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DeepExplainer with Dynamic Reference Functionality.ipynb
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{
"nbformat": 4,
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"metadata": {
"colab": {
"name": "DeepExplainer with Dynamic Reference Functionality.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/AvantiShri/8a3a0a03f4c4a578ee7909e3989467cc/deepexplainer-with-dynamic-reference-functionality.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"metadata": {
"id": "AE6yvKItdwPs",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"This runs DeepExplainer with the model trained on simualted genomic data from the DeepLIFT repo (https://github.com/kundajelab/deeplift/blob/master/examples/genomics/genomics_simulation.ipynb)"
]
},
{
"metadata": {
"id": "qapb9sqyeZqt",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"%matplotlib inline\n",
"from __future__ import print_function\n"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "5Yp2HR3UefAJ",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Pull in data and pretrained model"
]
},
{
"metadata": {
"id": "W7r-YMNMdu0q",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 785
},
"outputId": "c56279ed-2117-479d-c992-a8fb9065e129"
},
"cell_type": "code",
"source": [
"#pull in the relevant data and pretrained model\n",
"\n",
"! [[ ! -f sequences.simdata.gz ]] && wget https://raw.githubusercontent.com/AvantiShri/model_storage/db919b12f750e5844402153233249bb3d24e9e9a/deeplift/genomics/sequences.simdata.gz\n",
"! [[ ! -f keras2_conv1d_record_5_model_PQzyq_modelJson.json ]] && wget https://raw.githubusercontent.com/AvantiShri/model_storage/b6e1d69/deeplift/genomics/keras2_conv1d_record_5_model_PQzyq_modelJson.json\n",
"! [[ ! -f keras2_conv1d_record_5_model_PQzyq_modelWeights.h5 ]] && wget https://raw.githubusercontent.com/AvantiShri/model_storage/b6e1d69/deeplift/genomics/keras2_conv1d_record_5_model_PQzyq_modelWeights.h5\n",
"! [[ ! -f test.txt.gz ]] && wget https://raw.githubusercontent.com/AvantiShri/model_storage/9aadb769735c60eb90f7d3d896632ac749a1bdd2/deeplift/genomics/test.txt.gz"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"--2019-01-17 04:14:59-- https://raw.githubusercontent.com/AvantiShri/model_storage/db919b12f750e5844402153233249bb3d24e9e9a/deeplift/genomics/sequences.simdata.gz\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
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"HTTP request sent, awaiting response... 200 OK\n",
"Length: 629502 (615K) [application/octet-stream]\n",
"Saving to: ‘sequences.simdata.gz’\n",
"\n",
"\rsequences.simdata.g 0%[ ] 0 --.-KB/s \rsequences.simdata.g 100%[===================>] 614.75K --.-KB/s in 0.1s \n",
"\n",
"2019-01-17 04:14:59 (6.24 MB/s) - ‘sequences.simdata.gz’ saved [629502/629502]\n",
"\n",
"--2019-01-17 04:15:00-- https://raw.githubusercontent.com/AvantiShri/model_storage/b6e1d69/deeplift/genomics/keras2_conv1d_record_5_model_PQzyq_modelJson.json\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 3010 (2.9K) [text/plain]\n",
"Saving to: ‘keras2_conv1d_record_5_model_PQzyq_modelJson.json’\n",
"\n",
"keras2_conv1d_recor 100%[===================>] 2.94K --.-KB/s in 0s \n",
"\n",
"2019-01-17 04:15:00 (71.7 MB/s) - ‘keras2_conv1d_record_5_model_PQzyq_modelJson.json’ saved [3010/3010]\n",
"\n",
"--2019-01-17 04:15:01-- https://raw.githubusercontent.com/AvantiShri/model_storage/b6e1d69/deeplift/genomics/keras2_conv1d_record_5_model_PQzyq_modelWeights.h5\n",
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"\n",
"keras2_conv1d_recor 100%[===================>] 150.26K --.-KB/s in 0.04s \n",
"\n",
"2019-01-17 04:15:01 (3.53 MB/s) - ‘keras2_conv1d_record_5_model_PQzyq_modelWeights.h5’ saved [153864/153864]\n",
"\n",
"--2019-01-17 04:15:01-- https://raw.githubusercontent.com/AvantiShri/model_storage/9aadb769735c60eb90f7d3d896632ac749a1bdd2/deeplift/genomics/test.txt.gz\n",
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"HTTP request sent, awaiting response... 200 OK\n",
"Length: 2287 (2.2K) [application/octet-stream]\n",
"Saving to: ‘test.txt.gz’\n",
"\n",
"test.txt.gz 100%[===================>] 2.23K --.-KB/s in 0s \n",
"\n",
"2019-01-17 04:15:01 (37.0 MB/s) - ‘test.txt.gz’ saved [2287/2287]\n",
"\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "yqjwQ_CBd4ag",
"colab_type": "code",
"outputId": "fbae84df-34a9-4ce2-963a-d0f8e29e8cf5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 187
}
},
"cell_type": "code",
"source": [
"#read in the data\n",
"! pip install simdna\n",
"\n",
"import simdna.synthetic as synthetic\n",
"import gzip\n",
"data_filename = \"sequences.simdata.gz\"\n",
"\n",
"#read in the data in the testing set\n",
"test_ids_fh = gzip.open(\"test.txt.gz\",\"rb\")\n",
"ids_to_load = [x.decode(\"utf-8\").rstrip(\"\\n\") for x in test_ids_fh]\n",
"data = synthetic.read_simdata_file(data_filename, ids_to_load=ids_to_load)\n",
"\n",
"import numpy as np\n",
"\n",
"#this is set up for 1d convolutions where examples\n",
"#have dimensions (len, num_channels)\n",
"#the channel axis is the axis for one-hot encoding.\n",
"def one_hot_encode_along_channel_axis(sequence):\n",
" to_return = np.zeros((len(sequence),4), dtype=np.int8)\n",
" seq_to_one_hot_fill_in_array(zeros_array=to_return,\n",
" sequence=sequence, one_hot_axis=1)\n",
" return to_return\n",
"\n",
"def seq_to_one_hot_fill_in_array(zeros_array, sequence, one_hot_axis):\n",
" assert one_hot_axis==0 or one_hot_axis==1\n",
" if (one_hot_axis==0):\n",
" assert zeros_array.shape[1] == len(sequence)\n",
" elif (one_hot_axis==1): \n",
" assert zeros_array.shape[0] == len(sequence)\n",
" #will mutate zeros_array\n",
" for (i,char) in enumerate(sequence):\n",
" if (char==\"A\" or char==\"a\"):\n",
" char_idx = 0\n",
" elif (char==\"C\" or char==\"c\"):\n",
" char_idx = 1\n",
" elif (char==\"G\" or char==\"g\"):\n",
" char_idx = 2\n",
" elif (char==\"T\" or char==\"t\"):\n",
" char_idx = 3\n",
" elif (char==\"N\" or char==\"n\"):\n",
" continue #leave that pos as all 0's\n",
" else:\n",
" raise RuntimeError(\"Unsupported character: \"+str(char))\n",
" if (one_hot_axis==0):\n",
" zeros_array[char_idx,i] = 1\n",
" elif (one_hot_axis==1):\n",
" zeros_array[i,char_idx] = 1\n",
" \n",
"onehot_data = np.array([one_hot_encode_along_channel_axis(seq) for seq in data.sequences])"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: simdna in /usr/local/lib/python3.6/dist-packages (0.4.2)\n",
"Requirement already satisfied: numpy>=1.9 in /usr/local/lib/python3.6/dist-packages (from simdna) (1.14.6)\n",
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from simdna) (3.0.2)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from simdna) (1.1.0)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->simdna) (2.3.1)\n",
"Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->simdna) (2.5.3)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->simdna) (1.0.1)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->simdna) (0.10.0)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib->simdna) (1.11.0)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib->simdna) (40.6.3)\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "RyM5BPoLeVOh",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Load keras model"
]
},
{
"metadata": {
"id": "K9p4Jf-9eDxw",
"colab_type": "code",
"outputId": "d40f3e18-3922-4258-8f84-e0ff984e3e16",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"cell_type": "code",
"source": [
"from keras.models import model_from_json\n",
"\n",
"#load the keras model\n",
"keras_model_weights = \"keras2_conv1d_record_5_model_PQzyq_modelWeights.h5\"\n",
"keras_model_json = \"keras2_conv1d_record_5_model_PQzyq_modelJson.json\"\n",
"\n",
"keras_model = model_from_json(open(keras_model_json).read())\n",
"keras_model.load_weights(keras_model_weights)"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
],
"name": "stderr"
}
]
},
{
"metadata": {
"id": "JTEEYttzeSRF",
"colab_type": "code",
"outputId": "45f27120-bd48-425f-80d2-80c8d816c240",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 170
}
},
"cell_type": "code",
"source": [
"!pip install deeplift #for the visualization code"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting deeplift\n",
" Downloading https://files.pythonhosted.org/packages/54/23/938b1c8cdfaf2babc5b87f1c37099644d312e10e98a283f33dd88c4ce557/deeplift-0.6.8.1.tar.gz\n",
"Requirement already satisfied: numpy>=1.9 in /usr/local/lib/python3.6/dist-packages (from deeplift) (1.14.6)\n",
"Building wheels for collected packages: deeplift\n",
" Running setup.py bdist_wheel for deeplift ... \u001b[?25l-\b \bdone\n",
"\u001b[?25h Stored in directory: /root/.cache/pip/wheels/c0/32/2d/141e8cc6b98bc392a6a41025672b57e623c4a49cdb96c8ca47\n",
"Successfully built deeplift\n",
"Installing collected packages: deeplift\n",
"Successfully installed deeplift-0.6.8.1\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "4nzpEgBZepiv",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Compute importance scores"
]
},
{
"metadata": {
"id": "F47j3QN4eqQC",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"from deeplift.dinuc_shuffle import dinuc_shuffle, traverse_edges, shuffle_edges, prepare_edges\n",
"from collections import Counter\n",
"\n",
"#define the background generation function\n",
"def onehot_dinuc_shuffle(s): \n",
" s = np.squeeze(s)\n",
" argmax_vals = \"\".join([str(x) for x in np.argmax(s, axis=-1)])\n",
" shuffled_argmax_vals = [int(x) for x in traverse_edges(argmax_vals, \n",
" shuffle_edges(prepare_edges(argmax_vals)))] \n",
" to_return = np.zeros_like(s) \n",
" to_return[list(range(len(s))), shuffled_argmax_vals] = 1 \n",
" return to_return\n",
"\n",
"#This generates 100 references per sequences, which is a bit of an overkill;\n",
"# 10 is pretty good in practice\n",
"shuffle_several_times = lambda s: np.array([onehot_dinuc_shuffle(s) for i in range(100)])"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "kyaioAt8e5v0",
"colab_type": "code",
"outputId": "5259cc68-ee02-47f0-b508-db97d282c6d6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 598
}
},
"cell_type": "code",
"source": [
"!pip install shap"
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting shap\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/fe/93/9d41296314fe7ac42f1f117695d6d17b65b2c00790a88555522e52dbdfbc/shap-0.27.0.tar.gz (216kB)\n",
"\u001b[K 100% |████████████████████████████████| 225kB 4.5MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from shap) (1.14.6)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from shap) (1.1.0)\n",
"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from shap) (0.20.2)\n",
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from shap) (3.0.2)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from shap) (0.22.0)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from shap) (4.28.1)\n",
"Requirement already satisfied: ipython in /usr/local/lib/python3.6/dist-packages (from shap) (5.5.0)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->shap) (1.0.1)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->shap) (0.10.0)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->shap) (2.3.1)\n",
"Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->shap) (2.5.3)\n",
"Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas->shap) (2018.9)\n",
"Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.6/dist-packages (from ipython->shap) (0.8.1)\n",
"Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.6/dist-packages (from ipython->shap) (40.6.3)\n",
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"Requirement already satisfied: pickleshare in /usr/local/lib/python3.6/dist-packages (from ipython->shap) (0.7.5)\n",
"Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.6/dist-packages (from ipython->shap) (4.3.2)\n",
"Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.6/dist-packages (from ipython->shap) (4.6.0)\n",
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"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib->shap) (1.11.0)\n",
"Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->ipython->shap) (0.2.0)\n",
"Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->ipython->shap) (0.6.0)\n",
"Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->shap) (0.1.7)\n",
"Building wheels for collected packages: shap\n",
" Running setup.py bdist_wheel for shap ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \bdone\n",
"\u001b[?25h Stored in directory: /root/.cache/pip/wheels/64/d5/0a/e3b56a63e961ee9afe3c72f0b7cb9bae172c86bd63763c6655\n",
"Successfully built shap\n",
"Installing collected packages: shap\n",
"Successfully installed shap-0.27.0\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "LpaNYlhWes1k",
"colab_type": "code",
"outputId": "519d4370-5b69-4362-84c5-ca557c62ddf0",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 964
}
},
"cell_type": "code",
"source": [
"from deeplift.visualization import viz_sequence\n",
"import shap\n",
"import shap.explainers.deep.deep_tf\n",
"import numpy as np\n",
"np.random.seed(1)\n",
"import random\n",
"\n",
"pos_to_seq = {0:'A', 1:'C', 2:'G', 3:'T'}\n",
"\n",
"seqs_to_explain = onehot_data[[0,3,9]] #these three are positive for task 0\n",
"#Compute contributions w.r.t. the linear layer before the very final nonlinearity,\n",
"# as recommended in the DeepLIFT paper, to avoid saturation effects\n",
"dinuc_shuff_explainer = shap.DeepExplainer((keras_model.input, keras_model.layers[-2].output[:,0]), shuffle_several_times)\n",
"dinuc_shuff_explanations = np.sum(dinuc_shuff_explainer.shap_values(seqs_to_explain),axis=-1)[:,:,None]*seqs_to_explain\n",
"\n",
"#compare DeepExplainer calls that rely on a background generation function to those that use the standard API\n",
"indiv_bg_explanations = []\n",
"for to_explain in seqs_to_explain:\n",
" to_explain_seq = \"\".join([pos_to_seq[x] for x in np.argmax(to_explain, axis=-1)])\n",
" shuffled_sequences = [dinuc_shuffle(to_explain_seq) for i in range(100)]\n",
" background = np.array([one_hot_encode_along_channel_axis(x) for x in shuffled_sequences])\n",
" shap_values = shap.DeepExplainer((keras_model.input, keras_model.layers[-2].output[:,0]), background).shap_values(to_explain[None,:,:])\n",
" indiv_bg_explanations.append((np.sum(shap_values,axis=-1)[:,:,None]*to_explain[None,:,:]).squeeze()) \n",
"\n",
"for indiv_bg_explanation, dinuc_shuff_explanation in zip(indiv_bg_explanations, dinuc_shuff_explanations):\n",
" print(\"With standard API\")\n",
" viz_sequence.plot_weights(indiv_bg_explanation, subticks_frequency=20)\n",
" print(\"With dynamic reference\")\n",
" viz_sequence.plot_weights(dinuc_shuff_explanation, subticks_frequency=20)"
],
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"text": [
"With standard API\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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cBFu3afW1ct3K63gp8RKDuUFwhbH1IEqx56B+LNuiYlTYPrp91uPZ7qhzIbPV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\nRU5ErkJggg==\n",
"text/plain": [
"<Figure size 1440x144 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"With dynamic reference\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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gFMenhoEdyClWZl6eLl/Jscjfyr9s/BjvX38LqqIRL8bJV/IEnMGx57soGAVM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\nBY+VpZx5isiGK6gNt9nDmI0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/m+8M/4zebC9g0egN8C73hYywnKYljdQMj0CDiYcRunYo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\nYII=\n",
"text/plain": [
"<Figure size 1440x144 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"With standard API\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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+ChUn5GKQm35dISGOFb/fmhAIyg+PRuhL9bGyduVokBRMYPjEeCbDHhVNsuXE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\n",
"text/plain": [
"<Figure size 1440x144 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"With dynamic reference\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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f1v4bqlLinpTqwHT40QqlB/IdsGQJ8c2vQlMTVR+5hvh9D2POXwCAsXDx4d8v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\nhOggpBAkhBBCCCGEEEII0UH8H5XTGd6gvRnZAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 1440x144 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"With standard API\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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Xty1r6rLsz4FAYCTwIfCjYDAoowYKIYQQQhyArCyDlhYzEOR2d19X3lyOx+bBYXEQjofB\n4sJQbCjJKHgDMG8xvDcfXAX93p7ZIihsPnGP7swjVAR6BGb9AcZ+C6rdwMeH7o0KIU44ns0346j4\nD7rmJuTMwTNmQY80Mvi8EEfOwQwWvW+49qfAYqAOs+XQVcBzvb04KysDi0VLuS4313MQxRFHm9Rb\n+pK6S09Sb+lL6i49Hal6GzMGwmGIpmikU9dah8vmwqE5aIm3gCUD1eIwu3apGrgLzYRK6mssv98N\nfnfn41wPlHugpdpM0DWPUAlYvTD88rYXz+61zD6fy8zrGCX7XHqSektfKeuubDFUvw6AmmzBU/EP\nSBEI8vvdPZaJI0P2ufR1sHXXn0BQGWYLoHZDgfL2J8Fg8In2x4FAYBEwhT4CQfX14ZTLc3M9VFc3\n96M44lgi9Za+pO7Sk9Rb+pK6S09Hst5iMQWbzUUk0rOLRCgRwqE5cFgdVIerQbWTxIqWTNGPLIW6\nuhaoa8Hf9jhZ3YwzAu72AaG7FaQW8heCM898bnX2mm9DQ4j4Mfq9ln0uPUm9pa/e6s4dfBJnsvM3\noFG/oUfLApAWQUeL7HPpK1Xd9Tcw1J8xgt4EFgIEAoGZQFkwGGxue54ZCASWBAIBW1vas4CN/Sy3\nEEIIIYRo43IZOBxGyhZBoVgIh8XROUaQorSN8dO/QFBKqh1aU0z0Gm8E35TO56NHQzBo/i1ebC5b\nvBiCQfTCUQe/fSHE8c8wsDStAUWFGb+D/IUoJI52qYQ44e23RVAwGPw4EAisDgQCHwM6cHMgEPgq\n0BgMBl9sawX0aSAQaAXW0kdrICGEEEIIkVpGBthsqQeLDsfCOC1OHBYHoXgIaBvjJ9HUM3E/Ge2B\noHgLWLt0yYg1gWtk53NNg3Hjur+4sNBcVtcCcgdfCNELLbQFS3gHTLwLxt8Ood3w6dePdrGEOOH1\na4ygYDD4w30Wre+y7n7g/kNZKCGEEEKIE43NBjabOXPYvpbuXEp5SzkOzUE4YXaxMFQ7xBoPenuG\n5oFordkCqGsgKN4Idv9B5yuEEO1sNW+bLYBGXGO2CnIXQuH1PRMmk6hF2yGeRC0tAUAflg+aSnLk\nKDMgLYQ4ZA5msGghjpiGaAOlTXuZlDv5aBdFCCGEOOzsdmho6Lm8Mdpojg0EOC1tY/ZodjNoc5B0\nqw+MZFseXaaITzSDTQJBQoiBU+I1kHMaeMd3LvSluK7fsQPfyTNT5lH3yWqSo8cephIKcWLqzxhB\nQhxxmqagaQqrK5fz181/6XguhBBCHM8cDqiv77k8loh1PE7obeNrqHZINdhzPxlWX1vm+2ww0Qo2\n30HnK4QQ7dRYLQw+G9Qu7Q9chUevQEIIQAJB4lhhGGQEf4x39edwVj2H3+/G73eztXkjpaE9Hc8l\nGCSEEOJ4ZrdDXYrYTlTvHEE6aSTRdd3sGhatPehtGRY/BkC0pvsKqxs010HnK4QQ7dRYLTgGd194\nBFocWmuWkrH9f9Aa13XcUN73T4gTmXQNE8cE19Y7cO79PxQMrMkmmPQ1AEoaS6iPpLg1KoQQQhyH\n7HaDmpqey+PJeMdjwzBoiDTgUu3QvO2gt2VYPRhqBsq+wSTVYv7pSWjZYS5zjwZVxugQQhwYNV4D\n9px9FnYeSx5e8TA3z7n5kG7TVvE87q0/QItXYw+txTJmacp0Ml29OJFJiyBx1GnNG3BUPIOi2iBz\nEqre0rGuKlRlTpN7OLbbsAKtYbn5WO4SCCGEOAbY7Qbl5Z3P26eSj+tdAkEY7GncYw723LAR4gd3\nntSt2RgWN0Qquq9QNHN6+pYd8FrA/Kt4+6C2IYQ4wSWawZ7b6+pHVj1iPhg9mobla2h45oWOdQ3P\nvGCODzRy1AFsrwVX0S/Q4uaYapZIyUEVW4jjnQSCxFHnKH0C1YjDWa/CgnUw+Wcd6ypbKtENHcM4\nhNF6Q8e19ftkrbqIrFUX4d51b0fXs65/EgwSQghxpLndsGYNJJPm8/ZuYolkolu6XQ27MKxec/r3\n0K4e+dSEarjp1Zv63phqxbB4oW5d9+V6Agz9IN+BEEJ0UowktA9wv49oIkp5SzmxZAw0DX3MWPSC\nER3r9YIR5iDRBzBjmLPkUSytxTDtV7BgPYz44oDfgxDHIwkEiaPO2rQOJt4JeeebTdHzr+pY1xRr\nwm6xU9t68GMg7Mu5+0GcJY+iGHEUI4Gz4tlDlrcQQggxEJmZBhs3wp495vOSEtANHZfVxc/O+hnP\nLHwGn90HChiWTDNRU8/uYe/seoeVpSv3uz1D80LFEohUm4NEA+hR6NICSQghDp5utjIEs7tpwxYo\nWwKJGOsq16EpGpuqNh2yrVnrP4ER18L470HWVBh/+yHLW4jjiQSCxNGlx1AjJeZsAu0sDgAaIub8\nuTkZOawuWw3JJGrRdrRtQazvvo22LYi2Y3vnbdP+MJLYy/+Dkns6LFhr3inIPvlQviMhhBDioPl8\nBokEbNliPt+xA5qjzejoLJywkGsmXUN2RjaNkUazRRCkHCfos8rPaIzsf2p53ZoJ8SZo2gKhYnNh\notVcJoQQA6YAbS37W3bAoonw3nyoepeP93zMINcgPtzz4aHZlGGgte6CgmtAs5nLbJmHJm8hjjMS\nCBJHlda8CTXRCJ6xPdatrVhLpiOTYd5hfFb1GezYge/kmfhPn43v81fiP302/rmz0HaZF67V4Soe\nXfdwj3z+3GWZtfY9LKGNMON3kDUNsqbAhB8cvjcohBBCHIDMtt8sGzd2/m+INJA0kmQ6zJVOq5OG\nSAOGtW3cjaJHoGlzt3y21GwxZxfbXxev9lZFwYdg57/Mx1ZvzynlhRDiIBiKBnos5brdjbsZlDGI\nXQ27+pXXY+sf6XO9GqtEjZZC5sQDLaYQJxwJBImjytq0BsU3tee0ksC68nUM8wwjNyOXsuay/ea1\nqPhVntves5vXXzY80tG1zNK4EmXYpZA9uzOBb9LBvwEhhBDiIPQ2nbHfb945//3v4bnn4IUXoDJU\niaqouG1uAJwWJ6F4iKQ7YGYW2g3LruiWf1FdEX6nn221fc8qlrT6zAcl/4G9z5uPrV6INZgzhV0S\nhHmLwVVw6N68EOLEoTogWpdy1a76XeRn5vc7EPTwuvtJ6N3HS9tWt5XntpnX/5aGT1HtOZCRP6Ai\nC3EikECQOKoUPQrukaCoPfoNlzWXkZORQ7Yzm/Lm8v3mta5qLXWR7mMJtSZaiSYjLCp+FcBsLuqf\nbW6voxCyGwghhDi8lNZS3Ftux7v+S7hKH0s5SYHf72bCBLMFT1UVXH212fu5tLkUp8WJy+YCwGFx\nEIqF0DMK0bWe3R4iiQgtsRbG54xnaXHqaZPbGdbsngutXohUmVM8e8eBu7BzjA8hhDgAujUbojUp\n122v3878MfPZXrd9v/nsbtpFNBljRcWn3ZY/tfVf/HvLEwCosVrwjDMHp97nd4UQojv5BSyOMh2z\n7zA9+g0/t/k5nt74NH/49A+8UfRGj2kl951SclPtRmyqjaZo55gIS3e/hd/hZ32NOSOKkmiAjOFt\nm5YThBBCiJ7uXf7/DvxFySTajs5x7LqOZafVrSJr1XycDe9id2WRUfqPXrMZN07H4+k+U2ZFSwUO\niwNb25gXDouDUDyEYc1Ct/l75PHp3k/JtGcyzDNsvz+wDGvP12PNNM/JQggxQIbNbw5GD2Yrw3mL\nAQjbcmmKNHFO4Tk0RBqIJCJ95rOo+DXy3HksK3mv2/LNtRupCLXfMDZAs5gP9/ldIYToznK0CyBO\nPKsrVlEfqeW8kRdiKHYIl6RM1xBpoDnWDIBFtaArCvqYsaCbF8gdU0oCLfEWGmONjPWNZfGuN7gm\n8AUAVlYsZ1ruDNZXrQXaprBU2+5qtp8gwDwpDb3wcL1lIYQQaaK0eS9/3/Q4t5/0/Y7AS39ou4rx\nz52Vcl3s+Tlowy6AqfeAcwg09R6c8fvB79dpbu5sgROKhXB2mX7ZYXEQjodBUcxATuvObnl8XPIx\nI30jyfPk8cHuD/osd9I5oudCqxeqPzKnkO9nq9nPPlNYvtxCYaHOOecksViUnttKGileKYQ4num2\nHGhqmxVM1cwWhsBTwcXUttbylRe/Qk2ohuc2PcdF+Zf3ms+W2k2MyRzLxpoNHcsMw2BvcwkKCg2R\negZrTmitMo9dQog+SYsgccQ9tPY+Hlp3PwBx7zSMujXQWtkjXUuspeNxQk/0ObXkkp1vkOfKY5Rv\nDOuq1nQs31y3kTl5p1AXqTVbCik2iIcO4bsRQghxPHlq65Nk2bN4ueiFQ5PhYLDEgzDh+2YQCMDb\nc4KEdooC2dndAyahWAiH1dHxvL1rGIBu69m164n1T7BkxxLu/fBeluxY0mfxEu7JGF3vC7pGgHc8\nVLwFjW0DUEdSd+sAaGqCL3/Zwc03u9i0ycHvfufk2992puz2pmk9g0NCiOOLbuhc/tJFnc8tfih5\nCcLdx/t8ccdbtCZaWVK8hNZkK//Z8p8+811fvY6rxl1DsH5rxyD4qypX4rA4Ge0byys7XjKPZ41b\noLXi0L8xIY4zEggSR1RST7K9fhuVoQoiiQhJz1R0zWNOWduluWit6sWm2Xhu4XOsuHEFOc6cPpu3\nPxt8irrWWjZWr+fNXW8AZiBpVfkK1letJZaM8ae1D6LbB0NoZ6/5CCGEOHE0NcHy5SrbtikYhjmA\n84dly5gz9BTe27v0gAIXyZGjqPtkdUf3ZTC7MIf+/j3UvHlm8KefXZLz8roHgsLxMA5L90BQS9y8\nWaI7ugzi7BoB7tFUhaoIxUPUtNYQTUbZWLWx123pGSNJOjvzSNqHgm8KGAnY8bh5Z73qvV5f/8Mf\n2snJsfLOOwpPPgnvv69w/vnS4FyIE9XLRS9Q3FDEmspVACS8UyHRDFXLzATJVgD2NJXhsDjIc+fh\nsDjY07Cn1zy31m5md9MuPir9kIZIPa8UvQTA4p2vM9o3lgLvCFZVLCfpnohu9UFL998VHYPdG4Y5\ncHWsAQwDVe05YL84eHv2KDz4oJVf/9rGv/9tIRJJPSmCODbImVocUS8Xvcgg12BcVjdPb/0XX518\nA7pjOFrVMsiZ29FcdFXVZhxWBwvGLSDDmsHwzOEU1Rcxb8gFKfNdV7Wa+mg91IOKSm1rDX/77P8I\nJUL8c8vfAVi863X+Z+71UPoETPpR5wnivfkpZ0NR1dQHKmnaLoQQ6a2kROHuu+0MGaJRWKgSixk8\n9JDOLx/axc6GHdx1xo+46bWbyPD0fpnU42JWs3R0V26nF4zA0LZAzBzkub9dkseO7d6toSXe0iMQ\n1D6JQsI1vjOhajO7URs6F4+9mHH+cTy+9nGWFi/l+nE3pH4jikrSNRpLazEAScdwtGGXgM0Pwfuh\nfr3ZhbtgYY+XNjfDsmUaixZBXp65zOWCa69NvSkhxPElmTSPA3Y7ONt6rz4bfIqTh87lic1/ZfbQ\n2cT9Z5O0D0Nbe4c5+HzpawCM9A5j3qgLuX/B/dy66FZKm0t73c5vV/6GcCLEnzc8BMA/Nj3O5WOv\n5Kkt/6Ql3oKmaOjoPHDun0k68tGqP4BBp3f8rsAwYN1dULca/CcBOoRL8Z3ac7y2uroWudY/CPfc\nY2PLFivXXafidpvfi/xeJm87kM9Y27EdkjpqaQn6sHzQ1B7nWnFw+hUICgQC9wGnAAZwWzAYXNll\n3XnAr4AksCgYDP7icBRUHDmpdjjgkOx0v1/1G0BBU2t4aO0f+erkG0h4pmLd9GvIPQPsOQB8VrON\n3IxcMqwZAGQ7s3udQj6SiBBNRrl01BUM9xbwxMbHeWPn62xv2MZwdwGXjL6UlRXLaYjUEc25kIwd\nv0QtfR1GXtt5goi3wGf3mIPZWT2guvBNvSvl9lIevJJJtF3FPQ9UI0eBJjOtCCHE4WC0HYoVJUVg\npk2qi8077nDw+c9b+PrXwWoFUNi1S+OG179Beaici5++2Ez35h08sOAhXnzRwqpVGg6Hgq7D4MEG\nd9/dc/ygurqWHsvivtkY6+9DaS3b7w2IdnPnJvjjH22AQlaWTjgWxqF1BoLap48HiOXOR99+N6re\nStKSRbBmK3E9zp8u+hMFvgJWV6ymKlTV67YAks7RwFsA6M4RYPeDb7J5B7/qXbOlUQoNDRAOKwwe\n3H2519vn5oQQR9MhuGbduFHhoYfsTJqkMniwQjwOpaUGF337XdZWreabJ32TP638E/dqvybD5iPh\nmYJWsxgWd46jtrelglMKz0MFr5mSAAAgAElEQVRRFIZ4hrC8dHmv22uKNjLWN45zC87ng9JlhBMh\nIokITbEmRvlG47K6WVe1hs+q1jPXPRk2/wYGnW0eywC2/sGcm+bUJ8CWDU3boH6t2TLT0v+x4ERq\nS5dqfPyxjddeUzpuCnT1wKcPUNpcyr3n37vfvFaWf8r66nXcOPWbHcvU0hJ8n7+ShmdeQC9IfT4S\nB26/gaBAIHAWMDYYDM4NBAITgL8Cc7skeQC4ECgF3g8EAs8Hg8HNh6W0x7teDszAEQ8oHOgO98Nl\nd5BhyeCnp97TuVCPocbrQI9jaE7qdHPqxxmDZ2JVbSxv2M7OxiLG5l2LvfwZ1PcuaovSQ1moipyM\nnI6ssjN6DwS9U/I2Ds3J/ef+CZfVxWfV69hev41QvIVzCs7jntN+xatFL3HnB9+j2TYEd9bp2D/9\nKkSrOsc9+OSrMOu3kHM6RCogGTWb8Kv9+8x7GyS07pPVhz5qfQx9T4QQ4kgxDDPgU1MDv/2tnUmT\nNPLyFFQVwOALX0h97EsVvC8qUpg9uz0IZBo5EuqX1HN6/ulcNPYint30LEX1RXzxizBtmoO771YY\nNgzicQgG+1/uRObJJBwFWHc+CYHvdt6A6GM69tNO0xk2TKe0VKOgQE89RlBbIEh35JN0jkINbUJ3\nFvDOznfIychheKY5Q2aeO4/i+uI+y5h0T+h83D54dM6pnV05VJt5J38fw4bByJE6y5drXN5ljNdg\nEAKBPjd5eCRbUfRWQMOwuIGeAUK50y9OdIfimvX22x3ce6+FC7s0amxqgnlPfY+4Huf5Lc8TTUa5\n4ZUbeOri54lnnYa9ZnFH2hYd9jSXk+81m4zke/PZ3bibcDxMRorthZIhLhl9OT86+Sc8vPZ+nt76\nL5bufguv3cuShe/htDg5+5nTeLfkHWaMuR57xbOoyy6B3HlthdsC424FxyAzCNTeMtOe3aNlpvQI\nOHBFRSqjRqUOAq0uW81jax7Dqlm5oPgCzh11Lkq8EXv5K2jJRhRVRU/q6N5JlNhGccf7twM6M4bM\nYE7eKd2+k10nCsLQUSN7UWK15sQJthyUjPyOyajbSb31rj8tgs4FXgIIBoNbAoFAViAQ8AaDwaZA\nIDAKqAsGgyUAgUBgUVt6CQTth7ajbbybLj/m24MvqTQ880JHmsPdLK7XHS6FeDLOvGfm0hxtJmkk\n+bB0GW+d80u8VU9hKVyI4hhkNilvLudPFXUEcgJ8ctPHKIrCqY+fyvO7nubOmXcTHXQpzoqnoXIp\nhmsMZZEw2c7OATBznDlsqNqQsgwry5dT4B2By2o2vc/3FLC5dhO7Gos5Oc+MWZ42/AxiyShv717C\nVSNuw9K0Fm31d833656ApscgI98MAr3WdvV61huQMxvC5ZAMm/2NY/VomQvAktP9QDN6NHWfrEbd\ns7tbAK19avt9pWp1pWWswaZGsThzzABUqAQjXEbcUUh08KVg9QGg7tyJr5eZaQ5L4OkADbQJZ6p9\noz3Q1d98j3oz0uMoWJeqlcWBnFQPpD47NzDA708/8+1t2WH7rmzbhlbddFw0b65rrWVL7WZOHXY6\niqIcln0ukYDvf9/OOedoDB2q4HCY3RAeeAAmT1b57ndT70qrSlcx0jeSHFdOz5Vtzj8/ycMPq9x5\np8Lo0dDaCq++V87Ohp387bK/cfn4y7FoFh5b/RiDSnUWLlQZOhRU1ewCMXz4AbwRRSE84rt4Nt6C\nWrsaXPt/sc0GU6aYgaBAQGdtIoTH5ulY33WwaBSFhHcq1tAmEq4ARbVFDHUPRW2b7SvPnccnez/p\nc3vRQZeSUXwvJCPEss/FBTDpx7DnOWgpMruJpaCq8PWvx7n1VpWmJvOzrKmBv/7V4OWXj9w4EK4t\n38U+aCZK5ngUawa0FGPUlkDO9/H73d3S9tYlYaDHukPhcB1vj/Rx5qifgw9Eb+frpN7x/2i9jwOt\nz/5+7u3jmfX3mjUVjwfKysxjZ3uXsNLKMI2RRn517q+4Zc4t/OSdn/DmjjcxDIPWgm9jr3wZa5M5\nbtCyrGuoL36OWUPN69mZeTOpba1lfdVa5pLbbVs14Rq21QW5YfI3ADg571R+s/yXvFb8MoWZoztm\nVByVOZrPateTmHU70cGX4Sx7EvY+j+EajVLweSh+ArwB8E6CM18n/NZCfvfQTEaOB7fbPJ84nXDB\nBa6U77lhxVr0ePKQfx8OpDWrppnj2UWj5jmy/ZzUHG/gJx/+iC01m7ls7BXcMuM7GMbAj8GpyqYW\nbQddR91rzvqsD8/n62dqXPz8ZB55xMKVV0Jmpvn9yBxcy7x/zMPv9GNRLHzuqc9RdM2jDN75F7Q5\nfwJXIbTshObttA4+mzP/OAKn1YnT4uTKly5l7TfWkqsN61GGjKJf4MhwoQ45G8XrN4/5LWtQht3S\n4yb+AXdDg/Q5fg1QfwJBQ4DVXZ5Xty1ravtf3WVdFTD6QAux57OtlHz8IOPmno+ytxxyQYlW8/3V\ny1iasdOcAVAB1aIwPzmBldEgVRngTCq0qgkKrC5u2j2LU0+aSNawMSh7KyAHqsqrKQ9uZer8hSh7\nKyEHlFg1b66tYvrJ48gZexJqIoGRqKR4zzZu/fTv7PWAM6EQ0eIM0ZzcVD+X008ez6CJp3WkVcO7\nefPpjZS6vovX6cTitaJHw6ws/yOf+D+iwDec7IjGOkoZgZvTNpxH1owv43bY0Zwqhq5Ts+ZJThkT\nZYRvBK6v3kn40TvAr/POLecxa+rpZH7j54QfvQNjWC4bNpbhLnmdUa1jcX31h2baXPjtfVlcemGU\nITlu8Gahxsp5ZvUqnoivoMFuYE9AVEsw0ZbFf+0cx5xzzyOjMY4xfAhKpIJVGx08/YmPr19Sib91\nPQm3C6sGH25XuHCa+YW3hTZC1UZeX/ERv9/zEjV2sCchpiUZbfVAHbi8Bg9mXoIO/Kj5VX7yzB/4\nst3N9MxyrM4IRvNGbMFf8HawkJOyhxB94vcAzE3Y+ejdj/jx/y7iqqu+xVQqcGRU0Kh/gbIt/2as\n10/rO/9CKSlnaGIXwb11rHrsRk6dtQAAe+3bYHubbWsWM9ZtxbXhFZS95UxLNrB57zaSyTrODidx\nL3oU9yCYas1kyQMb2Os6hYvm3kWAh9FtNna2/Ig3Ftfyleafkl0wDrXwBxiKjU+eeYqp09/FN3mh\nuatEajFW3sI1wWEUexLYEhDXdLIsTr7ZcibTJ+YzNtsMIlld2zGs23n8zipyZnwBT4YTxW4eTBuK\nljPV8zFjBo/prM8cgxXr1jLrquuxKC6w2CG5jeA7i9Dy5lJ4+lgUQ4doOapSxuJbP8fscRPJvvVe\nQn+/F3Jg+/ZKEm/9L+NDC7p93x993cEZp1nIz8tGtdkxlHqWb9zMc7Ur+dLUKymsh22uSt6qXUd4\nb5L3vXtBNwAF1aJwnT6b81qsTDrncnPfats/f/ligOkjK5idX441w4Fhi1Jb1ULTzkZmTZnb+b3O\nhZ8tWs3r9k3ELApqQkfXdD7HFGoWf4cbfngWRiIJmgJ6krI3/8qcSS6G2rM78mh2weZP1zJn1vm4\nvta5D6xeVcqG4AxseefgtqoYNoVoYzXDyn/HqbPPwn3DXR370SNL/5d/G+/RYlWwte0b0+x+MiuS\nTJ1wChcmCmj2RfhX7WoyKzKIOhr54cxvYC+rYb2rlNdrVlK5s5a5k05lfmIk0aHZ/H3vK4wuH8xU\nu8roWfM6Pnd1VxDnLX/tcax78pSR/HF+mDo72Nr2o3FWLzfvmsScs87EHTI69s/tRZXElXoC5y1E\nS8YxElUo0Ur++etSWlyXM2hQHla7BawGlcW7WbXezrXfnomSSGBYFBQjyT/fvYYPfFvN+lTMBgjz\njUmsad2KPTuXKbqfoFJFPBnh1sYAc887C+/IU1BQMCKlVO4qpeS95Uw999Judf/gIj+e/HmMmz4C\nInEUK1TVF/Ng0Rep8CaxJc19Y5Bm57/KTuOMCfkMsQ/uqM+Q22DjJ2uZPesC3F3qc8mKRiYOtzPc\nnd+RNu6DW/97Fl/+wTlYVVAsChgGi9f/ljcTT9JiN88FYTXGdHsO3yzOZ3aKY6hLa2Z07rhux9CP\n3tnESRNn4fv2Lzv2o49WtrJk19lc8oUpKEkdNFCMJA3LbmNn7Bo8rqHYsr0QD7N97yJKsp7lv6Z/\nniG1cTZnlLO0bj11JWE+9FagJRV0q4YN+GpyLmfHbIwvnGm+t7/cATnw9st7mXn+GfhyhprNXRJV\nvPfZWn6+4z9UOQxsOsSUJAVWNzdWn0kzCxmSV4jNacOwGRSXrOC5+I/B72Gc7mOnWoumJ5hYN5R3\n3cXENBU1kSSpGSxgPK1NlXxtzvVMarBS7wvxWPlSMkoyKfHX8tWxl+GsauBl9TNszQZzi3VOuvRL\nKKWdx5PHNuzhPnUJCYuKU9doJMLVGRNYWOxl9sxzcX/9xx2f+8NPLCVsO4+zFoxC1c3PMpnQefjn\ny7jxh6fhsCrQdr1Wvek9xvhXMPqUs9CSSUhUYUQqcVcOorX+MlpdLpIxC8mEzneueocXng9zz4/O\npmCkA0VT0IjzXsk8XncU48JGXAMrcGPoJowSH1ee14zHkkTJiKNFq7l1QpDnN/6UO748Bry5xCOt\nKFN/zpBBGmftrSey/Hdc7K3lsZYafnDeWbz8t3tY+c5E8gqcJA2dnbtewci/n7tmfwN/RTPVWS08\nWvoWY6oDXDCmsGOft9e9TXHlKh75UxY3Xn03Ixv/hcWxlkTG1dS8+hN08nlr3QJqd+uoHp3pwzcy\na+hTOEZ/iVtmuKnZeSHfmPk0X9pYzGCPt+OcmGVswF5j5XtXvMbVXxjGoKYZjBm6nq075xHc9gOG\nORIdaUcZe/iovJyP7r+Os8+4stv584F/egk3+jlt7nAmxuZguDVKPtD52xPvcuWldka3LiAv5y1q\nQpfw6WO/Z0pOC6O8hbiBUNFLKC1wKX/D8+Wf8fQfp7KnwYff08oPP/8MO55cyZbqL7F+RQYJp0ph\ndiVDo08z84KLsNeGO451T21cxbbEbn5y8s1YS6vYk1nPPyveo25nM5dMOYezk8Mx8vP4T9kiNu2s\nQqGBu+d+B620kiZ/Kw+VLmFKUR7z5leTaWSh2F1QH+Szxa/x5K9P47KvTDGPi6q5Lz/4q+XMv/Z0\nJk7OwGJVAIM9uw22v/0CV5yzDY8WR3UlUKPVfLKqiTkXnknWsHHm/hmrYMWmDfxo478IeewM0TOo\nUlvwKipX7L6E02cNZfjYQizxKIbaSHVZC6+8rHPlVVa8ThuqPYISqeTev40lZ9LnOPWsQagYoEJD\n005+v2oBe91JPEmNkBYjW7Vxc2wGNZXz8Y04A6dmoNhVovVVfPzOdj73jflY9SSGBVAUyj96ktmF\nSQq8wzqPoVk6177yFPn54zhbz6c6o4X3m4sYVuvnU+s23O4sBsVtFFHLCKsT+7O/4Eu3n43HpWKo\nCopiYG/+kBVL12PNuwiv3YLiUEiEmvj43a1ccN05ZLqsGDYr6Anqi9dSGHmJCcPHdzvefu2VV9iT\nlWCulk/IbWFzczEXt8xmht3PjPMuxBKPALVo8Rr+/es9zD5vNn6fDwUDqOOtjet4rGwxs/NPYmKr\niw22Mopa9uKrdrIms5pC1bx5uFup44z4WFZHtxLxuMhPZlCqNeJB5aT1N3LOpBYmj7JjdXkwtGbq\nV2xlws+e7XG+fvmWa5g2czS5qq/js6yyZvCX32Ry3ZUN+JwKakYCNVbF/St38qprE1GLgiWhk9CS\nnKuNpfLZH3LLPWehKUbb9Dw6RW//lsnjnYyeMQ0tFgGjllBDGdcv+z/2eI2282eSbIuLG8pP5owR\nORRkjuj4LI0cePPZPVS4/otMjw+Lx4qRSNK44wPmjtjIKP+obp/7la88xWZ3KxYddIuKW9G4OXIO\nMwuHMGnYtI5rVmzbue6BP7PG3YCmg27RyFBULoxM4kM20eK04ElqNCgRxlg9/OaSIfzzpXv4ZPF4\nCid6iUbirGq8B21IAwvr7UT+8TsWuqM8W1vEf3/t75wyaRRz/ZdQ6G+mxTKSRZtzGGP4yFu/nEjp\nIobnxJlkz2bHs39l2oRJtIee7bVv89bWNfgjSS5sbMW96FFOzUkw3pbJnk0fMt6fh+uzV1BKypke\nb+WN3dX8+K9vcMXVNzCDMpyuUhqTl/PEoyOZmn0Sc8tux+Wyo6NQpl/B9uXrmDD5FDKsGppVxW7R\n+X/fegpL9mmcfOaQjuuAWKiZ5JK7OP2Us7pdtzz8bAHZRiVnzLXg0lSwh0k2V7L1g73MufQLWCpr\noe2exL/fcBBMXMTl1w7H2vYL3GrVWfLw41y2oJlsjwPVGUXXkyxZDKvqruSqL43EqgIqWK0Gd317\nHd++awbD8y1YrCqGofPWp4/yx5ofk+cexATDxxOr7uO1NQ/zg9azOfPiS3A3JdALhqLGytiwpoHi\nz3Zw1vnjcagaWJrQopUs/WADMy9biCt7FIamobSWsGN9MWrzNkac+zVUVIiWQayWt59bz5yhWeTc\n/gcAwn+5A4e/jv/7/B7+vOR3/OcvWaguO4VDWiidchnneAp52nc9iqLwTct/+MY//8yticHM9byO\nIzMPQluxbf8N122fxvSMbF6Zdjfa3kpus77Ajx/9At+wjOW0ky7udu569bUQs6c0MtSyE4utCqNx\nPVWr7+eef/6ZW+feyNhag3hOkgdL36BgTy6nnjSGIVPORtUNjFg5sfoyPnh7OWdc+y20vVWQawZ+\n33r1IybkWcj3FHTZ5wwueeVJdnjiWJMKSU0nU3NwZngc72qbabEaWBIGcS3JFFs2t9cXMmvhjdgd\nHnSLFaW1hGVv1/HOxpNZcPUklISOoRooGNz33plsyqrHpiskLAouReWm6JmcYtEYN+f8br/n3n9n\nO7POPh1Xs95x/lRi1ex0DieSeRp5Ezpb9vaXYhh9R8gCgcBfgNeDweDLbc8/BL4eDAa3BQKBU4Hv\nB4PBK9rW3QiMCgaDP+4tv0QiaVgsPW/hvbvkUSpbKvGU1UAuDLPA9Km3mCt3ts3ylAuE9sLgMzqX\nty17N5xgQ6XZYsRX0QC5MM2TTb1nEhsqN3Qs8yaa8BVc0j3tEJiWlc30gLm9+I5tWAZZUMKlPfMd\nAvlKAf/z2Le46+4sbBYNRQWnw2BOVimbG4t4oeRN9lZs43Mj53KGZTB3PDOVk073Yo0bKDbzR0y1\nfyfvF61gwlid4Q31KIN05mTnUZM5k5V7PsVfZi4zNJWTfYNZ1JJASeody+f4BzNiz5s4p3wbm9WD\noarQuhdrogWb/bJePx9fRQMNQ3z4kg3MdcXw2/eQPf17KL4pUP0JrLiB1yb+mZU1JfjL6qkbmoXf\nqOdk32BOyfxCj3y71UWXOno3nOioz5Y8PyMyNE4ZtdCM0vaRtnloDoPdQzi7cF7nl6PL9l4bPLjb\n55NpNPGV0T9LmbY/35OGIT6mDp4GGN0+H0VRmDJoSre0jUO8zMkZ2v19tN+0CO3lp4nd3eropKw8\nnv3n2YyckIFH1cy6Vww8msa/G//DqBE6BY2ddd9aN7h7GdCZkzOU5dVloKjdvqt7Axd0fA5d62j5\nlsoe+4DLUoo97yQybNko6KDXYE8087etm3mgahXxhI7VYuGH+edzw8wf77c+2/fPfONt/DN/gDL8\nUojWdLSkeq3wf1gZiu23joyWvSyuh42VQTJrWsGv4E42YTl9IeuqVmBVunzfU+wbmUYTHz93K7nD\nCzjvHJ95jaVAhkOhwre0x35024jbUn5PNtvz+Mqnd1IdqkLVVOYPOon/LZjPJcEnWFe/hWzVTb3e\nzC8LL+O0UVdz9Ye30RBpQNUszPQW8sLEG3h0z2fdP/eMLOp9U/isfB2ZVU00DvKSaTQxzZPN9Enf\nMm877Wf/nO72EXKPoCXRhKeuGcMXJ8ea5OGXryJ/4iAGezIwUMACHqvG1u1vYfNXMt7Y/zG0OnMS\nL5e+zcfFy5g1ZCyXeUeyXfemOC7mUG+b2OMYGnNcw/qSMA6rH0/EwMgwGGzRmDlyKoMHg7+x+z67\nonRF9+NXL/WZPfraHmln+/OoKx3OxspiMmsiGH4FBYMJLjezpnwRqyNGuHgrvjw3Sri0xzGi12Po\nPmXY3350yx9u57b/zqFguA1FM4/jmmLw6p7v8/fiF9DjMWxWKz8fsYAvTb+z83MvbLvb1fZZlNWX\n4N1bjTJIZ4RDJV7joiVaja8xjKqCnpkgW0swbvj1ZivAkrKOz/K2p8cxdJSf/NwMc2wcC7itGq07\na6nwraZCeZM5Q8cxzzkIf/78lN/3hxv28MvNf8JIJEgqOpdmT+FPZz7Crat+ybuVn5KIRyj05PGv\nwPVs1d096v6Ks37fr2OzoalM1aemPC+XW6exoXw7mTWtKNkqBgaTXTa2G9VkZjjJbAqjZCYYkeFg\nT2I0G2p34Ktu7dg3pnlyeD+c2CffHKZPvbnn517xD6IZCeyzfg32QRAqgsYi3g3W9djneuSRYv9s\n34+muLO5L7SbV0qXMtKay65YJVflzuDyeXdS0VCKd695j6y9nlvrBvc4z7348kSqG6dy/XVeCgrM\n1k5790KG5dHun3uyhSvO/G3Kz33fY4eiKHzn5O/0q44yjSYK1DP7fU4cdfL8lPty13NX+zmxQC/g\n5geu5+rLx3HyyRY0DWprIS/70R6f+2inm2+Wv0NNtJ5x1iGsbS3mukGzmTH0XL637l4mO/Jp1hKU\nhSv5+/iv8O2i57hpzNVczFjur3uJLU1F/GbinWyu3tzt/Dk7eyiN8UFUtDThrmyEbMizGDR5C6iN\nVpJVUdNRnxOUnQzzNmI79c8o9sFmC+CWnbwbrO9e923n4Mljv0lR825WbF3K9OGjCSgKW5s3odt0\nvL6R2GM6htqAN1pKrHYDztm/QEOBaBlKrIFPyjJT7Bvm9y+uxynftoacIV4yojXc9vQ4vIOGMeck\nH6oOqApuh8raNe/RaOh4olYUlwqGTmIMbG1eQV6OTm5VZx2NsJ3DT9b/kY2VG3E57Xw1/wKuzwpQ\n5ZvIsqqVbNm1mvmFJzHDYmdZGNaWbiazKgzZ5nm1ZM05VGqnc+012VgUBVRwOhQ+W/syxXUhMltt\nRDI9OJItDLWqvD9kC36PzuCazjLMcl/Fa2Xv8p/Nz+PNGswXB83iDIvGospPGJSdjyeugj2EX4mx\npzZMxKqRbctF1WxgacJHlF3R4fxv6TKqmqvJ82Rz59AzmTn2erY07eD1DS+AAhePPpkJikIkZzYb\nGoJ8tPVtTs4fz3SLnfq65/COuxRnzixQVIzWEtRQFR+UOjrO14pfx2O0YD3jRlaUr+72fT/DY2VM\n1bs4T38M1eaD0G5oLUdNnrrf/bP9GDpk6Ck0hLaQ5c3GHVNR7C341SR5rgvAORiqYh370WvueI99\nbrpnOL+4+2q+dVs2TqvW0To906OyOP67Hvtnt2v3Luejfa9Z+0qr555GY7yZ0u1rKBw2GFesNuV1\nS3+Poe3HmY5r/V6OU+3H0B5l2/c6vx/HxX2v6VMd69qPt778i9hQuQFdVzqWT3VnUz7j/B7H0Hmt\ntQwqmIp9zJdRNBeEijFaSnh/R7THcXGaJ5v3w8nu+707i1x1Ha7J38Kq2jFi5rT3Nj3MI2t39zhG\n1HsmHtDnvmf9Mr5y57/5x71fhFw4xZWg1Z5gUOGFWGMxDL2GDD3Eyj0xNlR+hq81z8yXho5rwI7r\n/yEwzKGwN3B+x7VMb+c58zpU6V6utuPtvnXUOMSLT2nmy7Pv6mzNc4DnrvbfaK81l3J/0T8Zbx/G\njlg545y5/M+429lau7Xzs2w7jtfbJnV8Pi/+6hKahnjJPq3ndWjK7x/s97dmRx3RwLSsbN6v36fu\nPdndr9O77HP371zTI23XmEZn3WczfeEDpNCv5mD9CQT9HCgPBoOPtj0vBqYFg8HmQCAwEngqGAzO\nbVv3M6A2GAw+1Ft+1dXNKTeYm+uhurq5P2U+Zh1I074jXY5UZXDsfgR3ZB3KSQ+AI8ec0ja0C/Iu\nSDk2TqqmdcdDvZ2oBlx3egxr3TKs0T0oqgUlWonRWkUs/0vo3undDkHH6j6wP9Xhal7Y9uz/b+/O\nY+0oyziOf89t6QIqLatgEIzIg0AgLFWQfVMRDSlUNBbFglABCbsCshVQiVvVCkZFQRADMQTEsC9a\nKmsTE1Axj6CiYStUWlrK1vYe/5gp3F7Oub0X2ntmer6fv+bMmZ68t7/znpl5ZuZ9OXq7Y2k03vjM\n3t5eenp6BviXq459rlqG8l2rUnbzX5nPuDHjOt2MYdFYPJ+RL8xmxJJ5NJpLWdoYQ++4HWmuuemg\nxhIYKLe5L8/lG/dP4+ydp7HO2NaPT7UzceJYTjllJBMnrnjbus1ic/rpo9hkk9Gcd97gtp/7vwWc\netdJ3PPkLE6Z8DUO2eJQAG79101ccP+5jBoxmp9/9HLeP35zpt5+BLc/fgtbrrMVf5n7EOfsciFH\nbTu15ecOqc/1vsqIF5OeV5+i0VxMszGG5rhtaI7eeKWPObEyjher+ijbymjHcP9WDvn/sncJjaUL\noLkUGiPpGT2+uGNsKJ+xClThO1Gl/dywaTZpLH6extJFQJPmiDXpGbNBy1Px4ezjQ5n8Z3XK7dlF\nz3L8XV9m6rbHsM+m+7fdrlaPsA6gVXbrr//OlVYI+ggwLTP3j4gdgB9l5m593v8bcCDwBHAfMDkz\n/9Hu81bnQlAdjXr6GkYt/DMjx21Bo9FDb28vo7Y5seW2FoJWL2ZXT+ZWX2ZXT6sqt0ceaXDJJaPZ\naace1luvGPdhwYImJ544uAsxVXbppWvw4IOjmTGj8fp4SosWFVPLt7Ls75v38jzGjx2/3HuznpjJ\nB9fZ+vUJJJrNJpNvOpSXF7/E9hvuyDm7nN/qIwH7XF2ZW32ZXT2ZW32t0kIQQERcBOwB9ALHAdsD\nL2TmdRGxB7BsLrhrMwL+80UAAAeYSURBVPO7A32WhaDqG8oVHnOrL7OrJ3OrL7Orp1WdW7MJL71U\nPDE6Zkw1ruq/Xc0mXH75SGbOXIMJE4qZ3ebNg+nTBz+z20DmLJrD5BsnccukPzCyp/1wl/a5ejK3\n+jK7ejK3+no7haDBDBZNZp7eb9VDfd67m+Wnk1fN1e2AU5Kkumo0lr9TZnXYBzcaMGXKEqZMKcZy\najaLdc8/v3IeJdpwrQ2549BZb7udkiR1q0EVgiRJkqS3YtnwKatDkUuSpNVBZ0Y7lSRJkiRJ0rCz\nECRJkiRJktQlLARJkiRJkiR1CQtBkiRJkiRJXcJCkCRJkiRJUpewECRJkiRJktQlLARJkiRJkiR1\nCQtBkiRJkiRJXcJCkCRJkiRJUpewECRJkiRJktQlLARJkiRJkiR1CQtBkiRJkiRJXcJCkCRJkiRJ\nUpdoNJvNTrdBkiRJkiRJw8A7giRJkiRJkrqEhSBJkiRJkqQuYSFIkiRJkiSpS1gIkiRJkiRJ6hIW\ngiRJkiRJkrqEhSBJkiRJkqQuMbLTDYiI6cDOQBM4ITNnd7hJGkBEfBvYneK78y1gNnAlMAJ4Gvh8\nZr7auRaqnYgYC/wVuAC4E3OrhYiYDHwVWAKcAzyM2VVeRLwDuAIYD4wGpgHPAD+h2N89nJnHdK6F\n6isitgF+B0zPzB9HxCa06GdlfzwR6AV+lpm/6FijBbTN7jJgDWAxcFhmPmN21dI/tz7rPwbckpmN\n8rW5VUyLPrcG8Ctgc2AhMCkz55ld9bTIbg/gmxS/lYso9nXzIuI04NMUxyvTMvOmjjVagz7/Hmqf\n6+gdQRGxJ/CBzNwFOBL4USfbo4FFxN7ANmVeHwd+AJwPXJyZuwOPAUd0sIka2FnA8+WyudVARKwL\nnAvsBnwSOAizq4svApmZewOTgB9S/GaekJm7AmtHxAEdbJ9KEbEWMIOiQL7Mm/pZud05wH7AXsBJ\nEbHOMDdXfbTJ7kKKA+A9geuAk82uWtrkRkSMAc6gOLHB3KqnTXZHAc9l5oeAa4Ddza562mT3feDI\n8ljlXmBqRLwP+CxvHHt+PyJGDHd7VRjs+fdb6XOdfjRsX+B6gMz8OzA+It7V2SZpAHdTVIcB5gNr\nUXzRbijX/Z7iy6eKiYgtga2AG8tVe2FudbAfcEdmLszMpzPzaMyuLuYC65bL4ymKsO/rc9er2VXH\nq8AngKf6rNuLN/ezDwOzM/OFzHwZuAfYdRjbqTdrld2xwLXl8nMU/dDsqqVVbgBnAhcDr5Wvza16\nWmX3KeAqgMz8WWbegNlVUavs+h+rzAX2Bm7OzNcy8zngPxTnEOqMwZ5/D7nPdboQ9G6KnfQyz5Xr\nVEGZuTQzF5UvjwRuAtbq81jKs8BGHWmcVuR7wMl9XptbPWwGrBkRN0TErIjYF7Orhcy8GnhvRDxG\nsRM/FZjXZxOzq4jMXFIeNPXVqp/1P2Yxww5rlV1mLsrMpeUV7OOA32B2ldIqt4jYAtguM3/bZ7W5\nVUyb38vNgAMi4o8RcXV5F4LZVUyb7E4Cro+IpHj06HLMrlKGcP495Nw6XQjqr9HpBmjFIuIgii/i\nV/q9ZX4VFBFfAO7LzH+32cTcqqtBcaXmYIpHjS5j+bzMrqIi4jDgv5m5ObAP8Ot+m5hdfbTLygwr\nqiwCXQnclZl3ttjE7KpnOstfsGrF3KqpQfEo9F4UY1Ge0WYbVc8MYGJmBvAnijsq+zO7CngL598r\nzK3ThaCnWP4OoI0pnwtWNZWD+H0dOCAzXwBeLAchBngPb77NV513IHBQRNwPfAk4G3OriznAveVV\nnH9SDMK40OxqYVfgVoDMfAgYC6zX532zq7ZWv5H9j1nMsLouAx7NzGnla7OrsIh4D7AlcFV5rLJR\nRMzE3OpiDjCzXL4V2Bqzq4ttM/Oecvl2YCfMrnIGef495Nw6XQi6jWIQTSJiB+CpzFzY2SapnYhY\nG/gO8MnMXDbo8B3AIeXyIcAtnWib2svMz2TmhMzcGbiUYtYwc6uH24B9IqKnHDj6HZhdXTxG8bw2\nEbEpRRHv7xGxW/n+wZhdlbXqZw8AEyJiXDkr3K7ArA61T22Us6a8lpnn9lltdhWWmU9m5vszc+fy\nWOXpcrBvc6uHmykGsQXYEUjMri6eiYhl4/9MAB4F7gIOjIhREbExRUHhkU41sNsN4fx7yH2u0Ww2\nV02rBykiLgL2oJjm7LjyyqkqKCKOBs4D/tFn9eEUxYUxFIOJTcnMxcPfOg1GRJwHPE5xxeYKzK3y\nImIqxa2gUMyGMxuzq7xyJ/xLYEOK6T7Pppg+/qcUF2EeyMwVPQahYRARO1KMo7YZxRS6TwKTKcZK\nWK6fRcQk4DSKKXVnZOZVnWizCm2y2wB4BVhQbvZIZh5rdtXRJreDl53kRMTjmblZuWxuFdImu89R\nzIy5EfAicHhmzjG7ammT3ZkURYbFFJNaHJGZ8yPieIr9YBM4q80jthoGQzn/Hmqf63ghSJIkSZIk\nScOj04+GSZIkSZIkaZhYCJIkSZIkSeoSFoIkSZIkSZK6hIUgSZIkSZKkLmEhSJIkSZIkqUtYCJIk\nSZIkSeoSFoIkSZIkSZK6hIUgSZIkSZKkLvF/B1FothTozZIAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1440x144 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"With dynamic reference\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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AkBBCCCHEHKBpEI1OfCyRS6AqKqXuUrpiXVDVAieQ0TMh1QVWduJjsT2w/XpI98Nfb4WP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\nUiD+H6oK03BdXTmkAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 1440x144 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "glnp3EgIfB5n",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
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