v4 session graphs since 2015-05-07 (re bug 1154113 etc) - cleaned.ipynb

{"nbformat_minor": 0, "cells": [{"execution_count": 8, "cell_type": "code", "source": "import ujson as json\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nimport plotly.plotly as py\nimport networkx as nx\nimport collections\n\n\n\nfrom moztelemetry import get_pings, get_pings_properties, get_one_ping_per_client\n\n%pylab inline\n\n", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Populating the interactive namespace from numpy and matplotlib\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 9, "cell_type": "code", "source": "sc.defaultParallelism", "outputs": [{"execution_count": 9, "output_type": "execute_result", "data": {"text/plain": "2"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "## Group pings by clientId \nGet the \"payload/info\" section recent pings from builds newer than 20150507000000, and which are not idle-daily pings, and group by clientId", "cell_type": "markdown", "metadata": {}}, {"execution_count": 10, "cell_type": "code", "source": "pings = get_pings(sc, app=\"Firefox\",\n                  channel=\"nightly\",\n                  submission_date=(\"20150507\",\"20150514\"),\n                  fraction=1,\n                  schema=\"v4\")\n\ninfo = get_pings_properties(pings, [\"clientId\",\n                                    \"payload/info\",\n                                    \"environment/build/buildId\"])\n\nsubsess = info.filter(\n    lambda p: ((p[\"payload/info\"].get(\"reason\", \"idle-daily\") != \"idle-daily\") and\n               (p[\"environment/build/buildId\"]>\"20150507000000\")) )\n\n\nclients = subsess.map(lambda p: (p.get(\"clientId\",\"noId\"),\n                                 [p[\"payload/info\"]]) ) \\\n    .reduceByKey(lambda l1,l2: l1+l2)\n\nclients.cache()\nnumClients = clients.count()", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 93, "cell_type": "code", "source": "numClients", "outputs": [{"execution_count": 93, "output_type": "execute_result", "data": {"text/plain": "66504"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "##Construct session graphs and compute degree distributions over graphs\n\nOnce we've grouped by clientId, we can use the subSessionIds and previousSubsessionId pointer to construct a graph of the session, and then look at the degree of each node in the graph to see whether the graph looks the way we expect. Ideally, we should have a graph that has *zero* nodes with degree greater than 1 (no subsession should be pointed at by two or more sessions) and only *one* node with degree zero (the first subsession in the chain won't point to any prior subsession). Additionally, any given value of 'profileSubsessionCounter' should appear only once.\n", "cell_type": "markdown", "metadata": {}}, {"execution_count": 89, "cell_type": "code", "source": "def sessGraphFromList(sessInfoList):\n    g = nx.DiGraph()\n    for s in sessInfoList:\n        # each s is a separate subsession submission \"info\" section\n        subsessId = None\n        prevSubSess = None\n        try:\n            subsessId = s['subsessionId']\n        except KeyError:\n            continue\n\n        N = s.get(\"profileSubsessionCounter\",\"NA\")\n        g.add_node(subsessId,subSessNum=N)\n\n        prevSubSess = s.get('previousSubsessionId',None)\n        if prevSubSess:\n            g.add_node(prevSubSess)\n            g.add_edge(subsessId,prevSubSess)\n    return g\n\n\ndef degreeDistrib(g):\n    return collections.Counter(g.out_degree().values())\n\ndef subsessSummary(sessInfoList):\n    g = sessGraphFromList(sessInfoList)\n    d = degreeDistrib(g)\n    subSessCounterMult = collections.Counter(map(lambda x:x[\"profileSubsessionCounter\"], sessInfoList))\n    return {\"info\":sorted(sessInfoList, key=lambda x:x[\"profileSubsessionCounter\"]),\n            \"graph\":g,\n            \"degDist\":d,\n            \"subSessCounterMult\":subSessCounterMult}\n", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 90, "cell_type": "code", "source": "subsessSummaries = clients.map(lambda c: (c[0],subsessSummary(c[1])))\nsubsessSummaries.cache()", "outputs": [{"execution_count": 90, "output_type": "execute_result", "data": {"text/plain": "PythonRDD[96] at RDD at PythonRDD.scala:43"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 103, "cell_type": "code", "source": "# subsessSummaries.take(1)", "outputs": [], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": 92, "cell_type": "code", "source": "badGraphSummaries = subsessSummaries.filter(\n    lambda id_summary: any([k>1 for k in id_summary[1][\"degDist\"].keys()]) \\\n            or any([v>1 for k,v in id_summary[1][\"degDist\"].iteritems() if k!=1]) \\\n            or any([v>1 for v in id_summary[1][\"subSessCounterMult\"].values()]) )\nbadGraphSummaries.cache()\nnumBadGraphs = badGraphSummaries.count()\nnumBadGraphs", "outputs": [{"execution_count": 92, "output_type": "execute_result", "data": {"text/plain": "5474"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"source": "#Percentage of clients with bad session graphs", "cell_type": "markdown", "metadata": {}}, {"execution_count": 95, "cell_type": "code", "source": "numBadGraphs/float(numClients)", "outputs": [{"execution_count": 95, "output_type": "execute_result", "data": {"text/plain": "0.08231083844580778"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "##Grab a few examples of graphs with imperfect session chains and plot them", "cell_type": "markdown", "metadata": {}}, {"execution_count": 81, "cell_type": "code", "source": "bg = badGraphSummaries.take(100)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 101, "cell_type": "code", "source": "def plotSessGraphsAndInfo(sessGraphInfo):\n    cId = sessGraphInfo[0]\n    g = sessGraphInfo[1][\"graph\"]\n    dg = sessGraphInfo[1][\"degDist\"]\n    sscc = [(sessCounter,mult) for sessCounter,mult in sessGraphInfo[1][\"subSessCounterMult\"].iteritems() if mult>1]\n\n#     print cId\n    print \"node degrees (degree, number of nodes with degree)--\",list(dg.iteritems())\n    if sscc:\n        print \"repeated subsession counters (counter, multiplicty)--\",sscc\n\n    pos = nx.spectral_layout(g)\n    labels = [(n,d['subSessNum']) for n,d in g.nodes(data=True) if d]\n    G = nx.draw_spectral(g,node_size=10, node_color=\"w\")\n    for sessId,label in labels:\n        x,y = pos[sessId]\n        plt.text(x+.02,y+.02,s=label\n                 ,horizontalalignment='center')\n    plt.show()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 102, "cell_type": "code", "source": "map(plotSessGraphsAndInfo,bg)", "outputs": [{"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 9)]\nrepeated subsession counters (counter, multiplicty)-- [(12, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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Dhg3Kzc3VU089ZToKAEOio6O1f/9+HTx40HQU/BvK14lNmDBBo0eP5lwP4MJK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\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d7423f0d0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 7)]\nrepeated subsession counters (counter, multiplicty)-- [(4, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d547d0750>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 11)]\n"}, {"output_type": "display_data", "data": {"image/png": 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AUEyMAaCYGANAMTEGgGJiDADFxBgAiokxABQTYwAoJsYAUEyMAaCYGANAMTEGgGJiDADF\nxBgAiokxABQTYwAoJsYAUEyMAaCYGANAMTEGgGJiDADFxBgAiokxABQTYwAoJsYAUEyMAaCYGANA\nMTEGgGJiDADFxBgAiokxABQTYwAoJsYAUEyMAaCYGANAMTEGgGJiDADFxBgAiokxABQTYwAoJsYA\nUEyMAaCYGANAMTEGgGJiDADFxBgAiokxABQTYwAoJsYAUEyMAaCYGANAMTEGgGJiDADFxBgAiokx\nABQTYwAoJsYAUEyMAaDYfwHMr9F7oUMLkQAAAABJRU5ErkJggg==\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d94073090>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 3), (1, 11)]\nrepeated subsession counters (counter, multiplicty)-- [(1, 3), (2, 2), (3, 2), (4, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d143f8d10>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 52)]\n"}, {"output_type": "display_data", "data": {"image/png": 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FRaxbt47hw4czYsQI8vLyAlWupSl8RURMoLseVvD5fFRWVrJp0yYef/xxbr75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\nKyIiYjCFr4iIiMEUviIiIgZ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"text/plain": "<matplotlib.figure.Figure at 0x7f7d147cb450>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 30)]\n"}, {"output_type": "display_data", "data": {"image/png": 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zFPTWrVvVvXt3Pfvssxo/frzi4uLUqVMnrVy5Ui6Xy3st4ebNm0M5vm3wDBgA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\nzGueOAIRAAAAAElFTkSuQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d74542d50>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 1)]\nrepeated subsession counters (counter, multiplicty)-- [(1, 2), (3, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d14546310>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 6)]\nrepeated subsession counters (counter, multiplicty)-- [(1, 2), (2, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d5435cb10>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 13)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d74754050>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 33)]\n"}, {"output_type": "display_data", "data": {"image/png": 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5s37/+9+rurpaBw4c0FNPPaXJkyerU6dOwZA+f/68Hn/8ceXl5dlUvTsQxgAA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\nAElFTkSuQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d145deb90>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 50)]\n"}, {"output_type": "display_data", "data": {"image/png": 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3b69GjRopLCxMa9euVVFRkaZNm6b9+/crISFBW7Zskd1uN2hyAEBVuOxscr4+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\ngg==\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d94173fd0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 3), (1, 24)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d74201510>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 60)]\nrepeated subsession counters (counter, multiplicty)-- [(50, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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7XawOHz7MmTNnWLhwIRcuXCAnJ4eTJ0/i7OzM+PHj1Y4pqoCRI0dy5MgRTExM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\nNBpWrVrFZ599xtmzZ/XH7ezsiIuLUyt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"text/plain": "<matplotlib.figure.Figure at 0x7f7d5454b210>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 59)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d9caedcd0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 28)]\nrepeated subsession counters (counter, multiplicty)-- [(13, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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Pjw+0Wi38/PyQnJwMDw8P42Oio6Ph6OjIrTgtHJeXpH8kMDAQeXl5AICCggKs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\nBg0aoKKiAt27d8cHH3yA7du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"text/plain": "<matplotlib.figure.Figure at 0x7f7d5432eed0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 72)]\nrepeated subsession counters (counter, multiplicty)-- [(46, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d94139b10>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 62)]\nrepeated subsession counters (counter, multiplicty)-- [(39, 2), (83, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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zJk2aRNOmTXF2dmb79u0P1IDjbmlpafTu3ZukpKTHHVmUc3LlK8qFomYaU6dO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\nqRMABQUF7N69m3vvvZeePXvS0NDAnj17GDFiBGV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"text/plain": "<matplotlib.figure.Figure at 0x7f7d3470de90>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 10)]\nrepeated subsession counters (counter, multiplicty)-- [(10, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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lC5dnt9v1wAMPaPHixU7j99xzjxYtWsQhBygyZ8+eVVxcnBo0aKDU1NSCxwMC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\n2P8CweJamiTu5sAAAAAASUVORK5CYII=\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d346739d0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 19)]\n"}, {"output_type": "display_data", "data": {"image/png": 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G+fpYsVgsLTWz5AwAwUH5AgBgGOULAIBhlC8AAIZRvgGRyWQUi8WUSCRK1w4cOKAbb7xR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\n+QIAYBjlCwCAYZQvAACGUb4AABj2f0p6HoPfVubbAAAAAElFTkSuQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d940bc6d0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 38)]\nrepeated subsession counters (counter, multiplicty)-- [(26, 2), (29, 2), (31, 2), (33, 2), (44, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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r14/x48dnu0+BAgWIjo4mLi6O+Ph4oqOj2bJlC8HBwaxcuZL69evbObVI/mAy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\nyZMZOHAgU6ZMoV69eixYsICEhATGjh1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"text/plain": "<matplotlib.figure.Figure at 0x7f7d5424bf90>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 55)]\nrepeated subsession counters (counter, multiplicty)-- [(52, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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2R+aK6RMnTnDq1Cl2797N/v376d27NzNmzODUqVO0adOGmTNnmqt0YSGk+Yp8\n5en4SFtbW5ycnOjduzd+fn60adOGgIAAFSsUBZ2trS3BwcHo9Xr0ej1Go5GlS5dy7dq1XMfnFj95\n8eJF6tevD0Djxo3/9BliUfhI8xX52u7duzEajTg5OaHT6Th06BBTp05VuyxRwK1du5ayZcuSlpYG\nwOrVq6lQoUKuY41GI4GBgTg7O9OgQQN8fHzw8fFh48aNAHz33XfExsaarXZhGaT5inwrMTGRbt26\nUbZsWRYuXEhsbKxE+QmzKFGiBF9//TXvvfceCxYsYMqUKXmufM4tfnL58uUsWrSIGjVqkJycLJGn\nIgdZ7SzynatXr/L666/j4+NDmTJlWLVqFUlJScCT+28ODg6mHWWEeNEURaF9+/ZUqlSJWbNmPXPs\nxIkTKVq0KCNGjDC9duHCBbp06cLhw4dfdKnCgsjMV+RL9+7d49SpU8yYMcMU5QdIlJ8wO41Gw9Kl\nS1m7di07d+7MduzOnTumldCZ8ZNBQUHEx8cDSOSpyJPES4p84/Hjx/To0YPdu3dz+/ZtnJ2dWbt2\nrUT5CdU5OjqyYsUKunfvzsmTJ9HpdEDu8ZONGjVi3rx5LFq0CJDIU5E7uews8o0+ffoQFRVFeno6\nHTp0YOTIkWqXJEQ2Q4cO5Y8//uDbb7+V+Enxr8hlZ5Ev/Pjjjyxbtozw8HAiIyNxdHRE/i4U+c3U\nqVM5d+4cy5cvl+hJ8a9I8xX5wuzZs9FqtWi1WuBJlrN8uIn8xs7OjrZt29KvXz90Oh2DBg3i4cOH\napclLJA0X2F2uUVIHjp0CA8PDzw8PMjIyMBoNGJnZ6dypUJkZzAYmDVrFtHR0URFRfHZZ5+ZIiXz\nkpGRQVBQEK+//joACQkJhIaG4unpSZMmTfKMrhQFmzRfYXa5RUja2NiwefNmNm/ejLW1NZ07d1ap\nOiGe7enbIQsWLCA8PDzP8fPmzcPb29t0j3jatGmEhoZy4cIFGjVqxLRp015ovSJ/kuYrzO7pCMlz\n585hZ2eHn58fvr6+lChRgi5duqhYoRC5s7W1Zdq0admiJ93c3EhNTc11/PXr19m6dSu9e/c2Ne1N\nmzbRrVs3ALp168aPP/5otvpF/iHNV6gqPT2dXr16ERYWRkJCAps2baJKlSpUqVJF7dKEyNWgQYP4\n4YcfSEtLo2bNmnh5eZlynJ82dOhQZs6ciZXV/z5q4+LicHZ2BsDZ2Zm4uDiz1C3yF2m+QlVz5sxB\np9PRvXu3ShGoAAAgAElEQVR3bG1tWb9+vVxyFvne//3f/3Hx4kX27t1LXFwcX3/9dY4xP/30E05O\nTgQFBeW5cl+j0cgjS4WUhGwI1aSmpjJjxgyOHDmCRqMhPT2dDRs2cPz4cbVLE+JPVa1aFYDly5fT\nokULmjRpgpOTk+n4wYMH2bRpE1u3biUlJYWkpCS6dOmCs7Mzt27dwsXFhZs3b2Z7jyg8ZOYrVGE0\nGrl+/TofffQRlSpVAmDHjh14eXlRtmxZlasT4q97+eWX6datG4MGDcr2+pQpU4iNjeXKlSusXbuW\nhg0bsnr1alq1asWqVasAWLVqFa1bt1ajbKEyab7CrK5evUqHDh2oXr06jx8/ZtasWaxYsQKAb7/9\nlk6dOqlcoRB/3/jx4zl27JhpG8HcZF5eHjNmDL/++iuenp7s2rWLMWPGmKtMkY9IvKQwm9TUVIKC\ngnj8+DEJCQkcPHgQb29vtcsS4rnYs2cP77zzDsePH6dkyZLY2tqqXZLIx2TmK8zmzTff5OLFi1y/\nfp3k5GQWLVqE0WhUuywhnouQkBCqVq1K+fLl0el0sgGIeCZZcCXMIiUlhe3btxMdHQ2AXq/n/v37\n2R7BEMKSGQwGjhw5wtmzZwHw9fWladOmVKhQQeXKRH4kn3zihckaI6nRaLCxefK33qpVq0hLS+PI\nkSOMHj1a5SqFeDHS09MZN27cM8c8HT353Xff4ePjg7W1taz6L+Ck+YoXJmuMpK2tLTVr1kSv1zNp\n0iRGjBhBdHQ0I0aMULlKIZ4PW1tbBg0alC396ptvvuG3337L8z1PR0/6+fmxYcMGXnvtNXOVLVQi\nzVe8MFljJI8dO0Z0dDTNmjVjy5YtzJw5E4AyZcqoWaIQz9XEiRPx9PQkLS2NjIwMypYt+7eiJ/V6\nPZ6enuYsWahEmq8wi/79+zN16lRiY2MJDw+ndu3ahISEcPToUbVLE+K5sbGxYfHixdja2hIcHExQ\nUBCNGzfOdWxu0ZOi8JD/18ULl5iYiI2NDV27diU9PZ3ExETCw8OZOXMmHTp0ULs8IZ6r+vXrc+3a\nNX7++WfOnz/Pli1bcoz5K9GTomCT5iteqMTERG7dusXChQuxsrLCzc2Ntm3bAvDKK69gZWXF3bt3\nVa5SiOfL2dkZW1tbFixYwKBBg0hJScl2PDN6slKlSnTq1Ildu3bRtWtXlaoVapDmK16oWbNm4eDg\nQGBgIACtW7dm165dAFy4cIHU1FQcHR3VLFGIF6ZZs2YEBgYyY8aMbK/nFj355ZdfZhsjM+KCTZqv\neO4MBgOKotC0aVPWrl3LgwcPcHd3Z8WKFfTs2ZPLly/j5+dHp06dcnzgCFHQhIWFMX/+fC5fvpzn\nmMzVzhs2bMDd3Z3w8HBatmxJ8+bNzVWmMDOJlxTP3fDhwzl8+DCJiYmMHj1aLqeJQm/q1KkcOnSI\nTZs2qV2KyCdk5iueq9OnTzN37lwOHDjA2bNnOXr0KA8ePFC7LCFUNWzYMM6fP88PP/yAwWBQuxyR\nD0jzFc9Vv379sLa2RqvVYm1tzcaNG7G2tla7LCFUZWtrS/PmzXnrrbfQ6XRMnz5d7ZKEyqT5in8l\na4SkwWDg+PHj9OvXD0dHRzIyMrCysnpmwo8QhYHBYGDZsmVER0cTFRXFRx99xN69e/Mc/3Ts5MiR\nI/Hy8iIgIIC2bdty//59c5UuXhBpvuJfyRohCU9WaGo0Gnr27ImdnR3nzp2jWbNmKlYoRP7w9A5e\nI0eOzHNF89Oxk02aNOHMmTOcPHkST09Ppk6d+sLrFS+WNF/xr2SNkCxSpAiVK1dm4cKFzJw5k3nz\n5mFnZ6dyhUKoz9bWlgEDBmTLfT59+jRnzpzJMTa32MnQ0FBTElatWrW4fv26WesXz580X/HcbNu2\njYyMDEaNGoWrqysLFy6kV69e3Lt3T+3ShFDdrFmzaN68OWlpaWi1WtavX4+vr2+OcX8WO7l8+XJa\ntGjxossVL5g0X/FcKIrC8OHDmTVrFoMHD+bq1aucOHECV1dXhg8frnZ5QuQLYWFhHDlyhHnz5jF9\n+vQcl53/LHZy8uTJFClShM6dO5urZPGCSPMVz0ViYiJubm60aNECJycnNBoNGo2G3r17ExERoXZ5\nQuQLlStXpkaNGvTs2ZO7d++ycePGbMefFTu5cuVKtm7dytdff61G6eI5k5AN8a+dOnWK6tWrc/z4\ncfz9/bl58yaurq7A//7S/+abb1SuUoj85ZdffmHAgAGcOXOGIkWK5Di+d+9eZs2axebNm9m+fTvD\nhw9n7969lC5dWoVqxfMmM1/xr3Tq1IlXX30VRVFo2bIly5cvZ/To0fj7+xMQEMDevXsJCwtTu0wh\n8p0mTZrg4eHBokWLcj2e+eQAwMCBA0lOTiY0NJSgoCD69+9vzlLFCyAzX/GPZGRkMG3aNJo0aULz\n5s05ffo0Li4uapclhEU5e/YsISEhREdHo9Pp1C5HmJE0X/GPLF68mP79+6PVamnUqBE//vgjtra2\napclhMUZMGAAAHPmzJHfoUJELjuLv23+/PkMHjwYrVaL0Whk+/btjBkzRu2yhLBIlStXZtmyZeh0\nOpYuXap2OcJMZOYr/haDwYBOpyMqKgoAvV6PlZUVFy9exN3dXeXqhLAsT/8++fj4sGTJEtkJrBCQ\nma/4SzIznF9++eVsr2s0Gl599VUqVKhAQkKCStUJUTBkZGQwevRoHj16lOfxrJnPH330EQEBAQQG\nBtKoUSNiY2PNWa74F6T5ir8kM8NZo9Ewa9Ys9Ho9vr6+jB8/Hq1WS4UKFdQuUQiLY2try/jx47PF\nTt66dYsFCxbkOv7pzOdRo0Zx8uRJTpw4QevWrfn000/NWb74F6T5ir8ka4aztbU19erVIzExkePH\njzNz5kyVqxPCco0cOZKBAweSlpZGRkYGer2eunXr5hiXW+azvb296XhycrI8A2xBbNQuQFgWo9HI\nhAkT2LBhA9u3b8fNzQ1/f3+1yxLCon3wwQccPnyY4OBgNm3axKuvvppjTGbmc1JSUo73rl69mmLF\nihEeHm6uksW/JDNf8bckJCRQu3ZtfHx8mDJlSrbLXLJ2T4h/RqfTsX//fiZNmkTx4sVZs2ZNtuPP\nynyePHkyMTExdO/enaFDh5qzbPEvyGpn8ZdlxkiePn2atLQ0GjduTLFixYAnl8TKlStHREQETk5O\nKlcqhOXau3cvPXr0IDo62hQ7OW7cOFavXo2NjQ0pKSkkJSXRrl07vvzyS9P7YmJiaNGiBadPn1ar\ndPE3SPMVf9mAAQNYs2ZNrquaK1WqxLFjxySlR4jnoHnz5vzf//2fKYAjq6yZzxcvXsTDwwOABQsW\nEBERwerVq81drvgH5LKzeCZFUUhNTaV169YsXryYhw8f4u7uzooVK7KNy1x9KYT496ZMmcKkSZNI\nTk7OcSxr5vPYsWPx8/MjMDCQPXv2MHv2bHOXKv4hmfmKZ9q+fTvvvfceVapUwd/fXzZJEMJM3nrr\nLfz8/Pjggw/ULkW8ANJ8RZ6MRiNBQUGcOnUKeJJm9cUXX+S6ElMI8XxdvHiROnXqcOrUKRwdHSX3\nuYCRy84iT8OHD+fcuXNotVqsra2Jjo6maNGiapclRKHg4eGBr68vlStXRqfTMW/ePLVLEs+RNF+R\nTWaMpK+vL0uXLiU6Opo+ffqQkZFByZIlGTFihETYCWEGBoOBI0eOcPbsWaKiohg1ahQGgyHXsU/H\nTmaaPXs2VlZWEv2aD0nzFdlkxkhm1adPH+zs7Dhw4IBE2AlhJg8fPiQ9Pd30taIo7NmzJ9exT8dO\nAsTGxvLrr79K9Gs+Jc1XZJMZI6nRaBg2bBh6vZ46deowb948fHx8JMJOCDPR6XR4eHhky31et25d\njnG5xU4CDBs2jBkzZpizZPE3SPMVeTp9+jRTpkwhISGBa9euUb58eVatWiV79wphJsuWLSMtLQ17\ne3saN27MsmXLcozJjJ20svrfx/nGjRsl+jWfk+YrcpWSkkJERAQDBgzA1tZWIuyEUEGdOnXYtWsX\nZ86cISIigj/++CPb8dxiJx89eiTRrxZAHjUSOVy9ehV/f38mTZrEoEGDsh2TCDsh1DF69GgePHjA\nokWLTK/lFjvZvHlz9u3bJ9Gv+Zw0X5HDTz/9RJs2bUhKSqJo0aISYSdEPhAfH49erycyMpLy5cvn\nOJ41djIriX7Nn2RLQZFNp06d2LBhA4qi4OnpyaeffsrWrVs5f/481tbWVKlShcWLF6tdphCFTpky\nZXj33XeZMmUKn332Wa5jcot5lejX/ElmvgKAtLQ0Pv/8c/z8/Hjrrbe4dOkSdnZ2apclhMjizp07\nVKtWjePHj8sjRBZOFlwJAFavXk3//v1p1KgRtWrVkgUaQuRDpUuXpm/fvkycODHPwA1hGaT5CtLS\n0hg7dixarRZFUdi4caOEuQuRT5UuXZovv/wSnU7H0qVL1S5H/ENy2Vlw8+ZNKlWqxNmzZ4EnGyic\nOXPGtMhKCJE/GAwGdDodUVFRAPj4+PDTTz/RqFEjlSsTf5fMfAuxzBzn0NBQ0wP6U6dOJT09nXbt\n2tG2bVvu37+vcpVCiLxkZGTwySef5Hksa97z+PHjcXNzIygoiKCgoBwxssK8pPkWYpk5zhqNBm9v\nb3x8fFi9ejULFy7k1KlTeHp6MnXqVLXLFEL8l62tLUOHDs0WOXngwAH27duXY+zTec+ZkbGRkZFE\nRkbSrFkzc5cvspDmW4hl5jgbDAauX7/OjRs3uH//Pv369QOgVq1aXL9+XeUqhRBZTZo0ieDgYNLS\n0sjIyKBLly45nvvNLe9ZURRZSJmPyHO+gvj4eEaNGkWpUqWyvb58+XI6deqkUlVCiLx88skn+Pr6\nkpiYiLOzc47HjjLznpOSkkyvaTQaFixYwJdffkmNGjWYPXs2Dg4O5i5d/JfMfAu5mJgYHjx4QP/+\n/bO9PnnyZIoUKULnzp1VqkwIkZd69eoRFhbGhAkT+Pzzz7Pt15tb3jNAv379uHLlCidOnMDV1ZXh\nw4erUbr4L2m+hdxnn32GTqejZMmSptdWrlzJ1q1b+frrr1WsTAjxZ8qXL88bb7zBggULTK8dPHiQ\nTZs2UalSJTp16sSuXbvo2rUrTk5OaDQaNBoNvXv3JiIiQsXKhTxqVIj98ccfeHt7U65cOdNjRtu3\nb2f48OHs3btX9u0VwgJcuHCBunXrcvnyZezt7bMdy5r3fPPmTVxdXQEICwvjyJEjfPPNN2qULJCZ\nb6EUHx9Peno6jRo1IiMjg0uXLuHu7s7y5csZOHAgycnJhIaGEhQUlONytBAif/H09KRhw4YsWbIk\nxzFFUUyrnUeNGoW/vz8BAQHs3buXsLAwc5cqspCZbyHUokULzp49S3x8PFFRUVSuXFntkoQQ/8LJ\nkydp3rw5586dw87ODltbW7VLEn9CZr6FzMcff8yOHTu4ceMGBoOB1157jRs3bqhdlhDiXwgICMDZ\n2RkXFxeJnbQQMvMtRAwGAw4ODpw5cwZ4EiMZFBREeHi4bDsmhAUzGAyUKlWK06dPA5geQ5IZcP4l\nM99ComfPnpQvXz7bTihGo5EbN25gY2PD8ePHVaxOCPFvZf0DOj09ncTExBxjno6cBFiwYAFeXl74\n+voyevRos9QqpPkWGplRki4uLuj1enx9fRk6dCg7duzgtddeU7s8IcS/UKRIEUqWLJktdnL58uU5\nxj0dObl79242bdrEqVOnOH36NCNGjDB36YWWNN9CIjNK0srKigYNGpCYmMjMmTOpVq2a2qUJIf4l\njUbD4MGDSUtLIy0tDQcHB5ycnLKNyS1ycvHixabtRAHKlClj9toLK2m+hYjRaCQ+Pp6xY8fKvSAh\nCpi+fftSp04d+vbti4+PD7179852PDNyMnMHM4CLFy/y22+/Ubt2bUJCQjh69Ki5yy60JNu5ENmx\nYwfW1tYEBwerXYoQ4jlzcHDg4MGDpKWlUbVqVSIiIqhZsyaQPXJyz549pvdk3hsODw/nyJEjdOjQ\ngcuXL6t0BoWLzHwLkSVLllC6dGlZ2SxEAabVahk2bBgzZ840vZZb5GSXLl1wc3Ojbdu2ALzyyitY\nWVlx9+5dtUovVKT5FhIHDhwgPj4+W4ZzVvLEmRAFR69evdizZw+XLl0CYMqUKcTGxnLlyhXWrl1L\nw4YNWb16Na1bt2bXrl3Ak5jK1NRUHB0d1Sy90JDmW8DFxcWhKAodO3YkKSmJ8+fPm6Ikf/zxR9zd\n3QkPD6dly5Y0b95c7XKFEM9B8eLF6du3L3PmzMn1eObVr549e3L58mX8/Pzo1KkTX375pTnLLNQk\nZKMAUxSF6tWr8+DBA27fvk1sbGyeM18hRMESFxeHXq/n1KlTODk5ySLLfEZmvgXY8OHDOXPmDDEx\nMTx69Ah/f3+Sk5PVLksIYQbOzs4EBATg4eEhkZP5kMx8C6CePXuyZcsW4uPjTfd8qlWrhoODA6VK\nlaJixYqsW7cOBwcHlSsVQrwoBoMBnU5HVFQUkHfkZEZGBjVq1MDNzY3NmzfTsWNHLly4AMC9e/dw\ncHAgMjLS7PUXdDLzLYB69OjBpk2bsr1mNBpp164dFy5coFGjRkybNk2l6oQQakhPT+fWrVs5Xn86\n9erbb78lMjKSyMhI2rVrR7t27cxdaqEgzbcAql+/vmmHE71ej5+fH2XKlOHTTz8FoFu3bvz4448q\nVymEeJGKFClCiRIlnhk5mVvqVSZFUVi3bh2dOnUyZ9mFhjTfAkyj0dC0aVMSEhIwGAw4OzsDT+4F\nxcXFqVydEOJF0mg0jBkzxhQ5WbduXd54441sY3JLvcq0b98+nJ2dqVKlirlKLlQk4aqAMhqN3Llz\nh5EjR+a4x6PRaCRoQ4hCoGfPnty+fRtXV1e+//57qlevbjqWV+pVpjVr1tC5c2czVlu4yMy3gNqz\nZw9WVlbUr18feDLbzbzfc/PmzRyh60KIgsfe3p7JkyfTt29ffv/992xbh+aWetW1a1fgyf3hDRs2\n0LFjR7VKL/Ck+RZQn3/+ebYoyVatWrFq1SoAVq1aRevWrdUsTwhhRlqtloEDBxIWFmZ6LbfUq8yQ\njR07duDl5UXZsmXVKrnAk+ZbADVr1ozDhw8TFxeHu7s7K1asYMyYMfz66694enqya9cuxowZo3aZ\nQggzevfdd/npp5+4ceNGrsez3or69ttvZaHVCybP+RYgBw4cIDIykkOHDuHn5ycNVgiRzcCBAylR\nogSTJ09Wu5RCT5pvAdKiRQu2bdsGPPklGzt2LK6uripXJYTILy5dukTt2rW5ePEipUqVUrucQk0u\nOxcQly5d4pdffkGr1WJlZcWCBQuoW7cu8+bNU7s0IUQ+sWvXLh48eEDZsmVZunQp6enpOcZkZGQQ\nFBTE66+/DmDaFzgoKIhXXnmFI0eOmLvsAkmabwHh5uZGkSJF2LhxI1WrVkWr1RIVFcVPP/3E77//\nrnZ5QgiVGQwGhg4dyrlz5zhz5gwDBw5k0qRJOcY9nXg1atQoJk6cSGRkJBMmTGDUqFHmLr1AkuZb\nQGg0GhRF4fLlywQGBmJjY4ONjQ3BwcH88MMPapcnhMgHst5lVBSFpUuXkpaWZnott8QrV1dX7t+/\nDzzJei5Xrpx5iy6gJGSjgChSpAjOzs4MHz6c1NRUZs+eTUZGBlu2bKFmzZpqlyeEUJmtrS1TpkxB\nr9cDT4J4ateuTUJCgin9LjPxKikpyfS+adOmUa9ePUaMGIHRaOTQoUOq1F/QyMy3gNi/fz92dnYk\nJiayePFivv76a5o3b05QUFCu0XFCiMJnyJAhDBkyhH79+lGyZEmmT59uarxZE6+yzpB79erF/Pnz\niYmJISwsjJ49e6pVfoEiq50LiDfffJPg4GDef//9bK+PGzeO8uXL895776lUmRAiPxo7diyPHj0y\nLcocN24cq1evxsbGhpSUFJKSkmjbti0bN240zYQVRcHBwcF0GVr8c9J8C4DY2FgCAgK4du0a9vb2\n3L59GycnJ2JiYmjatCmHDx+mRIkSapcphMhHYmJiCAwMNH1uZLV3715mzZrF5s2bqV69OmFhYQQH\nB7Nz507GjBkjK56fA7nnWwAsXryYrl27mn6B2rdvz927d9FqtSxatEgarxAih/Lly9OoUSNWrVqV\n44oZ/C/xaunSpQwYMACDwUDRokVZunSpuUstkGTma8FmzpyJg4MD48aN4+DBg3h4eKhdkhDCgvz2\n22+8++67nDhxAisrqxw7oIkXR1biWKi5c+fywQcfMGDAABITE6lbty4+Pj507twZg8GgdnlCCAtQ\nv359UlJScHR0RKfT5TqrfTp04+TJk9SpUwd/f39atWrFgwcPzF12gSDN1wIZDAbGjRtHdHQ0O3bs\nICMjA2tra06cOEFGRgZr165Vu0QhhAVITU3l9u3bnD59mqioKIYOHZrjj/enQzd69+7NjBkzOHXq\nFG3atGHmzJlqlG7xpPlaqMxfhOLFiwPQrVs3NBoNjx49kofghRB/WdZHEZ+Om8wtdOPixYumfcIb\nN27M+vXrzVdsASLN1wLZ2toSFhaGt7c3devWpWPHjixcuJCyZcvi4OBA48aN1S5RCGEB7ty5Q1pa\nGnq9Hr1eT0ZGBhcuXDAdzwzdyNqgfXx82LhxIwDfffcdsbGxZq+7IJDma6FatWqFnZ0de/fu5dSp\nU1y9epUbN26QnJzM119/rXZ5QggLUK5cOerWrUtaWhqKovDNN9/g5eUF5B26sXz5chYtWkSNGjVI\nTk6mSJEiapVv0aT5Wqhly5bRsWNHrly5wquvvoqjoyM2Nja0bduWgwcPql2eEMJCzJkzh/Pnz9O0\naVOSk5OxsXnyBOrBgwfZtGkTlSpVolOnTuzatYuuXbtSrVo1fv75Z44ePcpbb71FlSpVVD4DyySP\nGlmg9PR0KlasyJYtWwB4++23OXLkCHZ2dnTv3p2aNWsyYMAAlasUQliSbdu28eGHH3L06FHTmpJM\nWUM34uPjKVOmDEajke7du9OwYUO6d++uTtEWTGa+FmjTpk1UrFiRgIAAAgIC6Nq1KzVq1MDf3x+A\nPn36qFyhEMLSNG3alHv37hEREZHr8cyG/M0331CtWjW8vLxwc3OTxvsPyczXghw6dAgrKysGDx7M\nrVu3KFWqFACXL19m4sSJDBo0SOUKhRCWbNasWURGRrJ8+fIcgRsVK1akRIkSWFtbo9VqiYiIICEh\ngY4dO3Lt2jUqVqzIunXrcHBwUKl6yyIzXwvStWtX6tevz/Hjx2nQoAHh4eEcO3aMYsWK0aZNG7XL\nE0JYOCsrK7777rtcAzc0Gg179uwhMjLSNDueNm0aoaGhXLhwgUaNGjFt2jQ1yrZIMvO1EJcuXcLb\n25vo6GgA9Ho9x48f58aNG0yYMIH9+/erXKEQwpIZDAZ0Oh1RUVEA+Pn5ceXKFZycnACoVKkSR48e\nxdHR0fQevV7P3r17cXZ25tatW4SEhJg+o8SzyczXQri7u6PVak1fazQaPDw8WLt2LZ07d1axMiFE\nQZSWlsaCBQtMX2s0Gho3bkyNGjVYtmwZAHFxcab9gJ2dnYmLi1OlVkskuxpZCFtbW9q3b4+Xlxc2\nNjZMnjwZjUbD5s2bmT59utrlCSEsnK2tLe+88w56vR4Ao9HIDz/8wIQJE9BoNBw4cABXV1fi4+MJ\nDQ01jcuk0WhyrJIWeZOZrwU5e/Ys3333HQkJCYwYMYJt27bx8ssvU6ZMGbVLE0IUADNnzkSr1eLq\n6oqfnx8RERGmhurq6gpAmTJlaNOmDREREabLzQA3b940XaIWf06ar4U4evQod+7coWXLlqZViGvW\nrKFTp04qVyaEKChKlChBdHQ0Z86c4dq1ayQmJgLw6NEj0+5FDx8+5JdffsHPz49WrVqxatUqAFat\nWkXr1q1Vq93SyIIrC/Huu+/i6urK2bNnOXPmDIqicOvWLWJjY7G3t1e7PCFEAfP+++9TunRpxo8f\nz5UrV0xPVKSnp/PHH3/g7u6OoijExMTg7OxM0aJFMRgMXLx4kSNHjlC9enWVzyB/k+abz6WkpGAw\nGKhYsSKhoaE0a9aMnj17kp6ezsOHDylZsqTaJQohCqCoqCiaN2/O1atXTZGTmSpVqsSxY8fQ6XSm\n16Kjo7GysqJv377Mnj1bmu+fkMvO+djdu3dxcXGhcePG+Pv7c/ToUXr27AmAjY2NNF4hxAvj5+dH\nxYoV2bBhQ449fgGenrfp9Xo8PT3NVZ7Fk+abj73//vs8evSIkydPsn//fhITE+nRowfVq1fn3Xff\n5dGjR2qXKIQowPR6Pe+88w46nY7FixebXs/tsSPx90jzzacMBgObNm0iOjqa6OhoNBoNSUlJ9O/f\nn+PHj/PSSy9JmowQ4oUxGAysWbOGc+fOERUVxZAhQ0wz4AMHDhAZGcm2bdtYuHAh+/btU7layyPN\n10JotVrKlSvHK6+8AkD79u05fvy4ylUJIQqqxMRE0tLSTF8risLdu3eB3B87En+PLLjKx8LCwhg9\nejTp6emULVuWe/fuUblyZU6dOsX48eN5/PixBGwIIV4YT09Prl27hqIoTJ8+naFDh/Lo0SMyMjKw\nt7fn4cOHODo6UrZsWUqWLIlWq+Wll17C3d2dI0eOUKRIEapUqcKKFStkjcpTZOabj2m1Wlq3bk35\n8uU5deoUBw4cwNbWloCAAE6dOsW4cePULlEIUYAdO3aMpKQkatasSYUKFYAnkZL169cnMDCQWrVq\nUaxYMY4ePcrHH3/MzZs3CQ8PZ/PmzVSoUIGTJ0/i6enJ1KlTVT6T/EdmvvmUoigEBAQwb948evbs\nmSPQXAghzOWrr77iq6++Yvv27TmO5bbhQlYbNmxg/fr1fPXVVy+6TIsiM998RlEUxo0bxxdffMHj\nx48JCQmRlYVCCFW1a9eOo0ePcv78+RyPHf3Z59Py5ctp0aKFuUq1GLKxQj4zZswYwsLCALC3t+eL\nL8Nv8jYAAAjDSURBVL7INdC8fv36KlcqhCgsihYtSmBgIAEBAVhbWxMWFkafPn0Anvn5NHnyZIoU\nKSI7r+VCLjvnI0/vp6nX62nevDkxMTG4ubmxefNmPv30U4oXL87w4cNVrlYIUVgYDAZKlSrF6dOn\nAfDx8eHevXumnPmKFStSokQJ4uPj0Wq1xMTE0KpVK3bu3EnVqlUpXbo0K1euxN3dXc3TyFfksnM+\nZmNjQ7ly5fDw8ECj0WQLNBdCCHO5dOkSGRkZpq+NRiPwvw0XNBoNW7ZsoXLlynz++eds376dCxcu\ncO3aNU6ePEnr1q359NNP1So/X5LLzvmIra0tYWFheHl5YW1tzSeffMKmTZuIi4sjPj6eWrVq8fbb\nb9OkSRO1SxVCFCIVKlTgpZdewsvLC4D58+dja2tr2nDhxo0bhIaG0q1bN5o0aYKHhwepqamEhoYC\nTz7bQkJCVDyD/EcuO+czJ06c4PXXX+f8+fN069aNcePGkZSUxKxZs9i8ebPa5QkhCqn09HTu379P\nlSpVOHv2LGXLljUdq1y5MiVLlsTa2pq+ffvy7rvvAvDBBx+wevVqihUrRnh4OA4ODmqVn+/IZed8\n4s6dOyQkJLBo0SIURcHX15edO3eybt26HAHmQghhbjY2Njg6OtKhQwe++OKLbKueM+Mm4+LiGDRo\nEB4eHtSsWZPJkycTExND9+7dCQ0NxcrKioSEBBXPIv+Q5ptPdOzYERcXF1auXEnLli3p2LEjxYoV\nY86cObRr145du3bRtWtXtcsUQhRypUuXZuLEieh0OpYuXQr8L27SxsaGwYMH895772WLnAwJCeH/\n27ubkKjaPo7jvxmbOSUphUwqVloZydBkSVCLCRMq2kjaixgtomih1CIjQiIaKBiLLKe3VasQK8pN\nEbSUIKRFxkCoE0STwShCZkEvHic490KcHh97eO57c53x9vtZKbj4D4pfrnOuc51EIpE5qANcds4K\ntm1ryZIl6u/vlzS1y7m7u1s7duxQdXW1Tpw4oe7ubi47A3DVfz+REQwGlUql5Pf7lZeXp9LSUhUV\nFenixYtavXq1ysvLJUmbNm1SSUmJ+vv7Z70HeL5i5ZslvN6pX8Xw8LDS6bQaGhqUl5en/Px8lZWV\nyePxuDwhAPze6SxNHQo0ODiYOW5yZGREfX19qqur04YNG5Sbm6tVq1ZpaGhIr1+/1vDwsLZv3/7H\nk7LmG+KbBSzL0tmzZ1VeXq5t27YpEAhoYmJCHz9+VG9vrz58+KAnT564PSaAec6yLG3dulXBYFAV\nFRW6fPmywuGw4vG44vG4hoaGJEkrVqyQ4zgqLS1VYWGhmpqatGXLFjmOo8nJSfX09Lj8SdxHfLOA\nbdv69u2b6uvr9fLlSxUWFkqSSkpKVFZWpt7eXpcnBIApPT09+vr1q2pra5WTkzNj41VxcXHmmd/W\n1lYdOnRIyWRSN2/e1NOnT+U4jiYmJnT48GEXP0F2IL4uq6+vV35+vq5evapgMCifz5d5mD2RSCiZ\nTKq2ttblKQHgN8uytHz5cp0+fTqz8Wr6wA1J2rNnj65cuaJPnz5pdHRUK1euVEFBgTwej8Lh8IzH\nlOYrNly56E8brdra2nTu3DmtXbtW796904EDB3T37l2XJwWA3/608Soej6uxsVEDAwPKycnJvN/3\n3r17am5u1r59+3Tp0iUtW7ZMVVVVevz4scufwl2sfF02vdFq+uvq6mqtWbNGxcXFikajhBdA1nEc\nZ8Zxkx6PR6WlpZn7vj9//tSbN2+UTqf14MEDeTweffnyRbZt69atW3r27JmL02cHjpd0kWVZikQi\nWrdunfx+v27cuKGCggKlUint3LlTJ0+edHtEAJhl4cKFqqur0/r16/Xr1y+1t7dr0aJF+vHjhxYv\nXixJys3Nld/vVzqdViAQ0N69eyVJ79+/l2VZGhsbm9fvKOeys0vGx8dVU1OjRCIhx3F07do1HT9+\nXI8ePVJDQ4MqKyszjxe1tbVp9+7dLk8MADPdvn1bLS0tWrBggWKxmMLhsBobG+X1ejU5Oanv37/r\nzp07On/+vJLJpIqKihQIBDQ4OKhUKuX2+K5i5esSj8ejt2/famBgQNLUK7qeP3+uFy9eyO/3a2xs\nTBcuXNCRI0dcnhQAZrNtW2fOnFEikZBt29q4caO6uroyt9K8Xq+ampq0a9cu1dTU6OjRo4rH4xof\nH1dXV5fL07uP+Lqkq6tr1j2Tzs7OzPsxASDbOY6j+/fvKxqNynEcjY6OKh6Pz/o5n8+nzs5OFybM\nXsTXBalUSqdOnVIkElEoFFI6nVZHRwfhBTBnWJalaDSaWf1KUigU0v79+zNnFeB/Y7ezC6bfXHTw\n4EG9evVKXq9Xx44dc3ssAPhHmpub5fP5Mt+n02k9fPjQxYnmDuJrmG3bisVimVVvZWWl2tvbWfUC\nmHMsy1JLS4sqKioUCoUUiUTU2to649Qr/Bm7nQ37z4fTbdtWVVWVRkZGeMk0gDnJtm0tXbpUfX19\nsixLoVBInz9/ZkHxf3DP1zDLstTR0aFQKCRJun79OuEFMGdZlqVYLKbNmzdLEvtX/iZWvi6ZvizD\nHymAfwP+p/0zxBcAAMPYcAUAgGHEFwAAw4gvAACGEV8AAAwjvgAAGEZ8AQAwjPgCAGAY8QUAwDDi\nCwCAYcQXAADDiC8AAIYRXwAADCO+AAAYRnwBADCM+AIAYBjxBQDAMOILAIBhxBcAAMOILwAAhhFf\nAAAMI74AABhGfAEAMIz4AgBgGPEFAMAw4gsAgGHEFwAAw4gvAACGEV8AAAwjvgAAGEZ8AQAwjPgC\nAGAY8QUAwDDiCwCAYcQXAADDiC8AAIYRXwAADCO+AAAYRnwBADCM+AIAYBjxBQDAMOILAIBhxBcA\nAMOILwAAhhFfAAAMI74AABhGfAEAMIz4AgBgGPEFAMAw4gsAgGHEFwAAw4gvAACGEV8AAAwjvgAA\nGEZ8AQAwjPgCAGAY8QUAwDDiCwCAYcQXAADD/gKCob9/JCkimgAAAABJRU5ErkJggg==\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d5437a190>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 5)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d7473e210>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 9)]\nrepeated subsession counters (counter, multiplicty)-- [(9, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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UEGAAKCDAAFBAgAGggAADQAEBBoACAgwABQQYAAoIMAAUEGAAKPB/OIACzXAvOMwAAAAA\nSUVORK5CYII=\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d543786d0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 51)]\nrepeated subsession counters (counter, multiplicty)-- [(20, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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RR1hYWKDT6SgqKmLw4MFqxxJmTqadRYXRr18//vvf/z50iz8hTOU///kPw4YN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\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d54040490>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 11)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d546e84d0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 32)]\nrepeated subsession counters (counter, multiplicty)-- [(14, 2), (15, 2), (17, 2), (18, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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dklRbW6vs7GwdP35ct956qzIzM2W32zV8+HDt3LnTiNIBAJ0gfE2stLRUiYmJ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\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d54349a50>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 8)]\nrepeated subsession counters (counter, multiplicty)-- [(4, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d54198dd0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 21)]\nrepeated subsession counters (counter, multiplicty)-- [(14, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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Qy+WIi4sTHYVKCWe+9AStVov69etj+/bthpt3E5FYcXFxmD59OpKTkyGTyUTH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\nYv8HrWWpyEqxJEAAAAAASUVORK5CYII=\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d9caede50>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 15)]\n"}, {"output_type": "display_data", "data": {"image/png": 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AgMBKpVJKJBJyXfey3/34xz9WTU2NMpmMhWTXh/IFAARWZ2enurq6Lrv/5ptvauvWrZo2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\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d3475d810>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 30)]\nrepeated subsession counters (counter, multiplicty)-- [(40, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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YN26cijefUvnKX7JYLPTu3TvbsdjYWKxWq0mJRPK/RYsW4eHhQYcOHcyOIveJ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\nEwzDMIx58+YZjzzySI5XaP/d87dt29Y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"text/plain": "<matplotlib.figure.Figure at 0x7f7d74208410>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 19)]\nrepeated subsession counters (counter, multiplicty)-- [(7, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d346378d0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 23)]\nrepeated subsession counters (counter, multiplicty)-- [(19, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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g8pUHdvLkSerXr8/Ro0cpX7680XFE7MLx48d57rnnSE5OtqxoJPZL5SsPrEeP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\nAABJRU5ErkJggg==\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d540b7850>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 40)]\nrepeated subsession counters (counter, multiplicty)-- [(2, 2), (4, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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/fz+dMQDgO/EAlws8Ho+Ki4slSVlZWZarAQCc7whjw7xer+bMmdMYwnPmzKEr\nBgD8V2xTu+TE0SaCGADwfQhjAAAs42lqAAAsI4wBALCMMAYAwDLCGAAAywhjAAAsI4wBALCMMAYA\nwDLCGAAAywhjAAAsI4wBALCMMAYAwDLCGAAAywhjAAAsI4wBALCMMAYAwDLCGAAAywhjAAAsI4wB\nALCMMAYAwDLCGAAAywhjAAAsI4wBALCMMAYAwDLCGAAAywhjAAAsI4wBALCMMAYAwDLCGAAAywhj\nAAAsI4wBALCMMAYAwDLCGAAAywhjAAAsI4wBALCMMAYAwDLCGAAAywhjAAAsI4wBALCMMAYAwDLC\nGAAAywhjAAAsI4wBALCMMAYAwDLCGAAAywhjAAAsI4wBALCMMAYAwDLCGAAAywhjAAAsI4wBALCM\nMAYAwDLCGAAAywhjAAAsI4wBALCMMAYAwDLCGAAAywhjAAAsI4wBALDs/wHLOaC0fNdrXgAAAABJ\nRU5ErkJggg==\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d5478bed0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 29)]\nrepeated subsession counters (counter, multiplicty)-- [(22, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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6oFevXqJlJapqDhw4gLNnzwIAlEollEolYmJiMHz4cFy/fh0uLi7w9vZ+7oMv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\nRCL7f0nSUMIckwAfAAAAAElFTkSuQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d5457f250>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 9)]\nrepeated subsession counters (counter, multiplicty)-- [(2, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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w8vJSnz59NGPGDNNRICZfp9K2bVsdPXpUP//8s44dO6aqVauajgTAjpw5c0Yt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\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d940e35d0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 3), (1, 64)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d741fe850>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 3), (1, 96)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d54092910>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 4), (1, 4)]\nrepeated subsession counters (counter, multiplicty)-- [(1, 4), (2, 3)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d5409c7d0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 8)]\nrepeated subsession counters (counter, multiplicty)-- [(4, 2), (5, 2), (6, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d3408e990>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 9)]\nrepeated subsession counters (counter, multiplicty)-- [(6, 3), (7, 2), (8, 2), (10, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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OaxMmTFBKSooiIyMVGhqqlJQUBQYG6u2339batWsVGBio3bt3q3379mrbtq2h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\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d5459ded0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 78)]\nrepeated subsession counters (counter, multiplicty)-- [(20, 2), (61, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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JCQksW7ZM29BFiAclc76iQrp+/TpdunRh4MCBrFq1Crg7UqlevTr29vbA3cbp\nDRs25PDhw7LAShTh5+fHhg0bUErh4ODAmTNnsLa2LvX8efPmYW1tzdy5c7l16xZw94Odra2tdhta\niAchI19RIRV2KoqKiqJ///6YmJjQo0cPbt26RXx8PPHx8Tg5OXHs2DFJvKKY1atXk5qaSmpqKl26\ndMHPz6/IXZKSyo/atWtHs2bN2L9/PwB79+4t1k1JiL9LtpcUFUJJZUUZGRkcP34cS0tLrKysePXV\nV4s8R5qiiz9TWPv98ccf07lzZz788EOtlvfatWvFyo/69OnDypUr6du3L/n5+eh0OoYNGwbAsGHD\niIuLA+7+rdra2hIVFWWcCxMVgiRfUSFMmTKF/v37s2nTJvLy8ggLC+P8+fPMnj2bd955p8TnXLhw\nwcBRioqoWrVqfP3113Tr1o127doRFxdHu3btOHbsWLFzu3XrRlJSUpESpB9//JEvv/xSO2f69OnY\n2toa8hJEBSTJV5R7hWVF3bp102p2zc3NGTp0KNWqVTNydKIyaN68OWvWrKFPnz7k5uYCsHjxYqZM\nmVLs3PtLkO6tI1dKERoaKrW/4i/JnK8o9+Lj47G3t8fPzw8XFxeeeuopsrKycHR0ZOXKlbRt25Zx\n48Zpc3RCPKiMjAwWL15Mbm6u1oThzTff/FslSK1atdKOHTx4EAcHB9kHWvwlSb6i3CssK9LpdISH\nh2NjY0N+fj6TJk0iPj6e48ePU79+faZNm2bsUEUFZWNj87cTZkklSIU+//xzfH19yyhKUZnIbWdR\n7jk5OdGgQQPy8/Np2bIlBQUF1K5dGycnJ+2c8ePHl7pNoBB/RafTsXbtWho1aoReryc3N5eXX35Z\nW5RVkns7IHl5eZGXl8fWrVtLnCsW4n4y8hXlnqOjI46Ojty5c4eRI0diZWXFsGHDuH79unbO1q1b\n0ev1RoxSVHTW1tYsXLiQlJQUwsLC+PLLL0lMTCxyTmkdkAB2796Nm5sbDRo0MHjsouKRTTZEuXJ/\nSVFISAjt2rWjcePGpKSkYGpqipOTE0eOHGHy5MkcP34cnU6Hi4sLa9aswcHBwdiXICqJt956i8jI\nSHbs2PGXHZA8PT05e/YslpaWjBkzRtt2cuXKlaxatQpTU1MGDBjAu+++a8xLEuWIJF9RrtzfqSgj\nIwM/Pz9+/PFHLl26hIWFBTdu3KBu3brGDlVUcnl5eXh5eTFo0CBGjBhBbGwsjz/+eInnltQBKTc3\nl4ULF7KH3WyyAAAgAElEQVR9+3bMzc3l71YUIbedRblRWqei7777jrVr12JhYQEgb2DCIMzMzNi0\naRPz5s3DxcWFJ598stRGCiV1QPrwww954403MDc3B+TvVhQlyVeUG4Wdinx9fbVORYcPHyYnJ4fI\nyEg6d+6Ml5cXR44cMXaooooICQkhOztbKz+aN2/e3+6AFBcXx4EDB+TvVpRIVjuLcqOwpKhRo0Za\np6IPP/wQuDsXfOjQISIjIxk6dKjsXiUM4v5NXErbsrSw/Cg9PZ2+ffsSERFBXl4eqamp8ncrSiTJ\nV5QbTk5OODk50axZMwoKCti4cSMFBQU0btyY5557DoBOnTphYmLCzZs3te5FQpSV6dOnc/r0aa38\n6MUXX/zb5UdOTk48++yzgPzdiuLktrMoNxwdHWnUqBGenp5Ur14dGxsbatSogb+/P3v37gUgLi6O\nnJwceQMTBlG44j4lJYW9e/fyzTffcO3atSLnlFZ+NHjwYPm7FaWSka8oV5YuXYqnpye1a9emffv2\nvPXWW3h4eDB27Fj0ej0WFhZ8+umnxg5TVDGWlpZ0796dSZMmMX78eLZt26bdgi6pA1Lv3r3p2bOn\n/N2KUkmpkTCaktoEjh8/noSEBJo2bSqt2US5k5ubS+fOnXnppZeYMGEC+fn5mJqaFjsvKysLT09P\nsrOzycnJ4emnnyYwMJDZs2fz8ccfayufAwMDefLJJw19GaIckJGvMJrCNoFfffUVGRkZ/Pjjj9y4\ncYOYmBgaNGggrdlEuWNubs7GjRvp2bMnly9f5uuvv+aXX36hevXqRc6zsrJi3759xVoP6nQ6pk6d\nytSpU410BaK8kDlfYRSFNb0vvvgi77//PvXq1WPgwIEMHjyYBg0aaK3Zhg8fbuxQhSiiVq1aWFlZ\nERgYSGxsLEOGDCnxvJJqf+Fu20EhJPkKoyis6e3ZsyevvvoqTz31FFFRUXz22WdkZ2dLazZRbi1Z\nsoSkpCSt9vfgwYN/u/YXkDaYApDkK4yksKb35MmTmJubF6npBWnNJsqvt99+u9R633uV1HrQz89P\n2mAKQOZ8hZEU1vR+9tln9O7dW6vpnT17NqamptKaTZRbdnZ2rFixAr1eT0FBAU2aNNG2kCzJ/a0H\nC0kbzKpNRr7CKAprejdt2kS/fv3o3r07PXv25J133pHWbKLcmzRpEikpKdy8eRM7OzvWrFlT5Hhp\ntb/SBlMUkpGvMJpXXnmFESNG4OrqSosWLQgJCQHgyy+/lIVWotwr3Onq448/xtPTkwEDBtC4cWOg\n9NrfUaNGFWuDKaomqfMVBnF/Te/atWsZP3482dnZ2NraYmZmxqpVq+jUqZOxQxXigc2fP5+ff/6Z\nbdu2cebMGW1xVaHS6n4LBQUFERAQQHJyMnZ2doYOXxiB3HYWBlFY03vmzBkiIyP5/vvvSUxMZPXq\n1URFRTF37lxmzJhh7DCF+Edef/114uPjad++Pe3atePkyZNFjhfW/R4/fpzffvuNffv28eOPPwJw\n6dIlfvjhB5o0aWKM0IWRSPIVZa6wpnf48OHMnj2bevXqsWjRIho3bsytW7eAuyPjhg0bGjlSIf6Z\n0NBQfv/9d06fPo1Op+O1114rds79db+FI9ypU6fy3nvvGTReYXwy5yvKXGFN76OPPsrJkycZOnQo\n77zzDh07dmTq1KlMnz6dgoICfvnlF2OHKsQ/YmpqSl5eHjExMQDo9Xqys7OLdEAqKCigffv2nD9/\nHj8/P1q1akVYWBhOTk60adPGWKELI5GRryhzhTW9SUlJWk3v6tWrycnJYenSpSQkJLBs2TLGjh1r\n7FCF+EeeeeaZIns8l7SU5v663+3btxMYGMicOXP+9HmicpIFV6LMXb9+nS5durB3715atWpFbm4u\nBQUFWFhYkJWVBdx907G1tSU9Pd3I0Qrxz6xduxZ/f3/tb/nChQtYWVmVeO68efPQ6XSsXLlSux19\n+fJlGjZsyOHDh6lXr54hQxdGIMlXGETPnj1xdHSkXr16ZGZmkp6ezsWLF7UWgnv27OG///0vkZGR\nxg5ViH+scJvJ4cOH07ZtW2bNmgXcrfs1MzPD1taWO3fu0LdvX2bNmkXv3r2157q4uHD06FFZ7VxF\nyJyvMIjx48czfvx4XF1dcXV1JSQkhLNnz/Lyyy+TnZ2NtbU1a9euNXaYQvwrhXO8wcHBeHh44Ovr\nS/PmzUut+73X39myUlQeMvIVZeL+ut7c3FzGjRtHWFgYGRkZODs7s2nTJmrUqGHsUIUoE0FBQeza\ntYvw8HAiIyPp0aNHkeOl1f6+/fbbhIeHo9PpsLe3Z/369TRq1MhIVyHKiiRfUSZGjx6Np6cnI0aM\nYNmyZezcuZOMjAyWLl1Kjx49CAkJIT4+nrlz5xo7VCHKRG5uLi1atODOnTukpKRw+vTpYl26MjMz\ni/T8XbJkCW3bttU+lK5cuZLo6Gg+/vhjY1yCKEOy2lk8dIV1vadPn8bOzo533nmH3r17c/bsWe3T\nf58+fdiyZYuRIxWi7EybNo2EhARu3ryJUoq33nqr2Dkl1f7eezfo9u3b1KlTx2AxC8OROV/x0MXH\nx1NQUMDSpUtp1aoV7u7uBAYG0rZtW8LCwnj66afZvHkzly5dMnaoQpSZtm3bYmJi8sC1vwAzZ85k\n48aN2NjYcOjQIaPEL8qWjHzFQ5ebm0tCQgImJiaEh4djY2NDTk4Oa9euZdWqVXTs2JHbt29jYWFh\n7FCFKDMjRox44NrfiIgIABYsWEBCQgJjxozB39/fUCELA5KRr3joGjVqROPGjfnjjz9o2bIlBQUF\nNG/eHL1ez65duwCIi4vju+++M3KkQpQdKysrgoODtb6/jzzySKkfOEvr+evr60v//v0NFLEwJBn5\niofO0dERMzMzXF1dOX/+PM899xyDBg3ixo0bwN1bbfPnz8fPz8/IkQpRtiZOnEhKSgo3btxAp9Px\nzTffaMdK6/l77tw57ZywsDA8PDwMHrcoe7LaWTx0ycnJNG/enIYNG2JqakrTpk0JCQlhw4YNfPDB\nBwD4+PiwcOFCI0cqhOHs2bOH8ePHc/r0aaytrTlx4kSx2t+AgACGDBlCbGys9tpZvXq17HhVCUny\nFf9KbGwszz//vPb1hQsX0Ov1tGrVivj4eH7//XecnZ0JDQ3F1tbWiJEKYXxDhgyhbdu2vPzyy9y4\ncYMWLVoUOV5a7W9AQADbtm3DwsJC+zBbq1YtI12FeBgk+YqH5s6dO9pmAL6+vjg5OTFjxgzeffdd\nUlNTWbRokZEjFMK4zp8/T5s2bbCwsMDZ2ZkjR44UWZQFJdf+3rlzh969e2NiYsJ///tfAHk9VXAy\n5yseilmzZlG7dm1SUlLo378/33//PaNHjwbubrhx71yXEFVRRkYGQ4YMITs7m4yMDE6dOvW3a3+9\nvb0xMbn7dv3YY49x+fJlg8YuHj5JvuJfu3DhAoGBgQwcOJDZs2ezZcsWEhMTcXBwAMDBwYHExEQj\nRymEcVWrVo1mzZpptb8xMTGsWLFCa8ZQqKCggHbt2uHg4ECvXr202t9C69atkxXQlYAkX/Gvbdq0\nCaUUe/fupV+/fuTn5xc5rtPpZNN4IYDAwEDMzP68wrO02l+4W/9rYWGBr69vGUcqypokX/GvvfXW\nWzRt2pTU1FS6devGkCFDcHBw4Pr16wBcu3ZNVmsKATRr1ozly5ej1+txc3Nj+PDhRXa8ute9tb8A\n69evZ/v27WzatMmQIYsyIslX/GvJycnEx8cza9YsVqxYwfr16xk0aBAbNmwAYMOGDQwePNjIUQpR\nPhTW/u7YsYPvv/+eO3fuaMdKq/3duXMnixcvJiwsDCsrK2OFLh4iWe0s/rVJkybx6aefkpSUpG0K\nn5KSwtChQ0lISJBSIyFK4ePjQ4cOHXjzzTcBSq39bd68OTk5OdjZ2QHQpUsXVq1aZczQxb8kyVc8\nkPvres+dO4dSihUrVhAUFERMTAyRkZG0b9/eiFEKUTGcO3eOzp07c+rUKZRSODg4FFkfUVrd7+bN\nm5k9e7a83iow2dtZPJAWLVoQFRUF3L0tVrt2baZPn07Xrl3p3r07kyZNMnKEQlQczZo1Y9iwYfTt\n25eYmBi++OKLIlM0VlZW7Nu3r0jd748//oher2fr1q3yeqvAZM5XPLDMzExWrVqFra0tOTk51K9f\nn5YtW+Lq6mrs0ISoUHbs2MGmTZs4ffo0BQUF+Pv7F+t+VFLdr7zeKj5JvuKBzZ07l9dee41BgwYx\ne/ZsZsyYUaxWUQjx1xwdHcnMzNTqfpOSksjJySlyzl/V/YqKSZKveCBpaWmsXLmySF2vEOKfadWq\n1T/u+SsqNkm+4oHY2tqyc+dOAK2uNzAwsNRaRSFE6SwtLbWev+7u7jRo0OBv9fwVFZ8kX/HAfvvt\nN+rUqcOrr77K+vXrmTx5cpHjsoBeiL+vsO43JSWF6tWr/62ev/eS11vFJKVG4oHcvn2bpk2bkpOT\nQ0JCglbXu3XrViZPnkxycjK1atXCw8ODHTt2GDlaISqWHTt24O/vz8mTJzEzMyu17ldebxWfJF/x\np+6v6z1z5gzu7u48/vjj0l9UiIdMKUWvXr144YUXGD9+PPn5+UXmhEur+01JSWHYsGHSP7sCkeQr\n/pbs7GwSExNxdnbmwIED0l9UiDLy66+/MnjwYLp164aNjQ2ffvppkeMl9fsNDw+nTp060j+7ApE5\nX/GX1qxZg52dHc2aNaNmzZp0795d+osKUQZu377Nxo0buXHjBuHh4XzxxRfMnj27yDn31/3Wrl2b\n8PBw6Z9dwUjyFX8qKyuLyZMnc+LECZ5++mkyMzOL1fRKf1EhHg5ra2v27NlTpOfv4sWLi7zm7q/7\ndXd3l/7ZFZAkX/GnvvvuO5RS5OTksHfvXkxMTIqsrpT+okI8PKampgQGBmp3lUpyf93vvn37ihyX\n/tkVg+ztLP5U586dad++Pa1bt6agoID27dtrLc0K+4vu2bPHyFEKUXkMHjyYFStWoNfryc3NLbWO\nvrDu9+jRo1r/bEdHR+mfXUHIyFf8qYYNG/LII4/QtGlTunbtSkhICID0FxWiDBXW/o4dO5Zr165p\nj5dW9yv9syseWe0s/tSJEyfo06cPeXl5XLx4Uavrlf6iQpS9q1ev0rp1a06ePEmDBg1KrfuV/tkV\njyRfUcT9db0nT55kwIABtGnThrCwMHQ6Hfb29qxfv55GjRoZMVIhqoZp06aRnZ3N+++/X+zYpUuX\nGDVqFElJSeh0OiZOnMjkyZOJjo7mpZdeIiMjA2dnZzZt2qR9cBblgyRfUaLs7GyOHDlCz549iYmJ\nwdHRUXvxrly5kujoaD7++GMjRylE5ZeUlETLli2ZNWsWhw8f5rPPPtMWVF2/fp3r16/Trl07bt++\nTYcOHfjmm28YNWoUS5cupUePHoSEhBAfH8/cuXONfCXiXjLnK4pZu3YtdnZ2eHl5YW9vT/PmzYt8\nar59+zZ16tQxYoRCVB2//vorSikCAgLYvHlzkb3UHR0dadeuHQDVq1fHzc2NK1eucPbsWXr06AFA\nnz592LJli1FiF6WT5CuKuHXrllbXO3jwYNLS0rQaw5kzZ9K4cWM2bNig7WolhChba9asISMjQ6v7\n/eSTT0rsn33x4kWioqJ47LHHcHd3JywsDIDNmzdz6dIlQ4ct/oIkX1HEhg0bKCgo0Op6791XdsGC\nBSQkJDBmzBj8/f2NGKUQVcesWbOK1O2WVMN7+/ZthgwZQnBwMDVq1GDdunWsWrWKjh07cvv27VLb\nFArjkTpfUUTTpk2pXbu2Vtf7+OOPF6sx9PX1lR2thDCQTp06sXLlSq3u95133inymszNzcXHx4cX\nXnhBKzFq0aIFu3btAiAuLo7vvvvOKLGL0snIVxTRv39/2rZtS5MmTWjSpIm2qfvZs2e1c8LCwor1\nFBVClJ3Cut8ZM2Zw/vx57XGlFOPGjaNVq1a89tpr2uM3btwA7m5FOX/+fPz8/Awes/hzstpZFPHL\nL78wbNgwMjMziY+P1xZaDRkyhNjYWExNTWnatCmrV6+WXXSEMLDU1FSaN2/Or7/+StOmTfnxxx/p\n2bMnbdq00W5HL1y4kLNnz/LBBx8A4OPjw8KFC40ZtiiBJN8qrqS6Xh8fH7744gsAgoKCCAgIIDk5\nWdtQQwhhPLNmzeLy5ct88sknxY6VVvd7+PBhXnnlFXJzczEzM2PVqlV06tTJCNELjRJCKZWVlaUi\nIiKUiYmJOnfunFJKqYSEBNW3b1/l7Oysbt68aeQIhRBKKZWSkqLs7OxUWFiYCggIUAUFBdqxa9eu\nqaioKKWUUn/88YdydXVVp0+fVp6enmrnzp1KKaW2b9+uvLy8jBK7+H+y4Eqwdu1a/P39ycnJwd7e\nnqZNmwIwdepU3nvvPZ5++mkjRyiEKHT58mWsra0ZMmQIcLft54oVK4C7db+Ojo5A0brf+vXrk56e\nDkBaWhoNGzY0TvBCI7edq7iMjAzs7e05ffo0r7/+OmFhYfzxxx/s3LmTiIgIli1bhouLC0ePHpXb\nzkKUA4MGDWLnzp3ExMQA0Lp1a1JTU4tVJVy8eBFPT09OnTrFzZs36d69OzqdjoKCAn755RfZHtbI\nZLVzFffll18Wqes1MzMjMzOThQsXMmfOHO08+YwmRPnw5ptvFvn6r+p+q1evzrhx41ixYgUJCQks\nW7aMsWPHGipcUQpJvlVc9erVqVGjhvbp+dFHH+Xy5ctcvHiRtm3b4uLiwuXLl+nQoQNJSUnGDleI\nKq9z585av9+WLVv+rbrfw4cP88wzzwB3KxcOHz5slNjF/5PkW8UNHTqUXr16Ub9+fRwcHFi3bh16\nvZ7ExETi4+OJj4/HycmJY8eOSWmREOXESy+9REpKCpMmTSIxMVF7XJVS99usWTP2798PwN69e3F1\ndTV4zKIomfOt4mJjY+nevTsFBQXEx8dTs2bNYuc88sgjHDlyROZ8hShnrly5gl6vJy4ujjp16pRa\n91u3bl1efvllsrOzsba2ZtWqVbJRjpFJ8q1i7q/rPXXqFL179+axxx7j448/pm7dugAEBgby5JNP\nGitMIcTfNGnSJOrWrcv8+fOLHSut7nfYsGHExcUBd1c/29raEhUVZejQqzRJvlVUdnY28fHxtGrV\niujoaL7++mtq1KjB1KlTjR2aEOIBXLhwgUcffZTPP/+c48ePExAQoB0rrd+vm5ubds706dOxtbXl\nrbfeMkb4VZbM+VZBhf1627Rpg62tLXq9HqWUrGgWogK6cuUKSikGDBjAzJkzmTt3rnaspH6/V69e\n1Y4rpQgNDWX48OEGj7uqk+RbxWRnZzNlyhROnDjB008/ze3bt8nOzkan07Fy5Uratm3LuHHjSEtL\nM3aoQoi/oJTi5Zdf5o8//tD6/S5atOgv+/0WOnjwIA4ODtrGOsJwJPlWMVFRUeTn5xfr1+vn50d8\nfDzHjx+nfv36TJs2zciRCiH+ik6n4/XXX3+gfr/Vq1fXHv/888/x9fU1SKyiKNlesooxMzOjSZMm\nWr/e5s2bY2lpWaSMaPz48QwcONCIUQoh/i5fX18yMjJo3bo1eXl5BAcH/2XdL0BeXh5bt27l2LFj\nxgi7ypORbxXTsWNHxo0bR506dWjTpg3Lli0D4Nq1a9o5W7duRa/XGytEIcQDKFzFnJqaSteuXalW\nrZp2rLS6X4Ddu3fj5uZGgwYNDB2yQFY7VzlZWVk0adKEnJwcEhIStH69o0aN4vjx4+h0OlxcXFiz\nZg0ODg5GjlYI8SC+//57pk6dym+//YaJiUmJdb+FZYQvvvgiXbp0YeLEiUaOumqSkW8VEBsbi4eH\nBx4eHri6upKSksKcOXOoUaMGK1euxM3NjWPHjtGvXz+io6P55ptvJPEKUQF5e3tjYWHB9u3bAbQN\ndI4fP054eDi2trZMmzaN1q1b4+HhoSXewveB1q1b8/rrrxvzEqoMGflWIZmZmbRu3Zr09HSOHz/O\nuXPnWLhwIdu3b8fc3JwbN25om2wIISqmL774gqVLlzJo0CCaNGnCyJEjgdJrfq9fvy7vA0YgybeK\nWLt2LVOmTCEvLw9nZ2fOnj3L0KFDeemll3j88ceNHZ4Q4iG4efMmI0aMYO/evQDUrl2bK1euYGZW\nfG3t4MGDeeWVV/joo4+YNGmSvA8YmNx2rgJWr17NlClTOHXqFIMHD+b3338nOzubs2fPcuDAATp3\n7oyXlxdHjhwxdqhCiH/hjz/+YO/evVrN761bt8jMzCx23r01v3FxcfI+YARSalTJ5ebmsmDBgiK1\nvYWfgvPy8khNTeXQoUNERkYydOhQLly4YOSIhRD/VP369bXafbi7EvresiMoWvNbo0YNeR8wEhn5\nVnI6nY4lS5ZgYWGh9eydP38+lpaWODk58eyzzwLQqVMnTExMuHnzppEjFkL8U5aWlgQHB6PX63F3\nd6djx45/WfMr7wPGIcm3kjMzM0Ov11O9enW6du2Kr6+v1jxh8ODB2txQXFwcOTk52NvbGzNcIcS/\nNHHiRFJSUjh37hwnTpzg+vXrQOk1v/I+YByy4KoSKqltYK9evYiIiKBFixaYmpqSlpZGrVq10Ov1\nHD9+HAsLC4KCgvDy8jJe4EKIh+o///kP9vb2zJs3r1jNb25uLiYmJuTl5XH16lVsbGyoX78+er2e\n3bt3S3vRMibJt5K7cuUKjRo14vjx47Rp00Z7XNqICVH5nT17lq5du3Lx4kVMTU2xsrLSjpVWehQa\nGirtRQ1AbjtXYtnZ2bz22ms4OjoWSbzSRkyIqqF58+a0b9+efv360aBBgyLbyJbUbvDKlSsA0l7U\nACT5VlLLli3Dzs6OLVu20LFjxyLHpI2YEFXD9OnT2bt3L4cOHeL27dvF9ncuVFh61LlzZwBpL2oA\nknwroYsXL/L6669z9OhRateuze7du4v095Q2YkJUDU5OTuh0Oq3ud9u2bcV6/d7fblDaixqGJN9K\n6NChQwDs378fd3d3CgoKtGOFbcSGDRtmrPCEEAYyatSoEne3KlRS6VG9evXQ6XTodDrGjx/P4cOH\nDRVulSKbbFRCzz//PDt37sTPzw8TExOGDh2q1fpJGzEhqg47OzuWL1+OXq8nNzeX6dOna+8FpZUe\nXbt2jfr16wPSXrQsyWrnSuL+8qITJ05gamrK5s2bmTNnDgUFBZiZmeHo6MjAgQOljZgQVUh2djbB\nwcFERUXx+eefAxQpPcrNzSUhIQE7OzvS09OxsbGhbt26WnvRzz77jICAAJKTk7GzszPy1VQOknwr\noSNHjmh7to4bN4433niDvn37smPHDt577z327dtn7BCFEAaWnp6Oi4sL0dHRNGrUqMix0sqO3Nzc\nuHTpEhMmTCA2NpajR49K8n1IZM63ksnOziYgIIDGjRvTtGlT6tevT3p6OgBpaWk0bNjQyBEKIYyh\nVq1ajBo1ikWLFjFv3jxOnTqlHSup7Ojq1asATJ06lffee88oMVdmMudbiaxduxZ/f3/u3Lmj7dW6\naNEiunfvzvTp0ykoKOCXX34xcpRCCGM4f/48v/76K0ePHgXg+++/5+DBg8XOu7fjUVhYGE5OTkX2\nCRAPh4x8K4no6GgmT56slRdt376d7Oxsxo0bx4oVK0hISGDZsmWMHTvW2KEKIYzgyJEjHD16VCs7\nOnr06J+WHZmYmLBw4ULmzJmjHZdZyodHkm8lsXbtWgoKCrTyokKHDx/mmWeeAWDIkCFSNiBEFTVw\n4EBMTP7/LV+n0xU5fn/Z0fnz57l48SJt27bFxcWFy5cv06FDB5KSkgwdeqUkt50riUWLFnH06FGt\nvGjcuHFYWlrSrFkz9u/fj6enJ3v37sXV1dXYoQohjMDGxoYVK1ag1+vJy8vjpZde+tOyI71eT2Ji\novZ8FxcXWXD1EMlq5wru3hKjmJgYsrKyGD16NK+88gqvvvoqycnJJCcn4+TkRPXq1Vm1ahUeHh5G\njloIYSzZ2dl8+umnfP311+zYsQMoWnaUl5fH77//Tu3atalZsyYTJ05k8uTJ1K5dmwYNGmBubo69\nvT3r168vtmpa/H2SfCuJiIgI/Pz8SEtL49dff8XHx4elS5fSo0cPQkJCiI+PZ+7cucYOUwhRDmRl\nZdGkSRMOHDhAixYtihwrrezIycmJGjVqAHf3fo6Ojubjjz82RviVgsz5VgLZ2dksW7aMPn360KxZ\nMxo3bszZs2fp0aMHAH369GHLli1GjlIIUV5YWVkxceJEli5dSmhoKCdOnNCOlVZ2VJh44e7CrDp1\n6hg87spEkm8Ft3btWuzs7NixYweRkZFam0B3d3fCwsIA2Lx5M5cuXTJmmEKIciQ1NZXjx48TEhLC\nCy+8UOqOd/eWHQHMnDmTxo0bs2HDBv773/8aMuRKR247V2DHjh2ja9eunD59mpycHG03GicnJ2Jj\nY5k8eTI3b95k0KBBrFixguTkZGOHLIQoB8LDwxkyZAgxMTHA3Q/raWlp2gIsuDu69fLy4q233tKa\nLhRatGgRsbGxhISEGDTuykRWO1dg69at0zoW7d+/HxMTE+rWrQtAixYt2LVrFwBxcXF89913RotT\nCFG+eHt7Fyk7uvf/oeRuR/fy9fWlf//+ZR5nZSa3nSuowMBA9u7di7m5Oc2aNcPPz48uXbrw1FNP\n4erqipeXF2lpaRQUFDB//nz8/PyMHbIQopywtrbWyo7c3Ny00kT4/7KjRo0aERYWhru7O61bt+at\nt94CICAggE6dOnHlyhWeffZZbfta8WAk+VZAFy9e5KOPPuLYsWM0b96czp07Y2VlRfv27fH29iYu\nLo6aNWvStGlT3NzccHJyYsyYMcYOWwhRjkycOJGUlBRWrlzJ+fPntcd/+uknPvvsMw4dOsS1a9ew\nsLBg7ty5BAcH4+rqytdff023bt2IjY3F1dWVwMBAI15FxSW3nSugmjVrYm5uzr59+8jMzMTJyYmw\nsFmyfkYAABIvSURBVDBeffVV9u/fD9xdiOXl5aXN6QghxP0sLS0ZOXIkM2fO5Ny5czRr1ozu3btr\n01n3+vTTT3n11Vfp3bu39thjjz0mlRT/kIx8KyA7OzsmT57MoEGDuHbtGrVr18bb25vExEQcHBwA\ncHBwKLI7jRBClMTa2pqxY8cya9YsJkyYQHR0dLFz7l/1XGjdunUy9/sPyci3Apo9ezZz587F1NSU\nOXPmcPDgQT777LMi5+h0umJ7twohxP0OHDjAli1bSEhIACA+Pp7du3drx+9ttlC9enXt8QULFmBh\nYYGvr6/BY64MJPlWMGfOnGH+/Pk899xzBAYGotfrWblyJb/88guOjo5cv34dR0dHrl27Rr169Ywd\nrhCinLt+/ToJCQnaFJVeryc7OxtLS8tSVz2vX7+e7du3s2fPHmOFXeFJ8q1ggoKCyM/P55tvvuH2\n7dvk5uYSEhLCuXPnSExMZOHChaxYsYINGzaUWCIghBD3GjBgAGZmxVPBvc0WfHx86NWrF0lJSWRk\nZJCVlcXJkyf59ttvmT17NjExMURGRtK+fXsjXEHFJMm3Arl48SJ79+5l3Lhx2idPGxsb7O3t+fDD\nD3nppZc4dOgQrq6uODs7ExoaauyQhRDlXLVq1Vi+fDl6vZ7c3FymTp2KpaUlP/74I5999hlt2rTh\nhx9+IDc3l+XLl/PKK69w8+ZNbftaDw8Pucv2D0jyrUAKVznHxsbyySefEBoair+/P3369AHAzMyM\n4OBg+fQphHggEydOZPTo0QQHB2v7PJe26vn8+fMMHjy4yMrnXr16GTTeykBWO1cg1apVY+jQofz0\n008EBARgZ2enJV4hhPg3LC0tGT9+PNu2beP8+fP88MMPJZ5X2spn8WAk+VYQa9f+X3t3HhtVuf9x\n/D1LO20R6JK2bIWyt9CypwQlBS4IxIJoUKFBSAORSExskNBEMEJB9k0aQRBsEAURiZiIYARMgLKV\nssjWRYVSkCrLtEALM22Hc//oj7mXH3p/9/7iPWcKn9dfM3mmyfckzfnkec73PM9HhIeHM2/ePJ59\n9lmuXr1KVVUVmzZtsro0EXlM3LlzhyZNmpCYmEhaWhqLFi16aPzPOp/lP6fwbQDmzp3L66+/TkRE\nBHFxcRw4cICsrCyOHj1KZmamtngTkb/EyJEjKS8vp6ioiKKiIubMmYPX6wXq93tOS0ujsrKSmTNn\nkpSURE5ODgB1dXVMmTKFTp06MXToUCorK628jAZB4RvgSktLyc3Nxel0sm7dOtxuNzU1NaSkpDBk\nyBCys7Mf2uJNh1SJyP9Xdnb2v+x87ty5M9u2bePcuXMcOXKEVatWUVhYyKVLl+jbty8lJSUMHjyY\nhQsXWlB9A2NIQLt586bRqVMnY+nSpYbT6TQAw263G127djUmTJhgeL1eIysrywgLCzNCQkKM2NhY\nY/jw4VaXLSIN1Nq1a42QkBAjODjYWLt2rWEYhnHgwAHDZrMZ3bt3N3r06GH06NHD2Llzp5GSkmJE\nR0cbNpvNiI6ONoYPH26Ul5cbnTt3tvgqAp/O820APvroI6ZOnYrH42HMmDFs2LCB4OBg//jIkSNJ\nT0/XTjMi8pdwu9106NCBU6dO0bp16z/8TWlpKQMGDODs2bO0bt2aiooKoH6WHBkZ6f8uf0zLzgHu\nl19+YcWKFYwcOZI5c+Zw7949vvzyS/+4tngTkb9aZGQkr776KqtXryYvL48rV648NF5VVcXo0aNZ\nuXIljRs3fmhMW9v+ezTzDWALFixg8eLFVFZWYrPZmD9/PocPH+bgwYO0atWKmpoawsLCyMvLIyQk\nxOpyReQxUVVVxbhx49i1axcAgwcP9n+ura0lPj6e27dvEx8fz5kzZ0hISGDVqlW888473Lp1i7Ky\nMsrLyx8JZvkHzXwDVGlpKWvWrKGqqoqOHTsydOhQ3n33XUJDQ8nOzmbhwoVUVFSQkJCg4BWRv9Tn\nn3/Orl27/F3P+/fvx+v1+huvnnnmGfbv3+///fPPP09GRgaLFy9mwoQJpKamsmTJEguvIPBp5hug\n3G43vXv35tdffyUzM5MPP/yQ6upqxo0bR25uLl27duXGjRsEBwfTokUL+vXrx+rVq60uW0QeAxUV\nFbRs2ZKzZ88C9YctuN1ujh07RmpqKt26daOmpoaLFy+yfft2UlJSiI2NpW3btsTHx7N8+XLGjBnD\nuXPnLL6SAGZdr5f8X9auXWsEBQX5O5zHjx9vGIZhzJgxw4iLizM6d+5sVFRUWFyliDyO1q5da4SF\nhRlBQUHGe++998j4xYsXjaSkJP/3p59+2vj6668NwzCMZcuWGY0bNzat1oZIy84B6kGj1ahRo5g3\nbx4jRoxg2LBhQH2TVVlZGRkZGUydOtXiSkXkcTR58mTcbjdTpkyhsrLSv9nGn8nNzWX16tX06dOH\nqqqqh97IkEcpfAPU2LFjKSoqYtu2bWzbto1Ro0Zx6NAhoP5YQbvdznPPPcexY8csrlREHlcul4vI\nyEhWrlxJ06ZNGTlypH8sKyuLwsJCkpOTAejcuTNz587FbrezZcsWPB6P7k//gsI3AB05coSCggLa\nt2/PuXPnOH36NOvXr6dLly4cOHCA3bt306ZNG3bu3EnPnj2tLldEHlOnT59m/vz5FBUVcf78efbs\n2eOfAb/00kvEx8f7f3v9+nWysrLIzs6mT58+TJ48maysLIsqD3w6UjAAXb58Gag/5HrUqFH4fD5C\nQ0N57bXXaNeuHaGhoVy9epWDBw/y8ccfW1ytiDyu/vcGG3Z7/XwtPT2dffv2cf36daB+ybmqqooT\nJ04wadIkMjIySE5O5tq1a6bX3FCo2znALFiwgM8++4xLly5RXV0N1J/j27hxY4KDg6mqqmLjxo1M\nmTKF48ePExkZaXHFIvI4e7DDXm1tLbNnz2bGjBn+sdLSUnr27ElwcDAxMTHs2LGD/v3743a7qa2t\npWPHjty9e5fw8HBOnjxp4VUEHi07B5DS0lLWrVvHpk2bcDqdJCYmsmbNGlq0aEFqaioxMTH8/PPP\nDB8+HNAhCiLy3/eg8Wry5Mncvn37kcariIgIvvvuOwAmTZpETk4O1dXVbNq0iRYtWjB69GhGjx5t\nRekBTeEbQJo0acLdu3dJSUnh9u3bALRv357ExETy8/MpLS2le/futG3blitXrtC7d28t64jIf53L\n5SIqKorly5fTtGlTRo0a5R9r1KgRERERAOTn5/Piiy8C9c+E8/Pz2bp1K+np6ZbUHcgUvgGktraW\nmzdv4nA4sNvtFBcX079/fy5cuEBFRQWxsbH87W9/4+TJk7Rq1YoTJ04QExNjddki8pgrKChgwYIF\n/sar3bt3/+GrRx06dGDfvn0A/PDDDzRv3pzY2Fjat29vdskBTw1XAWTGjBnU1dXRpk0bgoKCKCoq\nokOHDsTExBAdHY3P56OwsJBp06Zp43IRMU3btm39zVZQf3jC+PHjycvL48aNG3Tp0gWPx0O7du3I\nysrC6/USGhpKSEgIZWVlJCUlkZaWxqJFiyy8isCi8A0QpaWlfPPNNyQlJVFcXIzP56Njx4506dKF\n6upqjh49SlBQEMePHycjI4MLFy5YXbKIPCGioqLIyckhOTnZ33g1bdo0XC4XAFu3bmXmzJmEhIRw\n9OhRAPbs2UNaWholJSW0adPG3xkt9dTtHCDS0tLYuXMnAM2aNaN79+6cOnWK27dvExsbC0B4eDgT\nJkzg2LFjbN682cpyReQJ5PV6GTJkCAUFBdhsNrKzs5k+fbq/H6W6uprExETOnDlDamoqJSUlOJ1O\noqOjgfq3OR40jD7pFL4BoLi4mMTERGbNmsW8efOora0lNDSUl19+mR9//BHDMDh//jw2m41+/fqx\ndetWfyCLiJilpKSEbt26cf78eQCSkpIYMWIEeXl5/pltbGwsV65cISoqiqioKLxeL23btmXp0qX0\n6dPHyvIDihquAsCGDRswDIN58+YxcOBA7HY77du3Jy8vj+LiYsrLy4mKiuKLL77g8uXLCl4RscS3\n33770CuONpuNTz/9lKtXr/LTTz8RFhbGb7/9RnJyMi1atCAyMpI333yTJUuWMGzYMOx2O26328Ir\nCBya+VqstLSUXr164fF4uHfvHlDf1t+8eXMqKyupq6sjKiqKmzdvcufOHTp06MDRo0eJioqyuHIR\nedIYhsG6devIzMzEMAyWLVvGG2+8AdTfy5KSkvB4PDidTgYNGkSzZs3Yvn07Ho8Hr9eLy+Xi2LFj\n/v2gn2Sa+VqssrKSiooKf/AGBwcTFxfH5cuXcTgchIWFcfXqVcLCwigpKaGmpkbBKyKWsNlsTJ48\nmezsbHw+H9OnT2fixIn+8cjISFq2bAnACy+8QFRUFGVlZQwcOJBGjRphGAaLFy+2qvyAom5niz14\nduJwOAgODubevXvcuHEDn89HXV0doaGh1NbWcv36ddLT09m4caPFFYvIk+zOnTvMmjWL4uJiALp2\n7UpVVRV5eXncvHkTn8+HzWZj4sSJTJw4keTkZDweD+vXr2fcuHHaEvd/aOZrsYsXLwJw//59f9t+\n06ZNsdlsTJgwgXXr1hEdHY1hGBw/fpyBAwdaWK2IPOmcTudDz33tdrv/uW9xcTGtW7fG4XAQFBTE\n7NmzadasGePHj2fKlCkYhkFmZqaF1QcOzXwtdvDgQf/nW7duAfVHcxmGQXFxMZs3b6Zfv37s2LHD\nqhJFRPxCQ0PJycmha9eu+Hw+HA4Hy5Yt48yZM+zbt49r167h8/nIzc1l+/btnDhxgpMnTxIUFITN\nZqNbt25cuHDhid+dTw1XFjp58iQpKSk4nU7atWtHYWEhMTExOBwOfv/9d/bu3cuAAQP45JNPyMjI\n0EEKIhIQvF4vERERHD9+HJfLRWJiIuXl5URGRpKXl8eQIUPweDyP/F1cXBxPPfUUhYWFFlQdWDTz\ntVBYWBhQvzH5g31SPR4PQ4cO5fDhw0ydOpX79+9z+fJlNVmJSED550dlTqeTRo0akZ6ezt69e6mp\nqSEuLo45c+bQv39/OnbsCMDdu3fp16+flWUHDIWvhfbt24fD4cDtduN2u4mIiMDn87Fq1SrGjh3L\n6dOniYyMpFu3bvTt29fqckVEgPrXIf956dlutzNp0iTu37+P0+nE6ayPFsMwePvttykuLsbhcDBw\n4EA++OADi6sPDFp2tkh+fj6pqamcP3+esWPHUlBQQHJyMu+//z6DBg3C7XbzyiuvUFZWRnx8PFu3\nbiU8PNzqskVEgPql5/DwcE6cOAFAr169uHHjBo0aNbK4soZB3c4WOXToEPfv3wdgy5YthISEkJ+f\nz6BBg4D69+X27NlDSUkJ33//vYJXRAKOzWbjq6++onfv3tTV1ZGVlWV1SQ2GwtcCOTk5ZGVlMWvW\nLJKTk0lISGDJkiX+5yciIoHO5XKxePFi5syZg81mIygoiNzc3D9stJJHadnZZA+Wanw+H0VFRXi9\nXnr16kVlZaXCV0QalFu3bhEdHU1RUREACQkJvPXWWyxcuNDiygKfZr4m83q9+Hw+/6y3e/fuZGdn\nK3hFpMEJCQnBbv9HjAQFBZGTk+N/e0P+nMLXZC6XC4fDQXp6OgUFBdjtdu34IiINksvlYtGiRSQk\nJJCcnMyMGTOw2WxWl9Ug6FUjk7lcLlauXOk/1SMnJ0ezXhFpsDIzM3E6nUyfPp358+ezYsUK3dP+\nDXrma5EHyzL6JxWRx4Huaf8Zha+IiIjJ9MxXRETEZApfERERkyl8RURETKbwFRERMZnCV0RExGQK\nXxEREZMpfEVEREym8BURETGZwldERMRkCl8RERGTKXxFRERMpvAVERExmcJXRETEZApfERERkyl8\nRURETKbwFRERMZnCV0RExGQKXxEREZMpfEVEREym8BURETGZwldERMRkCl8RERGTKXxFRERMpvAV\nERExmcJXRETEZApfERERkyl8RURETKbwFRERMZnCV0RExGQKXxEREZMpfEVEREym8BURETGZwldE\nRMRkCl8RERGTKXxFRERMpvAVERExmcJXRETEZApfERERkyl8RURETKbwFRERMZnCV0RExGQKXxER\nEZMpfEVEREym8BURETGZwldERMRkCl8RERGTKXxFRERMpvAVERExmcJXRETEZApfERERkyl8RURE\nTKbwFRERMZnCV0RExGQKXxEREZMpfEVEREym8BURETGZwldERMRkCl8RERGT/R3cpfe3L/zrmQAA\nAABJRU5ErkJggg==\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d34778f90>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 37)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d546952d0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 13)]\nrepeated subsession counters (counter, multiplicty)-- [(19, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d3461c310>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 14)]\nrepeated subsession counters (counter, multiplicty)-- [(14, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d74044e10>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 4)]\nrepeated subsession counters (counter, multiplicty)-- [(3, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d5424b290>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 26)]\n"}, {"output_type": "display_data", "data": {"image/png": 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C\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d94073950>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 8)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d7442dfd0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 51)]\n"}, {"output_type": "display_data", "data": {"image/png": 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rVikvL09er1chISFq3ry59u/fb2l657H5AgCMq+1Vo4EDB6qystL//zMyMtSm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\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d546c1dd0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 47)]\n"}, {"output_type": "display_data", "data": {"image/png": 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lpTX+O0IJYQwACIpAjjZdvJs6OztbXbt21YIFC1SvXj298MILWrNmjerVq6fd\nu3cbrNgeNnABAIwL9GhTcXGx9uzZo08//VTnzp1TWVmZ/6KI2NhYFRQUaOjQoUpLSzNduhWEMQDA\nKBNXKC5dulSxsbHKy8vTgQMH9Pjjj2vfvn3Kz8/X1q1bdeutt2rw4MHas2dPkD5F7SKMAQDGBXK0\nqbS0VFlZWerYsaNycnIUHh6u3bt3q1OnTnriiSc0b948SVJ6erqSk5ON1m0Lz4wBAEZFRUVp8eLF\nSkpKktvt1uLFi6u1mavyeXH9+vXVsWNHVVRUqF69enruueeUmZmprl27yuv1atasWTVaAg9FHG0C\nAARFTY82VU7f6tevnx5++GEdOHBAderU0Y4dO9SnTx/FxcVp0aJFysrKUuPGjYNUfe0ijAEAQVHT\nMD5+/LhSUlJUVFSkW265RT/88IPi4uKUn5+vsrIy/61N0dHRys3NVXx8fJA+Qe3hmTEAwLhAdlPf\ndNNNOnv2rBo1aqS77rpL586d0w8//KAVK1aoadOmSkxMVEREhPr27avIyMggfYLaRWcMADAq0Isi\nSktL1bZtW504cUJt2rRRUlKSEhISlJGRoZdfflmpqalq3bq1UlNTVVJSojfeeCOYH6dW0BkDAIwL\nZDd1Xl6eEhMTVb9+fTVo0ECNGzfWoEGD9O9//1sHDhxQjx49dMstt+hnP/uZMjMzTZduBWEMADCq\ncjf1bbfdpvbt21d7N7XH49HBgwfVpk0brVu3TjfeeKNeeuklRUZGqqCgQLt379b8+fP1+OOPKykp\nKYifpPZwtAkAEBQOh0MOh6Pa76u8PnHlypUaOXKkiouLdfr0aXXp0kX5+fm6/fbb5XA45HK5NHXq\n1CBUXvt4ZgwAMCrQZ8aS9H//939avny52rVrp+nTp+uHH35Qq1at9P3332vGjBnKyclRnz59dPTo\n0WB9jFpFZwwAMC6QZ8aStGjRIo0cOVLnz59X69attWLFCtWrV09jxoxRUlKSIiMjtXr1apMlW0Vn\nDAAwbtSoUVq3bp0k6ZFHHrmmgjMYCGMAgFGVy9S7du2SJN19993VXqa+3hDGAACjXC6XoqOjFR5+\n4Umox+NRWVkZYfxf8MwYAGBcWFiYfwNXp06dLFcT+jhnDAAwLiwsrMqvUTU6YwCAUVFRUXrttdf8\nAzlee+01lqj/B54ZAwCCwuVySRJBfAUIYwAALGMhHwAAywhjAAAsI4wBALCMMAYAwDLCGAAAywhj\nAAAsI4zf9D5KAAABFUlEQVQBALCMMAYAwDLCGAAAywhjAAAsI4wBALCMMAYAwDLCGAAAywhjAAAs\nI4wBALCMMAYAwDLCGAAAywhjAAAsI4wBALCMMAYAwDLCGAAAywhjAAAsI4wBALCMMAYAwDLCGAAA\nywhjAAAsI4wBALCMMAYAwDLCGAAAywhjAAAsI4wBALCMMAYAwDLCGAAAywhjAAAsI4wBALCMMAYA\nwDLCGAAAywhjAAAsI4wBALCMMAYAwDLCGAAAywhjAAAsI4wBALCMMAYAwDLCGAAAywhjAAAsI4wB\nALCMMAYAwDLCGAAAywhjAAAsI4wBALCMMAYAwDLCGAAAywhjAAAsI4wBALCMMAYAwDLCGAAAywhj\nAAAs+3/JUTD3sEaCFwAAAABJRU5ErkJggg==\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d5465fe50>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 13)]\nrepeated subsession counters (counter, multiplicty)-- [(11, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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RnXQUJ1/SGf/+979RWVmJu3fv4siRI7h+/broSKQnTE1NsWnTJuTl5eGLL7544q8vLy+H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\nQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d5457f850>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 105)]\nrepeated subsession counters (counter, multiplicty)-- [(48, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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BAJmZmdxzzz1069aNpqYmvF4vmZmZ/P73v7dz2CIiYhOd8zWZ2+1m4sSJFBQU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\n5OTkMHny5OgRJOg8uEVE5Ny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"text/plain": "<matplotlib.figure.Figure at 0x7f7d145da390>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 42)]\nrepeated subsession counters (counter, multiplicty)-- [(3, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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vv0RGRgYGDRoElUoldTS9wpFvCbBmzRqMGzcOOTk56NOnDzZu3FjgnPT0dJiammq6IC1a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\nZSy+REREWvb/AJAIs2w/N6l1AAAAAElFTkSuQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d743c6d10>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2)]\nrepeated subsession counters (counter, multiplicty)-- [(1, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d54404c50>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 12)]\n"}, {"output_type": "display_data", "data": {"image/png": 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FTQZAClnHzcVcNJvNmDNnjnu3AJzHyjcH9Xo9KpVKlEqlqNfrRY8DQJex8p1lzWYzKpVK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\nABL7P748H+A+8a1rAAAAAElFTkSuQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d7437b8d0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 5)]\nrepeated subsession counters (counter, multiplicty)-- [(8, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d34089d10>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 4), (1, 26)]\nrepeated subsession counters (counter, multiplicty)-- [(1, 3), (2, 3), (3, 3), (4, 3), (5, 2), (6, 2), (7, 2), (8, 2), (9, 3), (10, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d34610710>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 18)]\n"}, {"output_type": "display_data", "data": {"image/png": 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++efV19fHtjMAZAi2nTPcUA9cXbvtHI/HNXv2bO3Zs0cFBQUqKSnR1772NT399NOGJgeQ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\nCwCAYcQXAADDiC8AAIYRXwAADPt/4TAgeVdw/LwAAAAASUVORK5CYII=\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d744ffc50>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 19)]\nrepeated subsession counters (counter, multiplicty)-- [(7, 2), (8, 2), (9, 2), (10, 2), (11, 2), (12, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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jxz/+cVx88cWJpgbg5YhvwbVarejt7fVgBYASse1cYM1mM7Isi1qtFs1mM+9xAFggVr4F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\nTkSuQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d540fc7d0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 11)]\n"}, {"output_type": "display_data", "data": {"image/png": 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JfffdFxdccEHs2LEj7yl9ZSTvAQAMru9+97vx+OOPx69//eu8p/QdgQVgVfbt2xdf+9rX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\nrkJggg==\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d545869d0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 11)]\nrepeated subsession counters (counter, multiplicty)-- [(17, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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ffPKJbDab6ThwM3l5eWrbtq1atmypESNGmI4DN8bkC5cSHx+vXbt28fANlIi3\n335bXl5eevPNN01HgZtj8oXLSU1N1f/8z/9oxYoVatCggek4cBMrVqxQbGystm3bpsDAQNNx4OaY\nfOFyQkJC9P7776tr167KzMw0HQdu4PDhw+rdu7cWLFhA8cISTL5wWQMHDtTRo0f12Wef8fkviiw3\nN1f33XefHnzwQb3++uum48BDMPnCZU2cOFEHDx5UfHy86ShwYcOGDVOZMmX02muvmY4CD+JjOgBQ\nVH5+flq8eLGaNm2qpk2b6p577jEdCS5m2bJlSkxM1LZt2+TlxSwC6/C3DS6tZs2amjFjhrp166aM\njAzTceBC0tPTFRcXp8TERFWsWNF0HHgYPvOFW3jhhRe0d+9eLVmyhAkG15STk6MWLVqoS5cueuml\nl0zHgQeifOEWcnJy1KpVK3Xu3Fkvv/yy6ThwUg6HQzabTYMHD9b+/fu1ZMkSLtaDEXzmC7fg6+ur\nRYsWqXHjxrr33nvVvHlz05HgZH788Ue98sorevLJJ7VkyRJt27aN4oUxnJ+D26hWrZoSEhIUExOj\nEydOXPP9sbGxCgwMVERERMFaRkaG2rVrp5CQEEVHR+v06dMlGRkl5L//bP/44w898MAD2rBhgwYM\nGCAfHx+dPXvWcEp4MsoXbqVjx47q2bOnevbsqfz8/L98b9++ffXtt98WWhs3bpzatWun1NRUtWnT\nRuPGjSvJuCgh//lnm5+fr169eunMmTMFr+/bt09DhgwxFQ+gfOF+Ro0apQsXLmjMmDF/+b4WLVqo\nfPnyhdaWLl2q3r17S5J69+6tL7/8ssRyouT855/t+PHj9c033xR6vXnz5rr77rtNRAMkUb5wQz4+\nPlq4cKGmTp2q1atX39DXHjt2rODxgoGBgTp27FhJRIRFzp07p2HDhhVa8/X11fHjx3moBoyifOGW\nqlatqvnz56tnz546evRokb6HzWbjghwXdvLkSR06dKjQxw8VKlTQvn371LdvX047wyjKF26rXbt2\niouLU0xMjPLy8q7rawIDAwvK+vfff1elSpVKMiJKSF5engYPHqxLly4VWv/oo48UFBSkHj16aPPm\nzYbSAZQv3Nybb74pLy8vvf3229f1/ocffljz5s2TJM2bN0+PPvpoCaZDSRk9erR++OGHQmvPPPOM\nOnToIElasmSJoqKiTEQDJPGQDXiAY8eOqUGDBkpISFD79u0L1mNiYrR27VqdPHlSgYGBGjlypB55\n5BF17drzYnj0AAAIyklEQVRVv/76q+666y4tXrxY5cqVM5geN2rlypVq165dobWQkBCFh4crNTVV\n3t7eCg4O1rRp0zizAWMoX3iEtWvXqlu3btqyZYvuvPNO03FQQo4cOaKoqCgdP368YK1SpUpKTk5W\nlSpVDCYDCuO0MzxCq1at9Pzzz6t79+46e/assrOzTUdCMbt06ZK6detWqHhtNps++eQTihdOh/KF\nx3jttdd05swZVaxYUeXLl1fHjh2Vk5NjOhaKyd/+9jf99NNPstvt8vb2liS99dZbatOmjeFkwJU4\n7QyPkZ2drYCAAKWkpEiSQkNDFRoaqo8//liRkZGG0+FmXO3PtkWLFvruu+8KihhwJky+8GgpKSlq\n1KiRxo0bd8VtKXAdaWlphf78bDab5s6dS/HCaVG+8Bh+fn4aM2ZMwcR7+eELubm5Gjp0qFq2bKm9\ne/caTokb4XA4NH36dLVq1UqPPfaYIiIiFBERofj4eAUFBZmOB/wpTjvD4xw9elRDhgxRYmLiFa+V\nKlVK48eP14ABA+Tlxe+mzuy3335Tv3799Mcff2j+/PkKCwsruJDOz8/PcDrgr/HTBR6ncuXKWrhw\noT799FNVqFCh0Gvnz5/X3//+d7Vv316HDh0ylBB/xeFwaMGCBYqKilKzZs20ceNGhYWFSfp36VK8\ncAVMvvBoR48e1dNPP61ly5Zd8VrZsmU1bdo0xcTEGEiGqzl58qQGDBigXbt2af78+WrYsKHpSECR\nMPnCo1WuXFlLlixRQkKCypQpU+i1zMzMQnvAwqxly5YpMjJS1atX19atWyleuDTKFx7PZrOpb9++\nSklJUevWrQvW/fz8lJqaqosXL5oLB505c0axsbEaNGiQEhMT9e6778rf3990LOCmUL7A/1O9enV9\n//33mjJliqpUqaLNmzfr119/VcOGDbV161bT8TzS6tWrFRkZKbvdrp9//lktW7Y0HQkoFnzmC1zF\nxYsX5e/vL4fDoYULF2rIkCEaOHCghg4dKrvdbjqe2zt//ryGDh2qzz//XLNmzSrYjQhwF0y+wFVc\nPq1ps9nUo0cPbdu2TRs3blSzZs20e/duw+nc26ZNm9SgQQOdOHFCO3bsoHjhlihf4DpUrVpVy5cv\nV1xcnFq2bKnJkycXPKQDxSMnJ0fDhw9Xp06dNHLkSH3yyScKCAgwHQsoEZx2Bm5QWlqaevfuLR8f\nH82dO1d33XWX6Ugub+fOnerVq5eqVKmiWbNm6Y477jAdCShRTL7ADQoODtbatWvVsWNHNW7cWB9+\n+KH4HbZo8vLy9M477+i+++7TwIEDtWzZMooXHoHJF7gJTGxFl5aWpj59+sjb21tz5sxRjRo1TEcC\nLMPkC9yE8PBwJSUlKSoqSvXr19fixYtNR3J6lzdDaNq0qTp37qxVq1ZRvPA4lC9wk3x9fTVq1Cgt\nW7ZMb775pmJiYpSRkXFT3zM2NlaBgYGKiIgoWPv0009Vt25deXt7a9u2bTcb+6ZdLePLL7+ssLAw\n1atXT507d1ZmZmahr/ntt9/UoUMHffjhh1q3bp2GDBnCBhbwSPytB4pJkyZNtG3bNgUGBioyMlLL\nly8v8vfq27evvv3220JrERER+uKLL5zmQRNXyxgdHa1//etf+vnnnxUSEqKxY8dK+uvNEABP5GM6\nAOBOSpUqpcmTJ+vhhx9WbGys2rdvrwkTJqh06dI39H1atGih9PT0QmuhoaHFmPTmXS1ju3btCv7/\nnnvu0eeff15oM4Tly5fzTGZATL5Aibj//vv1888/Kzc3V/Xq1dP69etNR7LMwYMHNXXqVCUkJKhy\n5cpshgBcBZMvUELKli2rhIQELV26VN26dVOPHj00evRot90UwOFwaM6cORo8eLCysrJUqVIl/etf\n/1JiYqLTnCoHnAWTL1DCHn74Ye3YsUMHDx50200ajh49qocfflj9+vVTVlaWJOns2bNas2YNxQtc\nBeULWKBixYpavHixhg0bpg4dOmjkyJHKzc0t8vdzptvzMzMzFR4erq+++qrQ+vnz55WYmGgoFeDc\nKF/AIpc3aUhOTr7mJg0xMTFq1qyZ9uzZo6CgICUkJOjLL79UUFCQkpKS1LFjR+MbDjz++OOqU6eO\nDh06pD/++KPQazabTXfeeacOHDhgKB3g3HjCFWCAw+HQjBkzNHz4cA0bNkzPP/+8S93vumLFCsXG\nxurIkSNXvHbvvfdq3rx5qlWrloFkgGugfAGDLm/SYLfbNWfOnILHU/r5+RlOdnVnz57VCy+8oFmz\nZl3xmt1u18iRI/Xyyy/L29vbQDrAdXC1M2DQ5U0aJkyYoPr16ys7O1t5eXnKz89XXl7eX36tid+b\np06dqrlz58putxfKGBkZqY8++kiRkZGWZwJcEZMv4ASys7NVvnx57dy5U9K/H6hxrQuyrP6nm52d\nrYCAAKWkpEj6d8ZLly5p6NCheuutt+Tr62tpHsCVMfkCTsJms5Xo+4uD3W4vdLx69Wq1atXK8hyA\nq6N8ASfg5+enSZMmKSIiQrm5ucrPz7/m15g4aTVz5syCjRQmTJhA8QJFxGlnwIlkZ2dLct4LriTX\nyAg4O8oXAACLuc6NhQAAuAnKFwAAi1G+AABYjPIFAMBilC8AABajfAEAsBjlCwCAxShfAAAsRvkC\nAGAxyhcAAItRvgAAWIzyBQDAYpQvAAAWo3wBALAY5QsAgMUoXwAALEb5AgBgMcoXAACLUb4AAFiM\n8gUAwGKULwAAFqN8AQCwGOULAIDFKF8AACxG+QIAYDHKFwAAi1G+AABYjPIFAMBilC8AABajfAEA\nsBjlCwCAxShfAAAsRvkCAGAxyhcAAItRvgAAWIzyBQDAYpQvAAAWo3wBALAY5QsAgMUoXwAALEb5\nAgBgMcoXAACLUb4AAFiM8gUAwGKULwAAFqN8AQCwGOULAIDFKF8AACxG+QIAYDHKFwAAi1G+AABY\njPIFAMBilC8AABajfAEAsBjlCwCAxShfAAAsRvkCAGAxyhcAAIv9fyCY+UX6xKzSAAAAAElFTkSu\nQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d7441c890>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 6)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d74174350>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 105)]\n"}, {"output_type": "display_data", "data": {"image/png": 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h3LhxGDdunOioRCQxzgAIwquAtau0tBSTJk3CiRMnkJ+fj9/85jcIDQ3FI488\ngqioKADAs88+i6amJrz99tuC0xKRrLgCIACvAta2pKQkxMfHY8OGDRgzZgw+/vhjHD58GNHR0VAU\nBa+99hpeeuklnDhxQnRUIpIYLwLyMVVVsXr16uuuAv75z3/ObQANGTwEOPgmwObmZjidThw6dAjh\n4eHIyMgQHZWIJMYVAAE4BKhtQw0BPv/883jyySeRlZWFCxcuwG63o6CgQHRUIpIYC4CPcQhQ+4Ya\nAjSZTFiwYAGWL1+OmpoaxMTEICYmRnRUIpIYhwAF4RCgdg01BGg0GnHs2DEcPHgQoaGhSElJQWlp\nKe8BICJhuAIgAIcAtW2oIcAjR47g7NmziIqKgslkwqVLl5CdnY3W1lbRcYlIUiwAPjYwBFhZWQmr\n1YrVq1fzJkCNGeomwJ/85CdoamqCw+GAw+FAeHg4pk2bxm0AIhKGBUAADgFq241uAgwJCfF+j8fj\nQUREhMCURCQ7FgAf4xCg9t3oJkAAeOaZZ3DLLbcgJiYGL7zwguCkRCQzDgEKwiFA7brRTYDr1q3z\nfs/69etRXV2Nd999V2BSIpIZLwISoLCwEL/97W+9Q4ArV64UHYluoqSkJCQkJGDDhg3XPQ64r68P\ne/fuhclkQnx8POrr60VHJSKJcQvAxzgEqH1xcXGw2+3fexxwZmYmzp07h/LycvT390On448fEYnD\nv4EE4BCgtnV1dcFgMHzvJsC9e/ciOzsb48aNQ09PD8xms+ioRCQxbgH42OAhQKfTia1bt3IIUGPq\n6uqQlJSEjIwMlJeXe28C3LVrl/d75syZg/nz5wtMSUSy4xCgIBwC1K4fGgIsKChAWVkZ/vznPwtO\nSkQy4wqAABwC1LYbDQFaLBb85je/QWNjIx8FTETCcQbAxzgEqH03GgLs7OxEYGAgJk+ezG0fIhKO\nWwA+pqoqIiIiUFlZCQCwWCzo6OjgG4KGdHV1ISMjAwkJCXA4HEhNTcU777yD/Px8OBwOdHR0IDEx\nEXfccQe2bdsmOi4RSYpbAD7GIUDtu9EQYG1tLQBg2rRpePXVVzF+/HjBSYlIZtwCEGDlypVoamqC\n0+nk/r8GuVwulJWVYdWqVSgrK0NQUBDWr18vOhYR0XVYAATg44C1bajHAR8+fBgdHR2YMWMGSkpK\nsGrVKnR2doqOSkQS4wyAj6mqipCQEFitVgCA2WzG1atXuQ2gMbGxsVizZg2efvpprF27Ft3d3TCZ\nTIiKisL06ypbAAANPklEQVS+ffuQlZXFlQEiEooFwMc4BKh9XV1dyMzMRHx8vHcI8N1330VmZibc\nbjeuXLmC4OBg9PX1oaenR3RcIpIUhwB9jEOA2jfwOODBQ4AGgwG9vb24cuUKAMDj8SAiIkJwUiKS\nGVcABOFNgNpVWlqK/Px8pKWlITg4GE1NTVixYgU2b96MzMxM9Pb2YsSIETh27Ji3EBAR+RqHAAXg\nEKC2JSUlwWAwoKSkBGfOnMGuXbtQVlYGp9OJp556ChUVFZg2bRr0er3oqEQkMRYAH+NNgNoXFxcH\nvV7vPff/6aefIjMzEx6PB9XV1QCAlpYWkRGJiLgF4GscApRDUlISOjo6AADp6ek4cuQIZs6cCbvd\njr6+PiiKgqamJnR3dwtOSkSy4gqAjw0eAjSbzdi4cSPf/DXoyy+/hM1mw6VLl+DxeFBRUYHt27cj\nLi4OoaGheOCBB2AymUTHJCKJcQVAEA4BatuIESMQGhoKvV6P1tZWPProo7j11luxevVqOJ1OuFwu\neDwenD17VnRUIpIUVwAEMBgMiIqKgtFohMHAk5haY7PZ4PF4cOTIERw/fhwjRozwPgr4xRdfxOnT\npxETEwOXyyU6KhFJjAXAx1RVhU6ng9VqhdVqhU6n4xCgxrS0tKCpqQm33XYbJk6ciJ/+9Kfe/f+H\nHnoIY8aMQXBwMPLy8kRHJSKJ8eOnAEajccivSRtSUlK8RwH1ej2ioqIAAB999BGmTJmCvr4+nD59\nGidPnhSclIhkxhUAHxsYAszKykJ6ejqHADXK6XSiv78fqqpizZo1OHbsGCZMmACj0YjIyEg4HA5k\nZGSIjklEEmMBEERRFCiKIjoG/YsYDAYcOXIElZWVeOqpp3Dq1Cn09fXhwoULOHPmDJYuXQqn0yk6\nJhFJjAXAx3gRkPYNDAF6PB709vbi4MGDyMrKQlpaGo4ePQqPx4MdO3Zg1KhRoqMSkcQ4AyAAZwC0\nbWAIMCkpCQBw9913Y9asWYiMjMTDDz+M9vZ22Gw2bN++XXBSIpIZVwB8jBcBaV9KSgpiYmIwatQo\npKWlYf/+/Th27Bjy8vLws5/9DO3t7QgJCUFRUZHoqEQkMa4ACDKwREzaNDADEBERgRdeeAGnTp2C\ny+XCX/7yF5hMJpw+fZqrP0QkFFcAfGxgBuDs2bOorq7mDIAGDTUDYLFY8Oabb2L69OnIyMhAQkIC\noqOjRUclIomxAAjAGQBtGzwDEBUVhdjYWMycORO1tbUoKirCpUuXcPvtt6O0tFR0VCKSGLcAfGzw\nDIDT6cTWrVs5A6AxKSkpiI6ORkREBDwej3cGoLGxEb29vRg1ahSampowZ84cNDU1iY5LRJLiw4AE\n4cOAtC0lJQWnT5/2zgAEBwfjrbfewsyZM/HGG28AANLS0lBSUoLIyEjBaYlIRtwCEKCwsBDx8fEw\nGo0oLCwUHYduMpvNBrfb/b0ZALPZjNraWgBATU0NHA4H3/yJSBiuAPiYqqoICQmB1WoFAJjNZly9\nepXbABpSV1eHkSNHem96DAsLw5UrV7B27Vq89tprUFUVLpcLf/zjH7F48WLBaYlIVlwBEIBDgNqW\nkpKC5ORktLW1oaWlBcOHD8exY8ewevVqfP3117jzzjsRFhaGv/71r6KjEpHEWAB8jA8DkoNer4fH\n40F0dDTmzZuHU6dOISYmBo899hhefvllhISE4PTp06JjEpHEeApAED4MSLtsNhsAYPr06VAUBd3d\n3di2bRvee+89JCUlYezYsbDZbJgwYYLgpEQkM84A+BhnALSvrq4O6enp3lUAt9uNw4cPY9asWXC7\n3VAUBQ6HA4cOHcIdd9whOi4RSYpbAAJwBkDbUlJSkJiYiMuXL8Nut+OZZ57B0aNHERQUhPj4eMTF\nxQEA7r33XrS2tgpOS0Sy4haAj/n5+WHBggUwm80AgEWLFvHTv8YMdRXwc889h++++877PeHh4Vi2\nbBliYmIEJiUimXELwMcGtgDKy8sBANnZ2dwC0JgbHQN84okn8O6776K7uxsulwtfffUVxo4dKzgt\nEcmKWwACGI1G+Pn5wc/Pj1sAGpSSkoKkpKTvHQOcOXMmWltb4XA4MHXqVDzwwAOioxKRxFgAfIzH\nAOVgMBi+dwxwxowZ0Omu/cgtWbIE3377reCURCQzzgAIwmOA2mWz2fDtt98iKSkJiqLA7XZj9+7d\neP311/HWW2/BarVixIgRyMrKEh2ViCTGAuBjqqpi9erV1x0D/PnPf85VAA1paWmBTqdDamoq+vv7\n0dnZiYCAAOzZswdOpxMGgwHd3d04efKk6KhEJDFuAQhgNBqhqipUVeUMgAYNHAM8evQoqqqq8Ktf\n/QqnTp3CwYMH8fTTT8Pf3x+7d+/mCQAiEoqnAARYtmwZioqKAFw7Brh9+3bBiehmstlsCA0NhdFo\nvG4LoKurCw899BD6+/uRnZ2N/fv3Y9iwYaLjEpGkWAB8TFVVREREoLKyEgBgsVjQ0dHBLQANqaur\nw+jRo2E2m+F0OtHd3Y2dO3finnvuAQC4XC74+/sjOTkZZWVlgtMSkay4BUB0kw0cAzx8+DACAgIQ\nEhKCU6dOISwsDKmpqfB4PEhLS0NPT4/oqEQkMRYAHxs4BmixWGA2m3kMUIMGbgLMycnBpUuX0NDQ\nAIvFgoaGBmzcuBH5+fmYMWMG6uvrRUclIonxFIAgA1fFkva0tLTAZDKhvb0dqqqiv78fAQEB6O/v\nx5133gmXy4Vjx46hv79fdFQikhhXAHxs4Bjg2bNnUV1djdWrV0NVVdGx6CZKSUlBdnY2VqxYgalT\npyIgIACnTp2CTqfDI488Arfbjfvvv190TCKSHAuAADwGqG1//vOfYTQaUV5ejq+//ho2mw0WiwUB\nAQHYsmULAgMDUVxcLDomEUmOWwA+NvA0wOzsbAB8GqAWHT58GEVFRVAUxbvV88EHH+Dzzz/HxIkT\n4XQ64Xa7ERsbKzoqEUmMxwB9bOBpgINvAuTTALVl7969KC4uRkNDAyoqKtDe3g63242pU6di9OjR\nOH78OGpqarxPCSQiEoErAALo9fohvyZtOHHiBIqLi6HX671H/ZYuXYrPP/8cx48fh8FggF6vh91u\nF5yUiGTGGQABFEWBxWKBxWLhA4E0aPLkyVi4cCHi4uK8Kzs7duyAyWTCnDlzkJeXB5fLBbPZLDgp\nEcmMKwACKIqCEydOALj2ZkHacuLECfzpT3+C0+n0HvVbtmwZAGD37t3e71u4cKGQfEREAFcAfG5g\nCDA/Px/5+flYsGAB9/81ZvLkyYiNjUVCQgIMhmsde/v27d7VnoFHQRcUFIiMSUSSYwHwMVVVUVRU\nBKvVCqvViqKiIt4DoDHvv/8+Ghoa0NjY6F0BWLp0KWJjY3HLLbfgzTffhE6nQ19fH9rb2wWnJSJZ\nsQAIwCFAOej1ephMJgDwFoGEhAQUFBR4jwdGRkaKjEhEEuMMgAButxtZWVner0lbBpb6o6KivJ/w\nHQ4Henp6UFlZCYfDAbfbDUVR0NraipiYGJFxiUhSXAEQQK/Xez8BcgVAezweD/z9/WEymaDTXfsR\nCwwMxF133YWRI0ciMjISOp0OqampfPMnImFYAATo7++HTqeDTqfjA2E0aNSoUQgPDwcA7xAgAKxd\nuxaPPvooRo4ciWHDhiEnJ0dURCIi3gToa93d3YiKikJ5eTkAIDs7G21tbQgNDRWcjP4VNm3ahPXr\n16O5uRlTpkxBaWkp0tLSkJ6ejjfffJMrAEQkDFcAfMzPzw86nQ65ubnIzc2FTqfjMUANGzduHCZM\nmADg2mOCY2NjYTQaceHCBTz//PNiwxGR1LgC4GOqqiIsLMy799/f34+uri6WACIi8imeAvAxVVXh\ndrtx5swZANe2AFRVZQEgIiKfYgHwMVVVoSgK8vLyAFw7MsaLgIiIyNdYAATweDyorKwEAD4QhoiI\nhOAQoI+FhoZ6z4YDgE6n4wkAIiLyOa4A+Jifnx+2bNkCi8UCANiyZQv3/4mIyOd4CkCQgX1/vvkT\nEZEILABEREQS4gwAERGRhFgAiIiIJMQCQEREJCEWACIiIgmxABAREUmIBYCIiEhCLABEREQSYgEg\nIiKSEAsAERGRhFgAiIiIJMQCQEREJCEWACIiIgmxABAREUmIBYCIiEhCLABEREQSYgEgIiKSEAsA\nERGRhFgAiIiIJMQCQEREJCEWACIiIgmxABAREUmIBYCIiEhCLABEREQSYgEgIiKSEAsAERGRhFgA\niIiIJMQCQEREJCEWACIiIgmxABAREUmIBYCIiEhCLABEREQSYgEgIiKSEAsAERGRhFgAiIiIJMQC\nQEREJCEWACIiIgmxABAREUmIBYCIiEhCLABEREQSYgEgIiKSEAsAERGRhFgAiIiIJMQCQEREJCEW\nACIiIgmxABAREUmIBYCIiEhCLABEREQSYgEgIiKSEAsAERGRhFgAiIiIJMQCQEREJCEWACIiIgmx\nABAREUmIBYCIiEhCLABEREQSYgEgIiKSEAsAERGRhFgAiIiIJPT/ACKXdg97tHnLAAAAAElFTkSu\nQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d74114910>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 69)]\nrepeated subsession counters (counter, multiplicty)-- [(31, 2), (54, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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mfv362NvbEx0dra3PCXG/kpOTadGiBevWrSM0NBS4e/1vSef/btiwgZiYGHQ6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\nrWzYsIHbbrsNgLfeeguv18vQoUNpamoiODjYipI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"text/plain": "<matplotlib.figure.Figure at 0x7f7d7437cd10>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 22)]\nrepeated subsession counters (counter, multiplicty)-- [(12, 2), (13, 3), (14, 3), (15, 3), (16, 3), (17, 3), (18, 3), (19, 2), (20, 2), (21, 2), (22, 2), (23, 2), (24, 2), (25, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d543b7b90>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 13)]\nrepeated subsession counters (counter, multiplicty)-- [(11, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d5407be90>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 15)]\nrepeated subsession counters (counter, multiplicty)-- [(22, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d346bddd0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 5), (1, 56)]\nrepeated subsession counters (counter, multiplicty)-- [(1, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d540a0450>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 3), (1, 75)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d346a6350>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 4)]\nrepeated subsession counters (counter, multiplicty)-- [(5, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d143fb0d0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 6)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d54316f90>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 105)]\n"}, {"output_type": "display_data", "data": {"image/png": 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dupSxY8eaWZKIiKDwtazmvlLw4MGDzJ07l9tvvx2AiRMnNhroIiLS8rTtbGEt\n3XBls9l+7COKiMgFUPha2IU0XL355pt07NiRhx9+mIceeij0eWpqKs8++yy9evWiT58+FBYW0rFj\nR1PqEBGRcNp2tqj6hquioiIAMjIymDBhQtjWc05ODllZWWRlZYU+Ky8vJyYmhiFDhoT9bE1NDcXF\nxRw6dIjCwkKGDBnCgAEDzCtIRERCtPK1sKY0XOXk5ISuFHz11Vd56qmnqK2tpaCggOzsbCZPngzU\nhfKwYcPIzMxk3LhxZGZmsmXLFtNqERGRk7Tytagf23B13XXX8cwzz9C3b18AevfuTX5+Pjt37qSk\npIS+ffuyf/9+rX5FRFqBVr4W9mMark6Xm5tLWloa/fv3Z+rUqQwePBiHw9FMTyoiIudD4WthF9pw\n1aNHDz7++GO++eab0Oe1tbUcOnQIv9/Prl27+Oqrr+jataspdYiISDiFr0Vd6IQrt9vN22+/TWJi\nYtjnr732Gj6fj8LCQp566imOHj1KbGxsi9chIiINKXwt7EIaroqKihg2bBgej4d7772XUaNGAXVn\nyO+//z7du3dnzpw5dOjQAZfLZVotIiJykq4UtLAfc6Xg6Q1XAOPGjWP16tVUVVXx3HPPMXHixBav\nQUREGtLK18Kas+Fq0aJFVFdXc+jQIYqLi5kzZw7FxcXN9KQiInI+FL4W1pwNVwsXLmTz5s3079+f\nESNGsHPnTpYtW2ZKHSIiEk7ha1HN3XB14403MmzYML788ktefvlloqKiGDlyZIvXISIiDSl8Lawp\nDVe5ubmhKwVPb7i67777Qg1XkyZNwuv14na7uf7667nmmmvo2bOnabWIiMhJariysOZuuKrXpUsX\nVq5cSWZmZos9u4iInJlWvhZ2roar8znzBSgsLMTtdrN//37Gjh3bYBtbRETMofC1sHM1XJ3Pma/P\n52P8+PH06dOHmTNnsm7duka3skVEpOUpfC2qKQ1XQ4cOJSEhIeyzjIyMRsdGrl69GrfbzUcffcTY\nsWNJSkrCbtdfv4hIa9C/vhZ2roar7OxsbrnllrAJV8uXL6dTp04NJlzt3r2bo0ePUllZSVZWFk8/\n/bRpdYiISDhdKWhRF3qlIMCYMWMaNFzV1tby7bff8t133xETE8OwYcPo168f//Iv/2JqXSIiopWv\n5QWDQc7UkH4+DVdOp5OSkhKGDRvG4MGD8fv9bN26tcWfX0REGlL4WlT9me9XX33Fzp07f/SQjWuv\nvZbIyEg859KeAAAOCUlEQVQ2btzIF198gcvlokePHi1eh4iINKTwtbDmvNXI5XKRkpLCgAEDuPLK\nK+nXr1/oeyIiYi6d+VpUc5/5Avzwww+kpKSQkJDADTfcYFotIiISThOuLM7j8ZCQkNDouW9ubi4r\nV66ksrKSmpoaoO7Md+bMmXz99dcsWrSI22+/HQCv18uJEyeoqKigW7duREdHU1JSQnx8vKn1iIiI\ntp0trTmHbERFRZGUlMQDDzzA6NGjadu2bVijloiImEfbzhZV33BVVFQE1A3PmDBhQtjW89ChQ9mw\nYUPY78vIyGj0zysrK2PdunWkp6djGAZlZWWkp6e3XAEiInJGWvlaWFOGbGRlZYXdanTqkI1TbzVa\ntWoV48ePZ9WqVbz55ptkZWU1WB2LiIg5tPK1qPqGq549e1JbW8tLL73UoOEqJiYGv99PVFQU+/bt\nA+rOfF0uF/v37+f5558Pnfm+9957pKamYrfb8fv9eL1e02sSEZE6WvlanM1mw2azNfq98znz3bNn\nD8FgkPLycmpqanjjjTeYO3duiz23iIicmbqdLcowDOLj48POfCsqKhqsfjds2MDw4cND3c71kpKS\nmDt3bmjle6r777+fRYsWUVZW1nIFiIjIGWnla2HNebECwKZNm+jRowcvvfQSo0ePNqUGERFpSOFr\nUacO2cjIyODZZ59t9Mw3EAjgdDrZt28fubm51NbW4nK5CAQCvPDCC7z//vsA5OfnM3nyZBwOB926\ndeO9997j+PHjrVGaiMhFT9vOFne2IRvr16/H4/GQlZUV2nYuKirCbrdz9dVXh207b9u2jXbt2tGu\nXTt27NhBv379+Nvf/ka/fv1MrUdERLTytbRzDdkYOnQoCQkJYZ9lZGTQtWvXBj+bmJhISkoKAG3a\ntKG2tpbOnTu3yHOLiMjZaeVrUU1puMrOzmbNmjWUlpaSlpbGo48+SnJyMvfccw8HDx4kMTGRq666\nivfff5/XX3+dJ598ksjISCoqKnC5XLpSUESklWjla2Hnarg6nzPf66+/nksuuYRdu3ZRWlrKsmXL\nTKtDRETCKXwt6tQhG926dWu04ep83vONjo7m3nvvJS4ujhEjRnDZZZe1eA0iItI4TbiyuLMN2Tif\n2c5er5eZM2fyq1/9qtHmLRERMY/OfC2quc98H3/8cWbPnk3btm2pqqoiLS2N/Pz8UBOWiIiYR9vO\nFtacZ74PP/wwM2bMoKamBp/Px5NPPqngFRFpJQpfi2ruM9+vv/6aJUuW8MQTTzB69GgmT55MIBBo\n8TpERKQhha/FnevMt6nv+a5YsYLs7GwcDgcul4suXbqwadOmFnlmERE5O535WlRTznzT09NDtxXV\nn/lGRkZy1113YRgGkZGRXHPNNXz44YdMnjyZV199FZ/PRzAYJCoqiieffJJ77723tUoUEbloqdvZ\nws515rtw4cLQeMn6+3ynT5/OY489xvvvv4/b7SY2NhaAHTt2MHDgQD7++GNKS0vp3r07HTt2NKcQ\nEREJo21ni2rKmW9j284rV65kwoQJANxwww0sX74cqHvVKDU1FYBLLrmEYDCIx+MxoRIRETmdwtfi\nznbm25gjR46EQjY5OZkjR44AMHLkSFatWkV1dTWffPIJ5eXlDYJbRETMoTNfi7rQM98pU6YQCATw\ner1ERkYSCASora3lxIkTdO7cme+//x6Ajh076l5fEZFWopWvhTXlzPedd94Je883OjqaadOmEQgE\neOihh3C5XAC8+eabDB8+nEAgwIkTJygtLSUuLs60WkRE5CSFr0U5nU6ysrLIyMggIyODrKysJp35\n2my2RsdHJicn4/F48Pv9vPvuu9jtdvr27duiNYiISOO07WxRhmGQnJzM5s2bAejfvz/Hjh0767bz\nY489xn333Re6NjA+Ph6fz8fx48d57rnnePDBB0ODNWw2G9u2baNXr16tUp+IyMVMK1+LczqdDVa8\n9U7fds7JycHr9fKb3/wGr9fL9OnTMQwDgJSUFMaMGYPP52PdunVEREQQHx9vZikiIvIPCl+Lqn/V\nyO12k5GR0eRXjYLBIKNGjQLqOpzrNzY2btzIzTffjMPh4IMPPiAzMzO0qhYREXNpyIbFBYPBM14B\nmJuby/Lly/F6vWGf33LLLQ3OfjMyMnjrrbd46aWX2LJlCzabjcsvv7xFn11ERBqnla9FGYbBlClT\n+Oqrr9i5cydTpkwJbSHX27t3byicO3XqxIIFCxr8OfXvCN95552sW7eO/fv3Y7fbefjhh+nTp48p\ntYiISDiFr4VFRkZiGEZoTvPpfvazn4WGcNSf+QaDwdBVgSkpKaEGq6VLl1JRURFq5Dp06BB2u/76\nRURag/71taj6V4169+5N7969G33VKCcnhzlz5oR9FggESEpKYteuXSQkJITCt7i4mPj4eDweD23b\ntuWyyy4zrRYREQmn8LUowzBYvHgxRUVFFBUVsXjx4gbbzvPmzWPatGlh287BYJCKigq6du1KdXV1\n6NzX5/NRWVmJ2+1m06ZNvP3226xdu7Y1ShMRuegpfC3sXNvOMTExBAKBsG1nOHnOe2rDVfv27QGo\nqqoiLi6OQCDA1q1bTahCREROp/C1qKZuOz/zzDNhn9lsNuLi4ti1axdt2rQJBfG///u/k5mZSdeu\nXbnuuuvYsmWLtp5FRFqJJlxZVH1j1Pbt2wFwu92NTrgqLi4GCE24mjRpEjExMVRXV4f+6/V62bRp\nEzfccAPHjh0D6sJ9/fr1GjEpItIKtPK1uPpt58YsXLiQP//5z2HbzoFAgCFDhuD1ehk0aFCo4crl\ncpGeno7H4+HXv/411dXV/OxnPzOzFBER+QcN2bCoU7edAcaOHdtg23nChAmh2c6dOnXisccew+/3\n8+mnnxIVFUWbNm3w+/1A3Rb11q1badu2LT6fj+joaNq2bWt6XSIiopWvZRmGwbJly0LdzsuWLWuw\nAl64cCF/+tOfGjRcTZkyBa/Xy+TJk8P+vKSkJPx+Pz6fj5qaGubNm2dqTSIiUkfha3Fn23ZesGAB\n999/f1hXs81mY9GiRXTt2pW//OUvoYarDz/8kMzMTJxOJz179gTqmrBERMR82na2qKZsO3/wwQdU\nVlYChLadg8Eghw8fxu/343A4QsEcHR1Nt27d2LFjBzt37iQyMpK9e/eGpmGJiIh5tPK1qKYM2Viy\nZAmzZ88GYN++fXg8HgDsdjtPP/00gwYNAmDQoEH06NGDvLw8Jk6cSFRUFH6/XxcriIi0Eq18Lczh\ncDT663rz5s3jgw8+AKBdu3bU1NQAdcM1pk2bRo8ePQAoLy+nvLycyspKZs+eTWJiIhkZGRQVFTFw\n4EATKhERkVNp5WthNpsNt9uN2+0Ond2eKi8vj/nz52Oz2Zg+fXroasG4uDhcLhfx8fEAeL1e7HZ7\n6Aak22+/nT59+nDgwAFT6xERkTpa+VqYzWZj48aNAAwePLjB97Ozs8nPzycYDPLf//3fVFdXA1BZ\nWUl1dXVofGTnzp3ZuHFj6Px38eLFBAIBfv7zn5tUiYiInEorX4uqb7gaMGAAAwYMaHS8ZF5eHsuX\nL8fpdDJ58mRsNhsOh4Pk5GQ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"text/plain": "<matplotlib.figure.Figure at 0x7f7d742019d0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 15)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d340c2b10>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 3), (1, 38)]\nrepeated subsession counters (counter, multiplicty)-- [(1, 2), (6, 2), (7, 2), (8, 2), (9, 2), (10, 2), (11, 2), (12, 2), (13, 2), (14, 2), (15, 2), (16, 2), (17, 2), (18, 2), (19, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7cd4274b50>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 23)]\nrepeated subsession counters (counter, multiplicty)-- [(2, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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VK1YAAH755Re4urrC398fUqkU0dHRePvtt8UNSeWuRo0a2L9/Pz744ANYWVlh2LBhxfar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\nQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d54590d10>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 2)]\nrepeated subsession counters (counter, multiplicty)-- [(1, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d743a7650>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 21)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d741c6050>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 47)]\n"}, {"output_type": "display_data", "data": {"image/png": 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q6urqJEm1tbXq27ev4Z8BALAvtqkNWLNmjfLz84Nv4Jo/f36Hv8ett96q+Ph4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\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d54752890>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 12)]\nrepeated subsession counters (counter, multiplicty)-- [(15, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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NsLAwhIeHw8TERHQcIiojCwsLDBkyBL169UKLFi0QFxcnOhKVApedjURQUBC2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\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7d7420a1d0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 2), (1, 40)]\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7d547d3fd0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "node degrees (degree, number of nodes with degree)-- [(0, 1), (1, 26)]\nrepeated subsession counters (counter, multiplicty)-- [(28, 2)]\n"}, {"output_type": "display_data", "data": {"image/png": 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