\n",
-              "      <td>9.170748</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>chr1</td>\n",
-              "      <td>930248</td>\n",
-              "      <td>789256</td>\n",
-              "      <td>G</td>\n",
-              "      <td>A</td>\n",
-              "      <td>0.498874</td>\n",
-              "      <td>1.982327</td>\n",
-              "      <td>-2.750614</td>\n",
-              "      <td>-3.141907</td>\n",
-              "      <td>-5.241604</td>\n",
-              "      <td>-4.961285</td>\n",
-              "      <td>-1.702641</td>\n",
-              "      <td>2.543528</td>\n",
-              "      <td>-2.248600</td>\n",
-              "      <td>-0.774210</td>\n",
-              "      <td>-0.368460</td>\n",
-              "      <td>-1.392373</td>\n",
-              "      <td>-6.709529</td>\n",
-              "      <td>0.173268</td>\n",
-              "      <td>6.361717</td>\n",
-              "      <td>3.989930</td>\n",
-              "      <td>1.602195</td>\n",
-              "      <td>-5.265455</td>\n",
-              "      <td>0.852734</td>\n",
-              "      <td>-0.946416</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>chr1</td>\n",
-              "      <td>930275</td>\n",
-              "      <td>969662</td>\n",
-              "      <td>T</td>\n",
-              "      <td>G</td>\n",
-              "      <td>-3.028966</td>\n",
-              "      <td>2.984429</td>\n",
-              "      <td>7.532916</td>\n",
-              "      <td>6.588795</td>\n",
-              "      <td>19.307785</td>\n",
-              "      <td>5.661951</td>\n",
-              "      <td>0.962246</td>\n",
-              "      <td>5.863384</td>\n",
-              "      <td>-6.041630</td>\n",
-              "      <td>2.309155</td>\n",
-              "      <td>9.062576</td>\n",
-              "      <td>1.426052</td>\n",
-              "      <td>-3.792181</td>\n",
-              "      <td>3.136441</td>\n",
-              "      <td>-2.617620</td>\n",
-              "      <td>0.942220</td>\n",
-              "      <td>-6.195234</td>\n",
-              "      <td>-6.291888</td>\n",
-              "      <td>-5.329431</td>\n",
-              "      <td>-1.141422</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>chr1</td>\n",
-              "      <td>930336</td>\n",
-              "      <td>843786</td>\n",
-              "      <td>G</td>\n",
-              "      <td>A</td>\n",
-              "      <td>-45.674007</td>\n",
-              "      <td>-6.556405</td>\n",
-              "      <td>-0.550849</td>\n",
-              "      <td>16.986986</td>\n",
-              "      <td>-5.257315</td>\n",
-              "      <td>5.071064</td>\n",
-              "      <td>6.669114</td>\n",
-              "      <td>-1.328647</td>\n",
-              "      <td>-9.566864</td>\n",
-              "      <td>11.220359</td>\n",
-              "      <td>3.839488</td>\n",
-              "      <td>-8.214136</td>\n",
-              "      <td>-4.864720</td>\n",
-              "      <td>-2.947721</td>\n",
-              "      <td>-13.659990</td>\n",
-              "      <td>1.405919</td>\n",
-              "      <td>-3.936957</td>\n",
-              "      <td>10.316927</td>\n",
-              "      <td>1.663230</td>\n",
-              "      <td>-5.247654</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "  chrom     pos      id ref  ...      PC16       PC17      PC18      PC19\n",
-              "0  chr1  930188  846933   G  ...  2.897006   1.758686  3.685951 -6.672137\n",
-              "1  chr1  930203  972363   C  ...  0.141153  12.087668 -5.559410  9.170748\n",
-              "2  chr1  930248  789256   G  ...  1.602195  -5.265455  0.852734 -0.946416\n",
-              "3  chr1  930275  969662   T  ... -6.195234  -6.291888 -5.329431 -1.141422\n",
-              "4  chr1  930336  843786   G  ... -3.936957  10.316927  1.663230 -5.247654\n",
-              "\n",
-              "[5 rows x 25 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 34
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_G5cANX34SLw"
-      },
-      "source": [
-        "### Report all 5,313 features (z-score normalized)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "get8hogCySnt"
-      },
-      "source": [
-        "enformer_score_variants_all = EnformerScoreVariantsNormalized(model_path, transform_path)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 338
-        },
-        "id": "Q48earqRyFa6",
-        "executionInfo": {
-          "status": "ok",
-          "timestamp": 1610491940923,
-          "user_tz": -60,
-          "elapsed": 22163,
-          "user": {
-            "displayName": "Žiga Avsec",
-            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjeqpTSqpdiH6-w18IlBq5jUYLVI3qWge6m6wC5=s64",
-            "userId": "06962645976249714923"
-          }
-        },
-        "outputId": "dd7629d6-fdcb-4057-d72a-5c75cdc9b3b6"
-      },
-      "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 = {name[:20]: score for name, score in 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"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Done 0\n",
-            "Done 2\n",
-            "Done 4\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "execute_result",
-          "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>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>930188</td>\n",
-              "      <td>846933</td>\n",
-              "      <td>G</td>\n",
-              "      <td>A</td>\n",
-              "      <td>-1.786843</td>\n",
-              "      <td>-15.682123</td>\n",
-              "      <td>-22.520702</td>\n",
-              "      <td>0.528606</td>\n",
-              "      <td>-3.829948</td>\n",
-              "      <td>-3.232739</td>\n",
-              "      <td>-2.671064</td>\n",
-              "      <td>-9.467652</td>\n",
-              "      <td>-3.155200</td>\n",
-              "      <td>-7.706491</td>\n",
-              "      <td>-3.149441</td>\n",
-              "      <td>-0.523933</td>\n",
-              "      <td>-11.134032</td>\n",
-              "      <td>-11.109153</td>\n",
-              "      <td>-3.670341</td>\n",
-              "      <td>-8.464477</td>\n",
-              "      <td>-11.757355</td>\n",
-              "      <td>-8.598063</td>\n",
-              "      <td>-0.531494</td>\n",
-              "      <td>0.225998</td>\n",
-              "      <td>-0.777080</td>\n",
-              "      <td>-15.741483</td>\n",
-              "      <td>0.879350</td>\n",
-              "      <td>-6.576440</td>\n",
-              "      <td>-4.204203</td>\n",
-              "      <td>-8.632437</td>\n",
-              "      <td>-0.466419</td>\n",
-              "      <td>-3.439260</td>\n",
-              "      <td>-0.311319</td>\n",
-              "      <td>-4.804222</td>\n",
-              "      <td>-5.151290</td>\n",
-              "      <td>-1.418189</td>\n",
-              "      <td>0.366935</td>\n",
-              "      <td>-2.001119</td>\n",
-              "      <td>-2.294838</td>\n",
-              "      <td>...</td>\n",
-              "      <td>-33.057209</td>\n",
-              "      <td>-2.997374</td>\n",
-              "      <td>-8.419192</td>\n",
-              "      <td>-5.191596</td>\n",
-              "      <td>-4.536147</td>\n",
-              "      <td>-5.455176</td>\n",
-              "      <td>-3.037405</td>\n",
-              "      <td>-7.164758</td>\n",
-              "      <td>-2.292249</td>\n",
-              "      <td>-8.986766</td>\n",
-              "      <td>-4.790454</td>\n",
-              "      <td>-4.795162</td>\n",
-              "      <td>-1.450022</td>\n",
-              "      <td>-1.989321</td>\n",
-              "      <td>-22.918665</td>\n",
-              "      <td>-35.752350</td>\n",
-              "      <td>-35.278088</td>\n",
-              "      <td>-13.375848</td>\n",
-              "      <td>-28.361494</td>\n",
-              "      <td>-31.598894</td>\n",
-              "      <td>-11.373973</td>\n",
-              "      <td>-9.255016</td>\n",
-              "      <td>-9.010527</td>\n",
-              "      <td>-9.231528</td>\n",
-              "      <td>-3.780572</td>\n",
-              "      <td>-0.282759</td>\n",
-              "      <td>-12.095373</td>\n",
-              "      <td>-59.163910</td>\n",
-              "      <td>-66.295670</td>\n",
-              "      <td>-34.482758</td>\n",
-              "      <td>-21.271309</td>\n",
-              "      <td>-4.326236</td>\n",
-              "      <td>-16.074772</td>\n",
-              "      <td>-6.284147</td>\n",
-              "      <td>-10.391891</td>\n",
-              "      <td>-6.422609</td>\n",
-              "      <td>-9.180881</td>\n",
-              "      <td>-14.222376</td>\n",
-              "      <td>-46.489288</td>\n",
-              "      <td>-11.498157</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>chr1</td>\n",
-              "      <td>930203</td>\n",
-              "      <td>972363</td>\n",
-              "      <td>C</td>\n",
-              "      <td>T</td>\n",
-              "      <td>3.888234</td>\n",
-              "      <td>-0.792556</td>\n",
-              "      <td>-1.675466</td>\n",
-              "      <td>4.746172</td>\n",
-              "      <td>1.695362</td>\n",
-              "      <td>2.216201</td>\n",
-              "      <td>-0.281044</td>\n",
-              "      <td>-1.136617</td>\n",
-              "      <td>0.372290</td>\n",
-              "      <td>-3.771606</td>\n",
-              "      <td>3.466754</td>\n",
-              "      <td>1.097348</td>\n",
-              "      <td>2.531235</td>\n",
-              "      <td>5.103032</td>\n",
-              "      <td>1.873044</td>\n",
-              "      <td>3.959806</td>\n",
-              "      <td>1.216984</td>\n",
-              "      <td>3.810981</td>\n",
-              "      <td>0.649070</td>\n",
-              "      <td>2.553843</td>\n",
-              "      <td>0.989534</td>\n",
-              "      <td>1.633624</td>\n",
-              "      <td>5.512297</td>\n",
-              "      <td>-6.207155</td>\n",
-              "      <td>-13.487192</td>\n",
-              "      <td>6.362380</td>\n",
-              "      <td>0.166047</td>\n",
-              "      <td>-4.554312</td>\n",
-              "      <td>-0.160736</td>\n",
-              "      <td>-1.996482</td>\n",
-              "      <td>-2.641517</td>\n",
-              "      <td>0.414536</td>\n",
-              "      <td>7.214015</td>\n",
-              "      <td>4.067241</td>\n",
-              "      <td>2.957357</td>\n",
-              "      <td>...</td>\n",
-              "      <td>33.842506</td>\n",
-              "      <td>-0.583508</td>\n",
-              "      <td>0.268065</td>\n",
-              "      <td>-3.203938</td>\n",
-              "      <td>5.382962</td>\n",
-              "      <td>1.292423</td>\n",
-              "      <td>0.993049</td>\n",
-              "      <td>2.095632</td>\n",
-              "      <td>-0.293384</td>\n",
-              "      <td>4.008999</td>\n",
-              "      <td>-0.025227</td>\n",
-              "      <td>-0.550521</td>\n",
-              "      <td>-1.000858</td>\n",
-              "      <td>0.568729</td>\n",
-              "      <td>27.443323</td>\n",
-              "      <td>-22.869347</td>\n",
-              "      <td>-32.034206</td>\n",
-              "      <td>12.032639</td>\n",
-              "      <td>15.752299</td>\n",
-              "      <td>19.407349</td>\n",
-              "      <td>2.799562</td>\n",
-              "      <td>3.406885</td>\n",
-              "      <td>1.643052</td>\n",
-              "      <td>2.385034</td>\n",
-              "      <td>-1.272377</td>\n",
-              "      <td>35.168621</td>\n",
-              "      <td>-0.531365</td>\n",
-              "      <td>-15.452000</td>\n",
-              "      <td>42.711445</td>\n",
-              "      <td>22.988682</td>\n",
-              "      <td>-2.011288</td>\n",
-              "      <td>1.716742</td>\n",
-              "      <td>1.979876</td>\n",
-              "      <td>5.205636</td>\n",
-              "      <td>6.388551</td>\n",
-              "      <td>3.159813</td>\n",
-              "      <td>0.636698</td>\n",
-              "      <td>-0.784998</td>\n",
-              "      <td>-4.407548</td>\n",
-              "      <td>3.055573</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>chr1</td>\n",
-              "      <td>930248</td>\n",
-              "      <td>789256</td>\n",
-              "      <td>G</td>\n",
-              "      <td>A</td>\n",
-              "      <td>-3.392124</td>\n",
-              "      <td>-6.586333</td>\n",
-              "      <td>-6.679351</td>\n",
-              "      <td>-0.481332</td>\n",
-              "      <td>0.732646</td>\n",
-              "      <td>1.129496</td>\n",
-              "      <td>1.167766</td>\n",
-              "      <td>-1.667323</td>\n",
-              "      <td>0.800817</td>\n",
-              "      <td>-4.803396</td>\n",
-              "      <td>-0.813693</td>\n",
-              "      <td>0.774903</td>\n",
-              "      <td>-2.916472</td>\n",
-              "      <td>-3.757783</td>\n",
-              "      <td>0.205903</td>\n",
-              "      <td>-2.631254</td>\n",
-              "      <td>-2.177644</td>\n",
-              "      <td>-5.314756</td>\n",
-              "      <td>-1.064787</td>\n",
-              "      <td>-4.775336</td>\n",
-              "      <td>-0.307169</td>\n",
-              "      <td>-9.369971</td>\n",
-              "      <td>-4.574942</td>\n",
-              "      <td>-1.763874</td>\n",
-              "      <td>4.670806</td>\n",
-              "      <td>-9.955566</td>\n",
-              "      <td>2.540043</td>\n",
-              "      <td>-0.725449</td>\n",
-              "      <td>-0.909228</td>\n",
-              "      <td>-2.151628</td>\n",
-              "      <td>-1.621970</td>\n",
-              "      <td>0.037685</td>\n",
-              "      <td>-6.617916</td>\n",
-              "      <td>-4.519673</td>\n",
-              "      <td>-3.751483</td>\n",
-              "      <td>...</td>\n",
-              "      <td>-3.718003</td>\n",
-              "      <td>-0.702731</td>\n",
-              "      <td>3.008476</td>\n",
-              "      <td>0.761387</td>\n",
-              "      <td>-3.265729</td>\n",
-              "      <td>1.070971</td>\n",
-              "      <td>-0.497848</td>\n",
-              "      <td>0.965013</td>\n",
-              "      <td>0.367486</td>\n",
-              "      <td>-1.758073</td>\n",
-              "      <td>0.375682</td>\n",
-              "      <td>-0.949173</td>\n",
-              "      <td>-1.022737</td>\n",
-              "      <td>0.486035</td>\n",
-              "      <td>-0.185559</td>\n",
-              "      <td>56.499584</td>\n",
-              "      <td>67.082275</td>\n",
-              "      <td>1.473261</td>\n",
-              "      <td>-0.636782</td>\n",
-              "      <td>-0.981452</td>\n",
-              "      <td>-0.348060</td>\n",
-              "      <td>0.946137</td>\n",
-              "      <td>0.874985</td>\n",
-              "      <td>0.469065</td>\n",
-              "      <td>-3.461762</td>\n",
-              "      <td>-21.804483</td>\n",
-              "      <td>1.403917</td>\n",
-              "      <td>-1.323191</td>\n",
-              "      <td>0.396306</td>\n",
-              "      <td>-5.381416</td>\n",
-              "      <td>4.841636</td>\n",
-              "      <td>0.019535</td>\n",
-              "      <td>2.721695</td>\n",
-              "      <td>-1.743816</td>\n",
-              "      <td>-1.392196</td>\n",
-              "      <td>1.243525</td>\n",
-              "      <td>-1.840826</td>\n",
-              "      <td>-0.622136</td>\n",
-              "      <td>6.409196</td>\n",
-              "      <td>-0.923778</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>chr1</td>\n",
-              "      <td>930275</td>\n",
-              "      <td>969662</td>\n",
-              "      <td>T</td>\n",
-              "      <td>G</td>\n",
-              "      <td>3.287432</td>\n",
-              "      <td>-1.920073</td>\n",
-              "      <td>5.813212</td>\n",
-              "      <td>2.442116</td>\n",
-              "      <td>3.727493</td>\n",
-              "      <td>3.496869</td>\n",
-              "      <td>0.895325</td>\n",
-              "      <td>2.030542</td>\n",
-              "      <td>4.058121</td>\n",
-              "      <td>-1.889122</td>\n",
-              "      <td>1.946017</td>\n",
-              "      <td>-1.543661</td>\n",
-              "      <td>9.562781</td>\n",
-              "      <td>8.657282</td>\n",
-              "      <td>1.719417</td>\n",
-              "      <td>1.913109</td>\n",
-              "      <td>7.135823</td>\n",
-              "      <td>3.970765</td>\n",
-              "      <td>0.610678</td>\n",
-              "      <td>3.715666</td>\n",
-              "      <td>0.340136</td>\n",
-              "      <td>-12.232494</td>\n",
-              "      <td>5.522506</td>\n",
-              "      <td>7.254625</td>\n",
-              "      <td>7.424074</td>\n",
-              "      <td>-1.945692</td>\n",
-              "      <td>1.009203</td>\n",
-              "      <td>-0.339282</td>\n",
-              "      <td>-0.795034</td>\n",
-              "      <td>4.926074</td>\n",
-              "      <td>9.191165</td>\n",
-              "      <td>2.844069</td>\n",
-              "      <td>5.490332</td>\n",
-              "      <td>7.751811</td>\n",
-              "      <td>6.818331</td>\n",
-              "      <td>...</td>\n",
-              "      <td>-2.347892</td>\n",
-              "      <td>-3.573529</td>\n",
-              "      <td>-6.638988</td>\n",
-              "      <td>-5.852189</td>\n",
-              "      <td>8.638922</td>\n",
-              "      <td>-3.788154</td>\n",
-              "      <td>-4.986428</td>\n",
-              "      <td>-6.144402</td>\n",
-              "      <td>-2.663138</td>\n",
-              "      <td>-0.227634</td>\n",
-              "      <td>0.630000</td>\n",
-              "      <td>-1.148500</td>\n",
-              "      <td>-0.321750</td>\n",
-              "      <td>-0.875137</td>\n",
-              "      <td>-10.835594</td>\n",
-              "      <td>1.012006</td>\n",
-              "      <td>0.616927</td>\n",
-              "      <td>-0.123525</td>\n",
-              "      <td>0.815517</td>\n",
-              "      <td>1.417692</td>\n",
-              "      <td>1.730117</td>\n",
-              "      <td>2.003574</td>\n",
-              "      <td>2.723122</td>\n",
-              "      <td>1.179165</td>\n",
-              "      <td>2.852669</td>\n",
-              "      <td>39.458965</td>\n",
-              "      <td>5.736304</td>\n",
-              "      <td>-20.788565</td>\n",
-              "      <td>-5.314080</td>\n",
-              "      <td>0.397473</td>\n",
-              "      <td>1.973049</td>\n",
-              "      <td>4.607931</td>\n",
-              "      <td>7.300556</td>\n",
-              "      <td>9.865562</td>\n",
-              "      <td>3.085788</td>\n",
-              "      <td>1.254327</td>\n",
-              "      <td>1.854584</td>\n",
-              "      <td>2.621457</td>\n",
-              "      <td>-24.944792</td>\n",
-              "      <td>1.078750</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>chr1</td>\n",
-              "      <td>930336</td>\n",
-              "      <td>843786</td>\n",
-              "      <td>G</td>\n",
-              "      <td>A</td>\n",
-              "      <td>13.966989</td>\n",
-              "      <td>-14.674191</td>\n",
-              "      <td>-17.506096</td>\n",
-              "      <td>10.756916</td>\n",
-              "      <td>-1.225523</td>\n",
-              "      <td>2.911127</td>\n",
-              "      <td>0.152567</td>\n",
-              "      <td>-3.519540</td>\n",
-              "      <td>0.049325</td>\n",
-              "      <td>-11.031021</td>\n",
-              "      <td>1.868334</td>\n",
-              "      <td>1.091527</td>\n",
-              "      <td>-2.538539</td>\n",
-              "      <td>0.955972</td>\n",
-              "      <td>1.841750</td>\n",
-              "      <td>-4.661942</td>\n",
-              "      <td>-13.774675</td>\n",
-              "      <td>-9.152153</td>\n",
-              "      <td>-0.745051</td>\n",
-              "      <td>6.185897</td>\n",
-              "      <td>1.756672</td>\n",
-              "      <td>-18.894367</td>\n",
-              "      <td>5.834339</td>\n",
-              "      <td>-8.249739</td>\n",
-              "      <td>-11.984449</td>\n",
-              "      <td>-11.462355</td>\n",
-              "      <td>1.577443</td>\n",
-              "      <td>-6.717025</td>\n",
-              "      <td>0.324789</td>\n",
-              "      <td>-4.330544</td>\n",
-              "      <td>-2.505473</td>\n",
-              "      <td>0.463757</td>\n",
-              "      <td>7.450192</td>\n",
-              "      <td>7.164897</td>\n",
-              "      <td>4.730538</td>\n",
-              "      <td>...</td>\n",
-              "      <td>-19.131910</td>\n",
-              "      <td>-4.917017</td>\n",
-              "      <td>-8.311162</td>\n",
-              "      <td>-5.551866</td>\n",
-              "      <td>7.141852</td>\n",
-              "      <td>-4.919424</td>\n",
-              "      <td>-3.647799</td>\n",
-              "      <td>-6.864504</td>\n",
-              "      <td>-2.496408</td>\n",
-              "      <td>-9.047115</td>\n",
-              "      <td>-3.398409</td>\n",
-              "      <td>-6.225352</td>\n",
-              "      <td>-2.351780</td>\n",
-              "      <td>-2.361993</td>\n",
-              "      <td>-17.526522</td>\n",
-              "      <td>-46.520599</td>\n",
-              "      <td>-57.027424</td>\n",
-              "      <td>-8.135074</td>\n",
-              "      <td>-25.883907</td>\n",
-              "      <td>-6.518816</td>\n",
-              "      <td>-1.284766</td>\n",
-              "      <td>-2.227928</td>\n",
-              "      <td>-1.223655</td>\n",
-              "      <td>-1.058415</td>\n",
-              "      <td>3.015599</td>\n",
-              "      <td>69.669312</td>\n",
-              "      <td>-6.541300</td>\n",
-              "      <td>-73.421532</td>\n",
-              "      <td>-36.244965</td>\n",
-              "      <td>-20.472229</td>\n",
-              "      <td>-8.516649</td>\n",
-              "      <td>8.461418</td>\n",
-              "      <td>-3.483475</td>\n",
-              "      <td>14.235535</td>\n",
-              "      <td>6.582296</td>\n",
-              "      <td>4.010679</td>\n",
-              "      <td>1.865496</td>\n",
-              "      <td>-6.477487</td>\n",
-              "      <td>-42.006096</td>\n",
-              "      <td>-0.880794</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  930188  ...           -46.489288           -11.498157\n",
-              "1  chr1  930203  ...            -4.407548             3.055573\n",
-              "2  chr1  930248  ...             6.409196            -0.923778\n",
-              "3  chr1  930275  ...           -24.944792             1.078750\n",
-              "4  chr1  930336  ...           -42.006096            -0.880794\n",
-              "\n",
-              "[5 rows x 3378 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 21
-        }
-      ]
-    }
-  ]
-}
diff --git a/enformer/enformer-training.ipynb b/enformer/enformer-training.ipynb
new file mode 100644
index 0000000..0681e33
--- /dev/null
+++ b/enformer/enformer-training.ipynb
@@ -0,0 +1,1017 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "enformer-training.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "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 training 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\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2AVkKjy3bh_A"
+      },
+      "source": [
+        "## Steps\n",
+        "\n",
+        "- Setup tf.data.Dataset by directly accessing the Basenji2 data on GCS: `gs://basenji_barnyard/data`\n",
+        "- Train the model for a few steps, alternating training on human and mouse data batches\n",
+        "- Evaluate the model on human and mouse genomes"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sM_PMOT-2Xhi"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NqR7ol3rxrtM"
+      },
+      "source": [
+        "**Start the colab kernel with GPU**: Runtime -> Change runtime type -> GPU"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vhjR7StI1tZn"
+      },
+      "source": [
+        "### Install dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WiDFm-a41tKW",
+        "outputId": "8b889c6e-f113-4664-f2c9-91110808ad92"
+      },
+      "source": [
+        "!pip install dm-sonnet tqdm"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Collecting dm-sonnet\n",
+            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/13/28/9185afffefb655ef1a29f4b84aa9f656826408ca2d1b9ffeba81fbfd40ec/dm_sonnet-2.0.0-py3-none-any.whl (254kB)\n",
+            "\r\u001b[K     |█▎                              | 10kB 13.3MB/s eta 0:00:01\r\u001b[K     |██▋                             | 20kB 11.7MB/s eta 0:00:01\r\u001b[K     |███▉                            | 30kB 8.7MB/s eta 0:00:01\r\u001b[K     |█████▏                          | 40kB 7.7MB/s eta 0:00:01\r\u001b[K     |██████▍                         | 51kB 4.5MB/s eta 0:00:01\r\u001b[K     |███████▊                        | 61kB 5.1MB/s eta 0:00:01\r\u001b[K     |█████████                       | 71kB 5.1MB/s eta 0:00:01\r\u001b[K     |██████████▎                     | 81kB 5.5MB/s eta 0:00:01\r\u001b[K     |███████████▋                    | 92kB 5.5MB/s eta 0:00:01\r\u001b[K     |████████████▉                   | 102kB 5.7MB/s eta 0:00:01\r\u001b[K     |██████████████▏                 | 112kB 5.7MB/s eta 0:00:01\r\u001b[K     |███████████████▌                | 122kB 5.7MB/s eta 0:00:01\r\u001b[K     |████████████████▊               | 133kB 5.7MB/s eta 0:00:01\r\u001b[K     |██████████████████              | 143kB 5.7MB/s eta 0:00:01\r\u001b[K     |███████████████████▎            | 153kB 5.7MB/s eta 0:00:01\r\u001b[K     |████████████████████▋           | 163kB 5.7MB/s eta 0:00:01\r\u001b[K     |██████████████████████          | 174kB 5.7MB/s eta 0:00:01\r\u001b[K     |███████████████████████▏        | 184kB 5.7MB/s eta 0:00:01\r\u001b[K     |████████████████████████▌       | 194kB 5.7MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▊      | 204kB 5.7MB/s eta 0:00:01\r\u001b[K     |███████████████████████████     | 215kB 5.7MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▎   | 225kB 5.7MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▋  | 235kB 5.7MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████ | 245kB 5.7MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 256kB 5.7MB/s \n",
+            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (4.41.1)\n",
+            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.7/dist-packages (from dm-sonnet) (1.15.0)\n",
+            "Requirement already satisfied: absl-py>=0.7.1 in /usr/local/lib/python3.7/dist-packages (from dm-sonnet) (0.12.0)\n",
+            "Requirement already satisfied: numpy>=1.16.3 in /usr/local/lib/python3.7/dist-packages (from dm-sonnet) (1.19.5)\n",
+            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from dm-sonnet) (1.12.1)\n",
+            "Requirement already satisfied: tabulate>=0.7.5 in /usr/local/lib/python3.7/dist-packages (from dm-sonnet) (0.8.9)\n",
+            "Requirement already satisfied: dm-tree>=0.1.1 in /usr/local/lib/python3.7/dist-packages (from dm-sonnet) (0.1.5)\n",
+            "Installing collected packages: dm-sonnet\n",
+            "Successfully installed dm-sonnet-2.0.0\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "CokqDsb-fxme"
+      },
+      "source": [
+        "# Get enformer source code\n",
+        "!wget -q https://raw.githubusercontent.com/deepmind/deepmind-research/master/enformer/attention_module.py\n",
+        "!wget -q https://raw.githubusercontent.com/deepmind/deepmind-research/master/enformer/enformer.py"
+      ],
+      "execution_count": 15,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xmffZS_306eb"
+      },
+      "source": [
+        "### Import"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hTGOLrbZxNHK",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f58b5c21-0764-4003-c794-aa89e5d336cc"
+      },
+      "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'\n",
+        "\n",
+        "# Easier debugging of OOM\n",
+        "%env TF_ENABLE_GPU_GARBAGE_COLLECTION=false"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "env: TF_ENABLE_GPU_GARBAGE_COLLECTION=false\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "S9ywsUmT05C1"
+      },
+      "source": [
+        "import sonnet as snt\n",
+        "from tqdm import tqdm\n",
+        "from IPython.display import clear_output\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import time\n",
+        "import os"
+      ],
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "YUIbu0Xu1BnA"
+      },
+      "source": [
+        "assert snt.__version__.startswith('2.0')"
+      ],
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "PWzsyJddILcx",
+        "outputId": "3f1cac0f-6bce-430e-b3c0-9848d43e654c"
+      },
+      "source": [
+        "tf.__version__"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'2.4.1'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xOhdaXG95eOl",
+        "outputId": "1e57ef49-254a-4050-89af-61bc0f8ea577"
+      },
+      "source": [
+        "# GPU colab has T4 with 16 GiB of memory\n",
+        "!nvidia-smi"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Fri Mar 26 12:28:00 2021       \n",
+            "+-----------------------------------------------------------------------------+\n",
+            "| NVIDIA-SMI 460.67       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
+            "|-------------------------------+----------------------+----------------------+\n",
+            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+            "|                               |                      |               MIG M. |\n",
+            "|===============================+======================+======================|\n",
+            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
+            "| N/A   42C    P8    10W /  70W |      3MiB / 15109MiB |      0%      Default |\n",
+            "|                               |                      |                  N/A |\n",
+            "+-------------------------------+----------------------+----------------------+\n",
+            "                                                                               \n",
+            "+-----------------------------------------------------------------------------+\n",
+            "| Processes:                                                                  |\n",
+            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+            "|        ID   ID                                                   Usage      |\n",
+            "|=============================================================================|\n",
+            "|  No running processes found                                                 |\n",
+            "+-----------------------------------------------------------------------------+\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0Xx--Nco09fN"
+      },
+      "source": [
+        "### Code"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "BbXyDdoShFzX"
+      },
+      "source": [
+        "import enformer"
+      ],
+      "execution_count": 37,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "MEb8OZli2Nbu"
+      },
+      "source": [
+        "# @title `get_targets(organism)`\n",
+        "def get_targets(organism):\n",
+        "  targets_txt = f'https://raw.githubusercontent.com/calico/basenji/master/manuscripts/cross2020/targets_{organism}.txt'\n",
+        "  return pd.read_csv(targets_txt, sep='\\t')"
+      ],
+      "execution_count": 41,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2BuZ2gmUbpXZ"
+      },
+      "source": [
+        "# @title `get_dataset(organism, subset, num_threads=8)`\n",
+        "import glob\n",
+        "import json\n",
+        "import functools\n",
+        "\n",
+        "\n",
+        "def organism_path(organism):\n",
+        "  return os.path.join('gs://basenji_barnyard/data', organism)\n",
+        "\n",
+        "\n",
+        "def get_dataset(organism, subset, num_threads=8):\n",
+        "  metadata = get_metadata(organism)\n",
+        "  dataset = tf.data.TFRecordDataset(tfrecord_files(organism, subset),\n",
+        "                                    compression_type='ZLIB',\n",
+        "                                    num_parallel_reads=num_threads)\n",
+        "  dataset = dataset.map(functools.partial(deserialize, metadata=metadata),\n",
+        "                        num_parallel_calls=num_threads)\n",
+        "  return dataset\n",
+        "\n",
+        "\n",
+        "def get_metadata(organism):\n",
+        "  # Keys:\n",
+        "  # num_targets, train_seqs, valid_seqs, test_seqs, seq_length,\n",
+        "  # pool_width, crop_bp, target_length\n",
+        "  path = os.path.join(organism_path(organism), 'statistics.json')\n",
+        "  with tf.io.gfile.GFile(path, 'r') as f:\n",
+        "    return json.load(f)\n",
+        "\n",
+        "\n",
+        "def tfrecord_files(organism, subset):\n",
+        "  # Sort the values by int(*).\n",
+        "  return sorted(tf.io.gfile.glob(os.path.join(\n",
+        "      organism_path(organism), 'tfrecords', f'{subset}-*.tfr'\n",
+        "  )), key=lambda x: int(x.split('-')[-1].split('.')[0]))\n",
+        "\n",
+        "\n",
+        "def deserialize(serialized_example, metadata):\n",
+        "  \"\"\"Deserialize bytes stored in TFRecordFile.\"\"\"\n",
+        "  feature_map = {\n",
+        "      'sequence': tf.io.FixedLenFeature([], tf.string),\n",
+        "      'target': tf.io.FixedLenFeature([], tf.string),\n",
+        "  }\n",
+        "  example = tf.io.parse_example(serialized_example, feature_map)\n",
+        "  sequence = tf.io.decode_raw(example['sequence'], tf.bool)\n",
+        "  sequence = tf.reshape(sequence, (metadata['seq_length'], 4))\n",
+        "  sequence = tf.cast(sequence, tf.float32)\n",
+        "\n",
+        "  target = tf.io.decode_raw(example['target'], tf.float16)\n",
+        "  target = tf.reshape(target,\n",
+        "                      (metadata['target_length'], metadata['num_targets']))\n",
+        "  target = tf.cast(target, tf.float32)\n",
+        "\n",
+        "  return {'sequence': sequence,\n",
+        "          'target': target}\n"
+      ],
+      "execution_count": 42,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VzGRXfwV4tYH"
+      },
+      "source": [
+        "## Load dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 203
+        },
+        "id": "M_vr1mbl3jbD",
+        "outputId": "2de351ed-f43e-4469-a681-2a437d97c946"
+      },
+      "source": [
+        "df_targets_human = get_targets('human')\n",
+        "df_targets_human.head()"
+      ],
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>ENCFF463ZLQ</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:Ishikawa treated with 0.02% dimethyl sul...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>ENCFF890OGQ</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:GM03348</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   index  genome  ... sum_stat                                        description\n",
+              "0      0       0  ...     mean  DNASE:cerebellum male adult (27 years) and mal...\n",
+              "1      1       0  ...     mean  DNASE:frontal cortex male adult (27 years) and...\n",
+              "2      2       0  ...     mean                                      DNASE:chorion\n",
+              "3      3       0  ...     mean  DNASE:Ishikawa treated with 0.02% dimethyl sul...\n",
+              "4      4       0  ...     mean                                      DNASE:GM03348\n",
+              "\n",
+              "[5 rows x 8 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 43
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "YDSKttXI4hMT"
+      },
+      "source": [
+        "human_dataset = get_dataset('human', 'train').batch(1).repeat()\n",
+        "mouse_dataset = get_dataset('mouse', 'train').batch(1).repeat()\n",
+        "human_mouse_dataset = tf.data.Dataset.zip((human_dataset, mouse_dataset)).prefetch(2)"
+      ],
+      "execution_count": 44,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2vx3116C7oFW"
+      },
+      "source": [
+        "it = iter(mouse_dataset)\n",
+        "example = next(it)"
+      ],
+      "execution_count": 45,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XeztqJZ74ixT",
+        "outputId": "39dc4051-5a19-4443-b6b0-bf6869faf5ec"
+      },
+      "source": [
+        "# Example input\n",
+        "it = iter(human_mouse_dataset)\n",
+        "example = next(it)\n",
+        "for i in range(len(example)):\n",
+        "  print(['human', 'mouse'][i])\n",
+        "  print({k: (v.shape, v.dtype) for k,v in example[i].items()})"
+      ],
+      "execution_count": 46,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "human\n",
+            "{'sequence': (TensorShape([1, 131072, 4]), tf.float32), 'target': (TensorShape([1, 896, 5313]), tf.float32)}\n",
+            "mouse\n",
+            "{'sequence': (TensorShape([1, 131072, 4]), tf.float32), 'target': (TensorShape([1, 896, 1643]), tf.float32)}\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SHHNHzFVbvTk"
+      },
+      "source": [
+        "## Model training"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "0U3hLJaUdZkG"
+      },
+      "source": [
+        "def create_step_function(model, optimizer):\n",
+        "\n",
+        "  @tf.function\n",
+        "  def train_step(batch, head, optimizer_clip_norm_global=0.2):\n",
+        "    with tf.GradientTape() as tape:\n",
+        "      outputs = model(batch['sequence'], is_training=True)[head]\n",
+        "      loss = tf.reduce_mean(\n",
+        "          tf.keras.losses.poisson(batch['target'], outputs))\n",
+        "\n",
+        "    gradients = tape.gradient(loss, model.trainable_variables)\n",
+        "    optimizer.apply(gradients, model.trainable_variables)\n",
+        "\n",
+        "    return loss\n",
+        "  return train_step"
+      ],
+      "execution_count": 51,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ZXv5HU_242Ut"
+      },
+      "source": [
+        "learning_rate = tf.Variable(0., trainable=False, name='learning_rate')\n",
+        "optimizer = snt.optimizers.Adam(learning_rate=learning_rate)\n",
+        "num_warmup_steps = 5000\n",
+        "target_learning_rate = 0.0005\n",
+        "\n",
+        "model = enformer.Enformer(channels=1536 // 4,  # Use 4x fewer channels to train faster.\n",
+        "                          num_heads=8,\n",
+        "                          num_transformer_layers=11,\n",
+        "                          pooling_type='max')\n",
+        "\n",
+        "train_step = create_step_function(model, optimizer)"
+      ],
+      "execution_count": 52,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FrbDaOMWcFUl",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "cellView": "code",
+        "outputId": "6a42f69c-3003-47f2-a8d2-1b94c52eb57e"
+      },
+      "source": [
+        "# Train the model\n",
+        "steps_per_epoch = 20\n",
+        "num_epochs = 5\n",
+        "\n",
+        "data_it = iter(human_mouse_dataset)\n",
+        "global_step = 0\n",
+        "for epoch_i in range(num_epochs):\n",
+        "  for i in tqdm(range(steps_per_epoch)):\n",
+        "    global_step += 1\n",
+        "\n",
+        "    if global_step > 1:\n",
+        "      learning_rate_frac = tf.math.minimum(\n",
+        "          1.0, global_step / tf.math.maximum(1.0, num_warmup_steps))      \n",
+        "      learning_rate.assign(target_learning_rate * learning_rate_frac)\n",
+        "\n",
+        "    batch_human, batch_mouse = next(data_it)\n",
+        "\n",
+        "    loss_human = train_step(batch=batch_human, head='human')\n",
+        "    loss_mouse = train_step(batch=batch_mouse, head='mouse')\n",
+        "\n",
+        "  # End of epoch.\n",
+        "  print('')\n",
+        "  print('loss_human', loss_human.numpy(),\n",
+        "        'loss_mouse', loss_mouse.numpy(),\n",
+        "        'learning_rate', optimizer.learning_rate.numpy()\n",
+        "        )"
+      ],
+      "execution_count": 59,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 20/20 [00:24<00:00,  1.25s/it]\n",
+            "  0%|          | 0/20 [00:00<?, ?it/s]"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "loss_human 1.774059 loss_mouse 0.94303024 learning_rate 2.0000002e-06\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 20/20 [00:17<00:00,  1.13it/s]\n",
+            "  0%|          | 0/20 [00:00<?, ?it/s]"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "loss_human 1.0067647 loss_mouse 0.8752468 learning_rate 4.0000004e-06\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 20/20 [00:17<00:00,  1.13it/s]\n",
+            "  0%|          | 0/20 [00:00<?, ?it/s]"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "loss_human 1.0471998 loss_mouse 0.89318746 learning_rate 6e-06\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 20/20 [00:17<00:00,  1.14it/s]\n",
+            "  0%|          | 0/20 [00:00<?, ?it/s]"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "loss_human 1.010262 loss_mouse 1.02991 learning_rate 8.000001e-06\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 20/20 [00:17<00:00,  1.14it/s]"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "loss_human 1.111991 loss_mouse 0.84773445 learning_rate 1.0000001e-05\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "\n"
+          ],
+          "name": "stderr"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Cs0f0z0RcCfz"
+      },
+      "source": [
+        "## Evaluate"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8c4lNQrHkXSC",
+        "cellView": "form"
+      },
+      "source": [
+        "# @title `PearsonR` and `R2` metrics\n",
+        "\n",
+        "def _reduced_shape(shape, axis):\n",
+        "  if axis is None:\n",
+        "    return tf.TensorShape([])\n",
+        "  return tf.TensorShape([d for i, d in enumerate(shape) if i not in axis])\n",
+        "\n",
+        "\n",
+        "class CorrelationStats(tf.keras.metrics.Metric):\n",
+        "  \"\"\"Contains shared code for PearsonR and R2.\"\"\"\n",
+        "\n",
+        "  def __init__(self, reduce_axis=None, name='pearsonr'):\n",
+        "    \"\"\"Pearson correlation coefficient.\n",
+        "\n",
+        "    Args:\n",
+        "      reduce_axis: Specifies over which axis to compute the correlation (say\n",
+        "        (0, 1). If not specified, it will compute the correlation across the\n",
+        "        whole tensor.\n",
+        "      name: Metric name.\n",
+        "    \"\"\"\n",
+        "    super(CorrelationStats, self).__init__(name=name)\n",
+        "    self._reduce_axis = reduce_axis\n",
+        "    self._shape = None  # Specified in _initialize.\n",
+        "\n",
+        "  def _initialize(self, input_shape):\n",
+        "    # Remaining dimensions after reducing over self._reduce_axis.\n",
+        "    self._shape = _reduced_shape(input_shape, self._reduce_axis)\n",
+        "\n",
+        "    weight_kwargs = dict(shape=self._shape, initializer='zeros')\n",
+        "    self._count = self.add_weight(name='count', **weight_kwargs)\n",
+        "    self._product_sum = self.add_weight(name='product_sum', **weight_kwargs)\n",
+        "    self._true_sum = self.add_weight(name='true_sum', **weight_kwargs)\n",
+        "    self._true_squared_sum = self.add_weight(name='true_squared_sum',\n",
+        "                                             **weight_kwargs)\n",
+        "    self._pred_sum = self.add_weight(name='pred_sum', **weight_kwargs)\n",
+        "    self._pred_squared_sum = self.add_weight(name='pred_squared_sum',\n",
+        "                                             **weight_kwargs)\n",
+        "\n",
+        "  def update_state(self, y_true, y_pred, sample_weight=None):\n",
+        "    \"\"\"Update the metric state.\n",
+        "\n",
+        "    Args:\n",
+        "      y_true: Multi-dimensional float tensor [batch, ...] containing the ground\n",
+        "        truth values.\n",
+        "      y_pred: float tensor with the same shape as y_true containing predicted\n",
+        "        values.\n",
+        "      sample_weight: 1D tensor aligned with y_true batch dimension specifying\n",
+        "        the weight of individual observations.\n",
+        "    \"\"\"\n",
+        "    if self._shape is None:\n",
+        "      # Explicit initialization check.\n",
+        "      self._initialize(y_true.shape)\n",
+        "    y_true.shape.assert_is_compatible_with(y_pred.shape)\n",
+        "    y_true = tf.cast(y_true, 'float32')\n",
+        "    y_pred = tf.cast(y_pred, 'float32')\n",
+        "\n",
+        "    self._product_sum.assign_add(\n",
+        "        tf.reduce_sum(y_true * y_pred, axis=self._reduce_axis))\n",
+        "\n",
+        "    self._true_sum.assign_add(\n",
+        "        tf.reduce_sum(y_true, axis=self._reduce_axis))\n",
+        "\n",
+        "    self._true_squared_sum.assign_add(\n",
+        "        tf.reduce_sum(tf.math.square(y_true), axis=self._reduce_axis))\n",
+        "\n",
+        "    self._pred_sum.assign_add(\n",
+        "        tf.reduce_sum(y_pred, axis=self._reduce_axis))\n",
+        "\n",
+        "    self._pred_squared_sum.assign_add(\n",
+        "        tf.reduce_sum(tf.math.square(y_pred), axis=self._reduce_axis))\n",
+        "\n",
+        "    self._count.assign_add(\n",
+        "        tf.reduce_sum(tf.ones_like(y_true), axis=self._reduce_axis))\n",
+        "\n",
+        "  def result(self):\n",
+        "    raise NotImplementedError('Must be implemented in subclasses.')\n",
+        "\n",
+        "  def reset_states(self):\n",
+        "    if self._shape is not None:\n",
+        "      tf.keras.backend.batch_set_value([(v, np.zeros(self._shape))\n",
+        "                                        for v in self.variables])\n",
+        "\n",
+        "\n",
+        "class PearsonR(CorrelationStats):\n",
+        "  \"\"\"Pearson correlation coefficient.\n",
+        "\n",
+        "  Computed as:\n",
+        "  ((x - x_avg) * (y - y_avg) / sqrt(Var[x] * Var[y])\n",
+        "  \"\"\"\n",
+        "\n",
+        "  def __init__(self, reduce_axis=(0,), name='pearsonr'):\n",
+        "    \"\"\"Pearson correlation coefficient.\n",
+        "\n",
+        "    Args:\n",
+        "      reduce_axis: Specifies over which axis to compute the correlation.\n",
+        "      name: Metric name.\n",
+        "    \"\"\"\n",
+        "    super(PearsonR, self).__init__(reduce_axis=reduce_axis,\n",
+        "                                   name=name)\n",
+        "\n",
+        "  def result(self):\n",
+        "    true_mean = self._true_sum / self._count\n",
+        "    pred_mean = self._pred_sum / self._count\n",
+        "\n",
+        "    covariance = (self._product_sum\n",
+        "                  - true_mean * self._pred_sum\n",
+        "                  - pred_mean * self._true_sum\n",
+        "                  + self._count * true_mean * pred_mean)\n",
+        "\n",
+        "    true_var = self._true_squared_sum - self._count * tf.math.square(true_mean)\n",
+        "    pred_var = self._pred_squared_sum - self._count * tf.math.square(pred_mean)\n",
+        "    tp_var = tf.math.sqrt(true_var) * tf.math.sqrt(pred_var)\n",
+        "    correlation = covariance / tp_var\n",
+        "\n",
+        "    return correlation\n",
+        "\n",
+        "\n",
+        "class R2(CorrelationStats):\n",
+        "  \"\"\"R-squared  (fraction of explained variance).\"\"\"\n",
+        "\n",
+        "  def __init__(self, reduce_axis=None, name='R2'):\n",
+        "    \"\"\"R-squared metric.\n",
+        "\n",
+        "    Args:\n",
+        "      reduce_axis: Specifies over which axis to compute the correlation.\n",
+        "      name: Metric name.\n",
+        "    \"\"\"\n",
+        "    super(R2, self).__init__(reduce_axis=reduce_axis,\n",
+        "                             name=name)\n",
+        "\n",
+        "  def result(self):\n",
+        "    true_mean = self._true_sum / self._count\n",
+        "    total = self._true_squared_sum - self._count * tf.math.square(true_mean)\n",
+        "    residuals = (self._pred_squared_sum - 2 * self._product_sum\n",
+        "                 + self._true_squared_sum)\n",
+        "\n",
+        "    return tf.ones_like(residuals) - residuals / total\n",
+        "\n",
+        "\n",
+        "class MetricDict:\n",
+        "  def __init__(self, metrics):\n",
+        "    self._metrics = metrics\n",
+        "\n",
+        "  def update_state(self, y_true, y_pred):\n",
+        "    for k, metric in self._metrics.items():\n",
+        "      metric.update_state(y_true, y_pred)\n",
+        "\n",
+        "  def result(self):\n",
+        "    return {k: metric.result() for k, metric in self._metrics.items()}"
+      ],
+      "execution_count": 60,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "x80gX9LrhBU-"
+      },
+      "source": [
+        "def evaluate_model(model, dataset, head, max_steps=None):\n",
+        "  metric = MetricDict({'PearsonR': PearsonR(reduce_axis=(0,1))})\n",
+        "  @tf.function\n",
+        "  def predict(x):\n",
+        "    return model(x, is_training=False)[head]\n",
+        "\n",
+        "  for i, batch in tqdm(enumerate(dataset)):\n",
+        "    if max_steps is not None and i > max_steps:\n",
+        "      break\n",
+        "    metric.update_state(batch['target'], predict(batch['sequence']))\n",
+        "\n",
+        "  return metric.result()"
+      ],
+      "execution_count": 61,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "57fNitK9hzwd",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "947aaadb-dad2-4a00-ddac-d765f65d782f"
+      },
+      "source": [
+        "metrics_human = evaluate_model(model,\n",
+        "                               dataset=get_dataset('human', 'valid').batch(1).prefetch(2),\n",
+        "                               head='human',\n",
+        "                               max_steps=100)\n",
+        "print('')\n",
+        "print({k: v.numpy().mean() for k, v in metrics_human.items()})"
+      ],
+      "execution_count": 66,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "101it [00:23,  6.27it/s]"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "{'PearsonR': 0.0028573992}\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HY_wj95xiDtE",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "fea839f7-b6c9-46ed-aece-c56b02e9ea16"
+      },
+      "source": [
+        "metrics_mouse = evaluate_model(model,\n",
+        "                               dataset=get_dataset('mouse', 'valid').batch(1).prefetch(2),\n",
+        "                               head='mouse',\n",
+        "                               max_steps=100)\n",
+        "print('')\n",
+        "print({k: v.numpy().mean() for k, v in metrics_mouse.items()})"
+      ],
+      "execution_count": 63,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "101it [00:21,  6.54it/s]"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "{'PearsonR': 0.005183698}\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    }
+  ]
+}
diff --git a/enformer/enformer-usage.ipynb b/enformer/enformer-usage.ipynb
new file mode 100644
index 0000000..54990ba
--- /dev/null
+++ b/enformer/enformer-usage.ipynb
@@ -0,0 +1,1714 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "enformer-usage.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "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",
+      "metadata": {
+        "id": "hTGOLrbZxNHK"
+      },
+      "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'"
+      ],
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Eg8hcb45wqMM"
+      },
+      "source": [
+        "!pip install kipoiseq==0.5.2 --quiet > /dev/null\n",
+        "# You can ignore the pyYAML error"
+      ],
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MCDk7UQPG0Lr"
+      },
+      "source": [
+        "### Imports"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "NRI9KisU11bM"
+      },
+      "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'"
+      ],
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "g0F1A9AaCrkQ"
+      },
+      "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",
+        "fasta_file = '/root/data/genome.fa'\n",
+        "clinvar_vcf = '/root/data/clinvar.vcf.gz'"
+      ],
+      "execution_count": 47,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 141
+        },
+        "id": "OlE6JAVfI08a",
+        "outputId": "25e8b4c7-b2ef-4e8a-a81f-a032a3490bd9"
+      },
+      "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)"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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  ... sum_stat                                        description\n",
+              "0      0       0  ...     mean  DNASE:cerebellum male adult (27 years) and mal...\n",
+              "1      1       0  ...     mean  DNASE:frontal cortex male adult (27 years) and...\n",
+              "2      2       0  ...     mean                                      DNASE:chorion\n",
+              "\n",
+              "[3 rows x 8 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "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",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "flOUYxP7Fjvh",
+        "outputId": "bc48adb1-393c-408e-d699-5e70b183e2e9"
+      },
+      "source": [
+        "!mkdir -p /root/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 /root/data"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "--2021-03-26 11:38:38--  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  17.4MB/s    in 54s     \n",
+            "\n",
+            "2021-03-26 11:39:33 (17.3 MB/s) - written to stdout [983659424/983659424]\n",
+            "\n",
+            "genome.fa  genome.fa.fai\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "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",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4PjXFN5OcGbH",
+        "outputId": "ca4e8ca5-d388-4ba9-afb9-414f30e83f4a"
+      },
+      "source": [
+        "!wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz -O /root/data/clinvar.vcf.gz"
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "--2021-03-26 11:40:32--  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)... 130.14.250.7, 165.112.9.229, 2607:f220:41e:250::7, ...\n",
+            "Connecting to ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)|130.14.250.7|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 36564426 (35M) [application/x-gzip]\n",
+            "Saving to: ‘/root/data/clinvar.vcf.gz’\n",
+            "\n",
+            "/root/data/clinvar. 100%[===================>]  34.87M  22.0MB/s    in 1.6s    \n",
+            "\n",
+            "2021-03-26 11:40:34 (22.0 MB/s) - ‘/root/data/clinvar.vcf.gz’ saved [36564426/36564426]\n",
+            "\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Omj-KERcwSdB"
+      },
+      "source": [
+        "### Code (double click on the title to show the code)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "47E4AEgLx1VT",
+        "cellView": "form"
+      },
+      "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."
+      ],
+      "execution_count": 35,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "uLYRbOXDyA36",
+        "cellView": "form"
+      },
+      "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}}"
+      ],
+      "execution_count": 21,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Up1oCMFiPucp",
+        "cellView": "form"
+      },
+      "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()"
+      ],
+      "execution_count": 23,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zEwfoz3cwOzt"
+      },
+      "source": [
+        "## Make predictions for a genetic sequenece"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "WC-pgC35DgnL"
+      },
+      "source": [
+        "model = Enformer(model_path)\n",
+        "\n",
+        "fasta_extractor = FastaStringExtractor(fasta_file)"
+      ],
+      "execution_count": 36,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8u8Gt8WWyG53"
+      },
+      "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]"
+      ],
+      "execution_count": 37,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 440
+        },
+        "id": "_wL4c66ZXK0k",
+        "outputId": "3538b7f3-ebb0-48c8-f23a-1e8734253e81"
+      },
+      "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)"
+      ],
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1440x432 with 4 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "image/png": {
+              "width": 1432,
+              "height": 423
+            },
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gM2KwV8gwMNj"
+      },
+      "source": [
+        "## Contribution scores example"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "o4c2W_MjNBzp"
+      },
+      "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]"
+      ],
+      "execution_count": 39,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 224
+        },
+        "id": "y-s0yQMOXO7x",
+        "outputId": "a5cc620e-2680-4780-d546-845e9ca02740"
+      },
+      "source": [
+        "tracks = {'CAGE predictions': predictions[:, 4828],\n",
+        "          'Enformer gradient*input': np.minimum(pooled_contribution_scores, 0.03)}\n",
+        "plot_tracks(tracks, target_interval);"
+      ],
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1440x216 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "image/png": {
+              "width": 1433,
+              "height": 207
+            },
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iiDN_ScSv3sI"
+      },
+      "source": [
+        "## Variant scoring example"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hIaN9GKDVwLK"
+      },
+      "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]"
+      ],
+      "execution_count": 41,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 368
+        },
+        "id": "in1PyiWoXXjc",
+        "outputId": "664617ba-97c1-4ed3-8d4d-e5e8879ce474"
+      },
+      "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)"
+      ],
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1440x360 with 5 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "image/png": {
+              "width": 1436,
+              "height": 351
+            },
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "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",
+      "metadata": {
+        "id": "-vmj1MA3chRQ",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0e004c93-a817-4e87-842d-1ea04a26b649"
+      },
+      "source": [
+        "enformer_score_variants = EnformerScoreVariantsPCANormalized(model_path, transform_path, num_top_features=20)"
+      ],
+      "execution_count": 48,
+      "outputs": [
+        {
+          "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"
+          ],
+          "name": "stderr"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 275
+        },
+        "id": "1oGEYix3dRex",
+        "outputId": "c7e89a9e-faa6-4f8d-f018-db6bfa303283"
+      },
+      "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"
+      ],
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Done 0\n",
+            "Done 2\n",
+            "Done 4\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "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]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 49
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_G5cANX34SLw"
+      },
+      "source": [
+        "### Report all 5,313 features (z-score normalized)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "get8hogCySnt",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "efe3d5fd-faf5-4589-de79-b022a5c28ab2"
+      },
+      "source": [
+        "enformer_score_variants_all = EnformerScoreVariantsNormalized(model_path, transform_path)"
+      ],
+      "execution_count": 50,
+      "outputs": [
+        {
+          "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"
+          ],
+          "name": "stderr"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 339
+        },
+        "id": "Q48earqRyFa6",
+        "outputId": "fb26f901-7d3e-4305-9fb8-141046e56210"
+      },
+      "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 = {name[:20]: score for name, score in 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"
+      ],
+      "execution_count": 51,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Done 0\n",
+            "Done 2\n",
+            "Done 4\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "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>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]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 51
+        }
+      ]
+    }
+  ]
+}
diff --git a/enformer/enformer.py b/enformer/enformer.py
index 40e5ec1..7fa7b58 100644
--- a/enformer/enformer.py
+++ b/enformer/enformer.py
@@ -45,24 +45,31 @@ class Enformer(snt.Module):
   def __init__(self,
                channels: int = 1536,
                num_transformer_layers: int = 11,
+               num_heads: int = 8,
+               pooling_type: str = 'attention',
                name: str = 'enformer'):
     """Enformer model.
 
     Args:
-      channels: Number of convolutional filters.
+      channels: Number of convolutional filters and the overall 'width' of the
+        model.
       num_transformer_layers: Number of transformer layers.
+      num_heads: Number of attention heads.
+      pooling_type: Which pooling function to use. Options: 'attention' or max'.
       name: Name of sonnet module.
     """
     super().__init__(name=name)
     # pylint: disable=g-complex-comprehension,g-long-lambda,cell-var-from-loop
     heads_channels = {'human': 5313, 'mouse': 1643}
     dropout_rate = 0.4
+    assert channels % num_heads == 0, ('channels needs to be divisible '
+                                       f'by {num_heads}')
     whole_attention_kwargs = {
         'attention_dropout_rate': 0.05,
         'initializer': None,
         'key_size': 64,
-        'num_heads': 8,
-        'num_relative_position_features': 192,
+        'num_heads': num_heads,
+        'num_relative_position_features': channels // num_heads,
         'positional_dropout_rate': 0.01,
         'relative_position_functions': [
             'positional_features_exponential',
@@ -71,7 +78,7 @@ class Enformer(snt.Module):
         ],
         'relative_positions': True,
         'scaling': True,
-        'value_size': 192,
+        'value_size': channels // num_heads,
         'zero_initialize': True
     }
 
@@ -91,7 +98,7 @@ class Enformer(snt.Module):
     stem = Sequential(lambda: [
         snt.Conv1D(channels // 2, 15),
         Residual(conv_block(channels // 2, 1, name='pointwise_conv_block')),
-        SoftmaxPooling1D(pool_size=2, per_channel=True, w_init_scale=2.0)
+        pooling_module(pooling_type, pool_size=2),
     ], name='stem')
 
     filter_list = exponential_linspace_int(start=channels // 2, end=channels,
@@ -100,7 +107,7 @@ class Enformer(snt.Module):
         Sequential(lambda: [
             conv_block(num_filters, 5),
             Residual(conv_block(num_filters, 1, name='pointwise_conv_block')),
-            SoftmaxPooling1D(pool_size=2, per_channel=True, w_init_scale=2.0)
+            pooling_module(pooling_type, pool_size=2),
             ],
                    name=f'conv_tower_block_{i}')
         for i, num_filters in enumerate(filter_list)], name='conv_tower')
@@ -216,6 +223,17 @@ class Sequential(snt.Module):
     return outputs
 
 
+def pooling_module(kind, pool_size):
+  """Pooling module wrapper."""
+  if kind == 'attention':
+    return SoftmaxPooling1D(pool_size=pool_size, per_channel=True,
+                            w_init_scale=2.0)
+  elif kind == 'max':
+    return tf.keras.layers.MaxPool1D(pool_size=pool_size, padding='same')
+  else:
+    raise ValueError(f'Invalid pooling kind: {kind}.')
+
+
 class SoftmaxPooling1D(snt.Module):
   """Pooling operation with optional weights."""