notebook_examples/pypgx-graph_analytics-machine_learning.ipynb

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    "@notebook{pypgx-graph_analytics-machine_learning.ipynb,\n",
    "    title: Graph Analytics and Graph Machine Learning with PyPGX,\n",
    "    summary: Use Oracle's Graph Analytics libraries to demonstrate graph algorithms, graph machine learning models, and use the property graph query language (PGQL),\n",
    "    developed_on: pypgx2310_p38_cpu_v1,\n",
    "    keywords: pypgx, graph analytics, pgx\n",
    "    license: Universal Permissive License v 1.0\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "944b51eb",
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   "source": [
    "Oracle Data Science service sample notebook.\n",
    "\n",
    "Copyright (c) 2022, 2023 Oracle, Inc. All rights reserved. Licensed under the [Universal Permissive License v 1.0](https://oss.oracle.com/licenses/upl).\n",
    "\n",
    "---\n",
    "\n",
    "# <font color=\"red\">Graph Analytics and Graph Machine Learning with PyPGX</font>\n",
    "<p style=\"margin-left:10%; margin-right:10%;\">by the <font color=\"teal\">Oracle Cloud Infrastructure Data Science Service.</font></p>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21cb5cfa",
   "metadata": {},
   "source": [
    "# Overview:\n",
    "\n",
    "Graph analysis lets you reveal latent information that is not directly apparent from fields in your data, but is encoded as direct and indirect relationships between elements of your data. This connectivity-related information is not obvious to the naked eye, but can have tremendous value after it is uncovered. Parallel Graph AnalytiX (PGX) is a toolkit for graph analysis and supports both efficient graph algorithms and fast SQL-like graph pattern matching queries.\n",
    "\n",
    "The tools included as part of the PGX distribution include the PGX Algorithms. PGX is a domain-specific language for writing graph analysis algorithms in a simple and readable form, which the PyPGX runtime can transparently optimize and run. This notebook explores the built-in algorithms provided by PyPGX.\n",
    "\n",
    "PyPGX provides a machine learning library that supports the following (graph-empowered) machine learning algorithms:\n",
    "* [DeepWalk](https://docs.oracle.com/cd/F41824_01/latest/prog-guides/mllib/deepwalk.html) (Vertex embeddings)\n",
    "* [SupervisedGraphWise](https://docs.oracle.com/cd/F41824_01/latest/prog-guides/mllib/supervisedgraphwise.html) (Vertex embeddings and classification)\n",
    "* [UnsupervisedGraphWise](https://docs.oracle.com/cd/F41824_01/latest/prog-guides/mllib/unsupervisedgraphwise.html) (Vertex embeddings)\n",
    "* [Pg2vec](https://docs.oracle.com/cd/F41824_01/latest/prog-guides/mllib/pg2vec.html) (Graph embeddings)\n",
    "\n",
    "[Property Graph Query Language (PGQL)](https://pgql-lang.org/spec/latest/) is an SQL-based query language for the property graph data model that allows you to specify high-level graph patterns that are matched against vertices and edges in a graph. PGQL has support for grouping (GROUP BY), aggregation (`MIN`, `MAX`, `AVG`, `SUM`), sorting (`ORDER BY`), and many other familiar SQL constructs. Furthermore, PGQL has powerful regular expression constructs for graph reachability (transitive closure), shortest path finding, and cheapest path finding.\n",
    "\n",
    "In this notebook, you use the scikit-learn diabetes dataset to build:\n",
    "1. a graph to demonstrate several graph algorthms,\n",
    "2. graph machine learning models, and\n",
    "3. a graph to demonstrate PGQL.\n",
    "\n",
    "---\n",
    "    \n",
    "## Contents:\n",
    "\n",
    "* <a href=\"#intro\"><b>Introduction</b></a>\n",
    "    * <a href=\"#intro_session_analyst\">Session and Analyst Objects</a>\n",
    "    * <a href=\"#intro_dataset\">Dataset</a>\n",
    "* <a href=\"#create_graph\"><b>Create a Graph</b></a>\n",
    "    * <a href=\"#create_graph_nodes\">Create Nodes</a>\n",
    "    * <a href=\"#create_graph_edge\">Create Edges</a>\n",
    "    * <a href=\"#create_graph_save\">Save Graph Data</a>\n",
    "    * <a href=\"#create_graph_load\">Load Graph Data</a>\n",
    "* <a href='#built-in'><b>PyPGX's Built-in Algorithms</b></a>\n",
    "    * <a href='#built-in_pagerank'>Personnalized PageRank</a>\n",
    "    * <a href='#built-in_eigenvector'>Eigenvector Centrality</a>\n",
    "    * <a href='#built-in_diameter'>Diameter / Radius</a>\n",
    "    * <a href='#built-in_lcc'>Local Clustering Coefficient</a>\n",
    "    * <a href='#built-in_label'>Communities Label Propagation</a>\n",
    "* <a href=\"#example\"><b>Graph Machine Learning Examples</b></a>\n",
    "    * <a href='#example_pagerank'>Example: Personalize PageRank</a>\n",
    "    * <a href='#example_deepwalk'>Example: DeepWalk</a>\n",
    "* <a href='#pgql'><b>PGQL Queries</b></a>\n",
    "    * <a href='#pgql_patients'>Example: Filter Patients Based On Age</a>\n",
    "    * <a href='#pgql_bmi'>Example: Patients with a High BMI</a>\n",
    "* <a href=\"#ref\"><b>References</b></a>\n",
    "\n",
    "---\n",
    "\n",
    "**Important:**\n",
    "\n",
    "Placeholder text for required values are surrounded by angle brackets that must be removed when adding the indicated content. For example, when adding a database name to `database_name = \"<database_name>\"` would become `database_name = \"production\"`.\n",
    "\n",
    "---\n",
    "\n",
    "Datasets are provided as a convenience.  Datasets are considered third-party content and are not considered materials \n",
    "under your agreement with Oracle.\n",
    "     \n",
    "You can access the `diabetes` dataset license [here](https://opensource.org/licenses/BSD-3-Clause).\n",
    "\n",
    "---\n",
    "The notebook is compatible with the following [Data Science conda environment](https://docs.oracle.com/en-us/iaas/data-science/using/conda_environ_list.htm):\n",
    "\n",
    "* [Parallel Graph AnalytiX 23.1 and Oracle Property Graph 23.1 for CPU on Python 3.8 (version 1.0)](oci://service-conda-packs@id19sfcrra6z/service_pack/cpu/Parallel_Graph_AnalytiX_23.1_and_Oracle_Property_Graph_23.1_for_CPU_on_Python_3.8/1.0/pypgx2310_p38_cpu_v1)\n"
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      "SLF4J: Class path contains multiple SLF4J bindings.\n",
      "SLF4J: Found binding in [jar:file:/home/datascience/conda/pypgx2310_p38_cpu_v1/lib/python3.8/site-packages/pypgx/jars/log4j-slf4j-impl-2.17.1.jar!/org/slf4j/impl/StaticLoggerBinder.class]\n",
      "SLF4J: Found binding in [jar:file:/home/datascience/conda/pypgx2310_p38_cpu_v1/lib/python3.8/site-packages/opg4py/jars/logback-classic-1.2.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]\n",
      "SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.\n",
      "SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]\n"
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   ],
   "source": [
    "import logging\n",
    "logging.basicConfig(level=logging.ERROR)\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import json\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import pypgx\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "import shutil\n",
    "import tempfile\n",
    "\n",
    "from ads.common.model import ADSModel\n",
    "from ads.dataset.dataset_browser import DatasetBrowser\n",
    "from ads.dataset.factory import DatasetFactory\n",
    "from ads.evaluations.evaluator import ADSEvaluator\n",
    "from sklearn import datasets\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e710c96",
   "metadata": {},
   "source": [
    "<a id=\"intro\"></a>\n",
    "# Introduction\n",
    "\n",
    "Graphs are a powerful abstraction that enable knowledge discovery by revealing the relationships between entities. Graphs consist of a set of entities, which are represented as vertices (also called nodes). The relationships between vertices are connected by a collection of edges (also called connectors). Graph analytics strive to reveal the latent or hidden information within these relationships. The information in a graph encodes not just the direct relationships, but the indirect relationships between entities as well. While humans are good at determining connections between entities, it's not practical to do this at the scale and level of detail that is common in modern graphs because the datasets tend to be large and heterogeneous.\n",
    "\n",
    "<!-- Graph analytic algorithms have been developed to merge the properties of graphs with deep neural networks. In this approach, the graph is a representation of learning that employs neural embeddings using vertices and edges as learning units. This representation enables graphs to learn low-dimensional vector representations in an unsupervised and task agnostic manner. This approach has been used in applications such a link prediction and friendship recommendation engines in social networks.\n",
    "\n",
    "The [Parallel Graph AnalytiX (PGX)](https://www.oracle.com/middleware/technologies/parallel-graph-analytix.html) library provides access to powerful graph analytics functionality. It supports a broad spectrum of analytics capabilities such as Graph Machine Learning (for example, DeepWalk, [GraphSAGE](http://snap.stanford.edu/graphsage/), Deep Graph Infomax, and many more). PGX has more than 60 built-in graph algorithms such as [PageRank](https://en.wikipedia.org/wiki/PageRank), InfoMap, Centrality, and others.\n",
    "\n",
    "Computational performance is baked into PGX as its algorithms are optimized and parallelized. It is written in Java and has Python bindings in the PyPGX product. -->"
   ]
  },
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   "cell_type": "markdown",
   "id": "51bb2ea0",
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   "source": [
    "<a id=\"intro_session_analyst\"></a>\n",
    "## Session and Analyst Objects\n",
    "\n",
    "To access the graph analytic functionalities, you need an [analyst](https://docs.oracle.com/cd/F41824_01/latest/api/api.html#pypgx.api.Analyst) object as the entry point to all the graph analytics capabilities. PyPGX is [session](https://docs.oracle.com/cd/F41824_01/latest/api/api.html#pypgx.api.ServerInstance.get_session)-based and the method `pypgx.get_session()` returns a session object. An analyst object is obtained by calling `.analyst` on the session object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "# pypgx variables\n",
    "session = pypgx.get_session()\n",
    "analyst = session.analyst"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f9f4ef4",
   "metadata": {},
   "source": [
    "<a id=\"intro_dataset\"></a>\n",
    "## Dataset\n",
    "\n",
    "The SciKit-learn [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) has 442 observations and eleven metrics. This notebook uses the age and BMI metrics. The data has been preprocessed so that it is centered and scaled. \n",
    "\n",
    "The next cell reverts this preprocessing so that the original raw data is recovered. However, an additional transformation is made so that the values are converted to natural numbers. This conversion is explained in more detail when the [bipartite graph](https://en.wikipedia.org/wiki/Bipartite_graph) is constructed."
   ]
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   "source": [
    "df = DatasetBrowser.sklearn().open(\"diabetes\").to_pandas_dataframe()\n",
    "\n",
    "# reverse transforming the variables for use in the bipartite graph\n",
    "df['age'] = df['age'].apply(lambda x: int(275.29 * x + 48.52))\n",
    "df['bmi'] = df['bmi'].apply(lambda x: int(92.78 * x + 26.375))\n",
    "df['sex'] = df['sex'].apply(lambda x: int(10.49 * x + 1.468))"
   ]
  },
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   "id": "7eae22d4",
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   "source": [
    "<a id=\"create_graph\"></a>\n",
    "# Create a Graph\n",
    "\n",
    "With this data, there's no obvious graph structure to it. However, you could construct a bipartite graph to impose a graph structure onto the data. A [bipartite graph](https://en.wikipedia.org/wiki/Bipartite_graph) is a graph where the vertices are split into two separate sets. Each edge in the graph goes from one set to the other set so there aren't edges that connect vertices in the same set. In this model, the patient information is placed in the left set of vertices. The right set of vertices contain all of the possible values for each feature.\n",
    "\n",
    "The following figure presents a representation of this graph showing the relationship between the patient, age, and BMI. Notice that on the left each vertex represents a patient and all data associated with that patient. The vertices on the right contain one feature and a possible value. All possible features and values are represented by a vertex.\n",
    "\n",
    "Features such as age and BMI are continuous, and it isn't possible to make a vertex for all possible values. Therefore, the vertices in the right set are treated as discrete values, but the edges between the two sets connect to adjacent values. You can see this in the graph where the data for Patient i has connections to BMI=16, BMI=17, and BMI=18 because the recorded BMI is 17 (BMI=16 and BMI=18 are the adjacent values of BMI=17). This procedure of creating edges is repeated for each feature. Notice that the edges for Patient i connects to Age=20, Age=21, and Age=22 because the stated age is 21 and 20 and 22 are adjacent values. This captures the continuous nature of BMI, Age, and the other features."
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boNj87K5XwZVi/GS/Ah6QlcdkNhkI0NlzxoVK9klv3F/M8RtxCCFTHgsl3IhELaQRuJYj\nCYlSCsAGxgE2RvdNdRIwdp4oA6DgJqkNZ2cZ+eGElaSotssAoNCBPcnOhTlQks5TNgbwtRRGQMqh\nrD1Y3WGJDBayUqmuFS+8xhLA66Rnwxh46azVpB+39GMknPhlOccm9+gvmgm2Wo3L48yx0Ec4UJ0i\nOqgMPBaoCSww5U288aOBUfcOakINgBt3ptblUTuSea8j6MEITVwHeXTRpagyr7Y70nYOcgLeEx3U\nMSAItCeYExrGFU3kEVa3UNeSbljWAgrnTdiBkZ0Xd7ElksTUYdHBPclbEdhTiPu5lXLmtykRRAUH\n0LxMQ8VFUTip74fiUQRW1NN4DSIj7yvadkpPvzqQ6AXe6w4oQUR8lzLrWPwlberuywpA8Ct4wcSI\nGtkMIGMiO2iDksZtGtByV1EtvsRlYdJR16/rM9Xlpq3Se+AFYpQtVSBkdtUMCYhzOUh4WAQFr6L3\nBbCDH9HBwIBVzL/cZh4kpCFnCnlOJr4296AOoVDt2TL/JH5Xb3J0LdXB7SNQGqVgm6TeehHqak7G\nntc1ynz0sXjf5ab4WwYR66iE3BPyJaUZ/QRGEf8zYFiXx3CzVNGB1j2gsAyM8EJfdc4ay+qFZWdV\nvCf1PF8Ha5Wf/fhet/rxF0BVP54RqY2NwPl33sw4Xwv224/62Y3SW119Mfjt3YLjgrxIfgPUpU4t\nmmkbA1F6PBJiibKxN8xudwh1L4jNHSAmkRvqW7UGlYSMTARwsCPkkYh3IYomOpvqzYPkQs7iGjey\nAHoC3DC+ZHZUtb6W5VHWDgi+47DISe3T4DtWrAAF1yUDiTT4ryLBjvOGVi8qiirP3UVxFDpoqS3G\n8nJhDyocSCoDD6YWphzkBBhwgIiGZiTOrvjdXNDIMfL+dgi/pAb6rbYpKcTzoaMhJpGc4CCZijoH\nPqCwIUtLwVQUuhKTrAScxuvEO2AjhDbUBJvLA/A9hur5xTqFhUBhXUUYOM+1ClwMAIJTmKKc/QsU\nTAUAO0AkAGGxouRw09RuxOQi58xQGxTZBsmxLqJeFCXyTLp2IeFIwG0Thft0qQM4tGOhTmdJDcPR\nETK7o8Vek5xXgbydhRBVGbY9UxEqAG0LZQM5UXP3OGzJUhdabTodpegQBXwas9Q0CkWtRZYJcsVS\nTqE9d7xZlMmiQ3kV1MDeONzua/FA5totQ/YBETEmAIVPNZwkZYqUQHIv+7DqMGghVhZEYFSp9jGm\n29oVFw5FnBTvl+1D7aL4gVLF4GgjY/rX88U7qG2t3jS3Qkon9BhUjDhwUpBgGYuK1E7SREfuKWrr\ngayHftDszD0COo8YX7AL1FIT1ZkICDn+vIgU3PgJHYE7cwOEo0aJ3pOiqXhGQJLkfx5WHQbFJuIF\nmjQQsta1R3KUgTAw85oojA9gxUkK6hdC0AYauC1XfrRfNLAUb3qZPIHeMOzV+TLlBq6cyEuvgRRE\n25PWQBiyDGol/aayZasa1XF7zfjXtdpyH1BJdb19JqVjEdT7lZoX4VNXoWv3sYDkYIOFZdo1qPK7\niFwcF6srKyTZO7P768XlfrlCxg8vr7F3KVuUj1BzSYOKluI2lgnukX5CCsYMHakj/YCu/DP+ybqj\n0aeqqGq7BVmQagLMq/QsXucSAKe+YSxQazyLrOtkK55yfxnxBzPy4SRCwpE3AQyuGaEKuGjLCmEx\ndUpDY1BR7fy6VESbCk+BmqVO8O6k3EhXbSdGTnVjkIGs2YWGQR1UFDlFW18HHi1NUlrU0Rc+Oneu\nlfGCeqowIrfk37IWrDxUAct10MKr6bxI2uluwZu9+JJlWX0EcBZLA7A1SXPU15WVCOfmE1A6fGxp\n1yngWqhnf41R23Xab4mzDGQf0jpQ3XcXj0S+Rc2Ko902MCouhKDwZiRJoIgjVZJJWRe4O103VwcH\nAdTGR5eMN0iJgBwyf6zulRdFpFPaYk+gaaMl82ginYMdQmu1awI2sm8ZH+j4HHwC/y/bqD8mrpYj\nyHP7TNgHYAEpzzS4QFAfUv1fRHLBB3RX6sa4JYxJRBxTz5amxJdpVV4SNZRNagmMzh4BThEi0cUH\nxoyQm4QbZewujhvnYFOylfyHIqmSYEMbeFm7Pwt780435fDRpKgwhMU6RQtWwAa4duBp8dYgPJab\nt86SqV9sQCXpNiolAHCVrGaF1fgTHmpTa9YjxzHWp7d92n0JidKr2gpSXj8JQAIiztRDRd2GurRF\nc6BL0i2jfzrRADkwGayrzkWgI1yhjHLbrAxX8xVR+Pe7/fc3e92o4D6Wgnv/MBWIGYqWX9aefvQH\ncqYUifErp/BpR1VqZNezIB08ajourAnhADMkVPz0xrPODYnGgDjYa271n9H0qJ2Sfi/l3x9dgerD\nqYAAYqHgLBFvCpZ6MgQfJYnoeRvPAZRksbVBAhhzc6AYE8aCZWbgIKChVi0qzxsyYQnXCRtZNjti\nJKptmx5eqyMZ8GlT+/+sL2v7aDqJCeE1VstARaQwEemChs8OO8g0KnmHxlCLhSp/+7tZDRGEUr/1\nITOCDutwnDrOTIRkeh3It1eNZmPyElEZIMob5ZGIwZWHFrFP1Stk0CWRXqisOxafqrH62UAo8OKQ\n0I/MapxXVkckBISC550owIhZ2wAUu0cfcT21nLv70ScaiH+ijVY7tYDilhHs8ot7b3k4teoQJbD+\nXlzMdmnaFEF/FbCmDTdfAIsYBnGJ/FKDFBhlmEmyoSTEBNUE4XGlfbYaZZT8zM/Rk9XgICrIrYCh\n6/KyGGCuAtxhmqCP4duELiAEnSqAwDepBegenbqpG8GFJe1iikBadPex/1tHIPprKD70Bma0bJOS\nZm1Uy6kEAI+0sIaLRxyoaRjVUMCQoaXcc/pd3qEM0u571ONzIsKWaHigTN+pDdwhNRYZrlpVc+Ob\nFxclthClq9G074Y2J7/KgUUGJvOoA38zy05JSwIVGAaRmrQt5xv2kcwrpDRgzjhKju7bGSBFAzDz\njxvfolpUm3ZxyjtAQgberBM9RZZyhA0Uu6izBxnMYmV0Bcwx+ghABANxeBHLsDEA+aN+5KCDpDue\nZcjno/4Od6nB8Xm1xHGjwKw9KIad1cKgmLUxxaoOnKJ/KKLRRTIVTIK6EIEEB9zd6KMq8iZbl05n\naF8QPcb6+doBqnBMnSMvNySoJ8GhPRW+YHlicGUxQIF13V4KURGVfvr9BOSdbRHLZ50a4SID64q1\ngQyqel1R/QzAe6iHHuUucnKNV6aR5hQgihTGBWvh1mJb/TWhphe1Wm8yrax+0cT60jmOlyxN3EfU\nYHwL6grkCAh2HUDRwU44jruFuNVzBf0h3bdTk8SoITPsU65iiOA3iNkp11pIj6uDaEkIRhTlub5h\n3g2GRGsCSBRS6VVih2fv1vldakZbp05neFaWGSyImmhPaOkkBTqvCW9IFA0FDz61Ba1DLEKmhR5o\n1mVTWLxuTu0CePz1I9T/eGcsxngHfPa+VQe+ueWUVkRIHEas/jnJghXTmROUJzW53GvzxAUIW5U6\nH7lvtc+LdkHaxAfV3RNXSXp6v+3a9Bpouh23Le8g+3bvADWqrXVJAKQM8Z+nAg1Yan1wgFjbutwz\nGFuTzrFs7aQliLt8j+it9Y7oiXIpvXdET62G+47B2dkBTsUSZe36klg6lwZhva3hz9t1Him9aDp9\npv1x7e+li/bUdR7h6iDzxXJhDrYODQiXScX+49jdjpgSNKRqvH7O3FFXVQd4l/pekuHU52uXY6XO\nDZ6PbFvIptwbqYR3xDWmR38un6R26VrY/DzxzMSwa6PWQFJkmyQVkTrYPRHLlirogLtOa8uF8tMh\nvQBmqyXWj04ZHTUntI1FxLSpIQWHK/VVze4kAaZ0JaDP3TDV3XAAOhQTYFpgJAgeKxnKbEiVt0kO\niKnLFq9OVyLogCodmUTUQW45qmekoqIgSXhA3mnTNGk3OKrbdxC96lnPwRjAaMMJNarpaDvpNWO6\nsIoM1FYeldCbOs35iLIDhfXON4yMrbW6WAOrDcogZFA98IDbRgvgGWYRUFfTptBOU60QX3DoMbp3\n2kitjvUElH/nM3F9FZ2VuRrOKhq62XZK6gz+ueH269F9/0CpwJoZmwO6Mu8HzfWzIdIAAvIgIaMy\nOuC8HqwIv9Wu7ZisJpbrOoCAUUaDB50lAGdglaL9uavu2hXQabuf9VvaClzoLh3yCTr4C6Bmrza6\nTmmcHlAUKMHKrDO3j0nD0FYYdEcgPqdfb33GZpbeMEaIcnm8gsPqeSRgBCuFqN0Su4gPmcEwJgKd\n5Mw63E8Sv3YpkDnlQkmnXydOf57umsv9s/k0HcUDqJRkktbo0gMB1hrB9uvB0EbMBHNNuynIbdfK\n1zjqdHfPVT0si+ImLOfQNiA4j53wr+f4L0L2zh7/ixf0iIbuAcSuO7QSkSMQMtl5/WtOfUNq71gn\nU0ufU53hc2L0xxnQ846Ats8RUL7DjOGz+xRwbExAp7dkEPrrW+Jp32FSHGaVTn2Sxp5DWK8klraL\nrbE0VeeMtYOIfwOYJLK0bQ4XFwKNPtI6UEJDtngfdZo8tSrXElfQeQRQC6/M7Y1EeIdWlGR/O9Xp\n/sMTwpJIzwNAZ/xtULZWb70NHBtoawwgPkEHx+FbNRriO3kyzzsAZE3OhDUdMs3Kdg9E4O68e05Y\nNrySaUPblRhEMAl9PjeVDwCw6LAQafnnheznXXT02r1j2lNn7EGiAtfHKp0dmoSWbEM7LxfQo3+3\nRL89un/1wq/H4temGtRmPlM78sijofPk3ciVAP0wtOK6zIl1vLQOrB7qV10i+KBTyFH76GoXkvVy\npfAW1oW10RF0nJi20CEzAGy7xIxNMB3Wmy1q+E4dUNCu3xOlCJ/XbfLaZRw625GICPVKtNQwkBdF\n1nhV/HkbxmT7RSGej3laT0QwCUhjdsHoUpsVithP15OgtasDf58/cu28Rpb0+j+LY7Vr3hmKVAv+\nkBIRM5FF6go8eMY87+7+H3pgwjNFAltRAAABhGlDQ1BJQ0MgcHJvZmlsZQAAeJx9kT1Iw0AYht+m\nSkUqDhYUccjQOlkQFXHUKhShQqgVWnUwufQPmjQkKS6OgmvBwZ/FqoOLs64OroIg+APi5uak6CIl\nfpcUWsR4x3EP733vx917gNCoMM3qGgc03TbTyYSYza2KoVeEaA5CRExmljEnSSn4jq97BPh+F+e9\n/Ov+HH1q3mJAQCSeZYZpE28QT2/aBud94ggrySrxOfGYSRckfuS64vEb56LLAu8ZMTPpeeIIsVjs\nYKWDWcnUiKeIo6qmU38h67HKeYuzVqmx1j35C8N5fWWZ67RGkMQiliBRRgpqKKMCG3HadVIspOk8\n4eMfdv0SuRRylcHIsYAqNMiuH/wPfmdrFSYnvE7hBND94jgfMSC0CzTrjvN97DjNEyD4DFzpbX+1\nAcx8kl5va9EjoH8buLhua8oecLkDDD0Zsim7UpCWUCgA72f0TTlg4BboXfNya53j9AHIUFapG+Dg\nEBgtUu91n3f3dOb2b00rvx91ZnKoBDIciAAAAAZiS0dEAPwA+wD5CXfQqgAAAAlwSFlzAAALEwAA\nCxMBAJqcGAAAAAd0SU1FB+UHBxchFV2BgMUAACAASURBVHja7N13fM3n///xx8mUIVOE7IQEicau\nTatVtNTWqh2K1qjVj9qK1tZqzVhBFTGrRVulVZSiVkiQKYnsvZOzfn/0m/Nrmg7UyEle99vNHzLe\nOe/rvM95nut6X9frUmi1Wi1CCCGqlIKCAiwsLPDx8UFiQP9FRERgJM0ghBBV04ABA9i7d680RCXg\n4+ODgTSDEEJUTdIzr1wk0IUQQggJdCGEEEJIoAshhBBCAl0IIYQQEuhCCCGEBLoQQgghJNCFEEII\nIYEuhBBCCAl0IYQQQgJdCCGEEBLoQgghhJBAF0IIIYQEuhBCCCGBLoQQQggJdCGEEEJIoAshhBBC\nAl0IIYSQQBdCCCGEBLoQQgghJNCFEEIIIYEuhBBCSKALIYQQz5JWq0Wj0UhD/AsjaQIhhBAV2erV\nq7lx4waBgYEYGT2+2IqJieHatWuUlJRQp04dmjRpgoGBAUqlkrNnz5KVlUWtWrVo3rw5xsbG0kMX\nQgghHlVaWhpBQUFs27aNa9euPbbjxsbG0rFjR8LDwykoKKBLly6cPHkSrVbLpk2b+Oijj8jOzmbu\n3Lns2bMHrVYrPXQhhBDiUV25coXmzZvTo0cPvv/+e5o3b677XkhICBcvXsTW1pZatWphampKs2bN\nKCws5NixY6SkpFC7dm26du1KtWrVyhz366+/ZuTIkbz//vsA2NjYsHbtWlq3bs24ceO4efMmfn5+\ntGvXDm9vb7p3746tra300IUQQlQs+tDjBNi1axf9+vWjV69eLF++nNzcXABCQ0Px9/fHxMSE7Oxs\n2rZtq+vBf/DBB1y6dIlGjRpx6tQp5s+fX+64L774IoMHD9b9Pz4+HldXV8LDw2nTpg116tQBwN7e\nHoD8/HzpoQshhKhYNBoNBw8exNLSskI/zqioKHbs2MGiRYswNzfH3Nyc3377jY4dO/L999+zdu1a\nhgwZogtkhULBnTt3+Oyzz7h37x4WFhaMGzeO+vXr8+677+Lm5qY7tq+vLwCZmZns3LmTDRs2cOjQ\nIZKTk7GystL16I2MjGjbti2ZmZm4uLhIoAshhKg49uzZw+7du6lVq1aFfpynTp0C4M0338TQ0JCE\nhAS+/PJLOnbsSEhICCNGjND9rJOTE1qtlqKiIgCmTJmCQqFAoVDQr18/lEplueOfPXuW0aNHM2jQ\nIH788UccHR3Jzs4u87NqtZqbN29iY2MjPXQhhBAVx969ezlx4gSbN29m0qRJFfZx5ufnExwczIkT\nJ2jatCkAkZGRdOnShdmzZ+Pp6UliYmKZ3nydOnUwNDQEYMOGDZiZmVFcXMzJkydxcHAoc/yrV6/y\n7rvvsmvXLpo0aaL7upubGydPniQuLg5XV1fy8vLIzs7GwsJCAl0IIUTFsG/fPo4cOcKaNWswNTWt\n0I/17t27nDhxosytARsbGzw9Pbl69SqdO3emT58+ODo6Ehsby8cff8yWLVvw9vamS5cuHDx4kLZt\n23Ls2DFu375N3759yxz/xIkTNGnShIiICMLDwwGwtLSkW7duTJkyhU8++YShQ4cSFBTE0qVLK/yE\nOJBJcUIIUSUcOnSIffv28dlnn+lFOBUVFXH27NkyPWMDAwPWr1+PqakpLVu25OuvvyYiIgJra2vm\nzp2LgYEBpqambN++HbVazaeffoqdnR3Lli3DwKBs3DVr1ozu3bsD6IbmVSoVCoWCBQsW4O/vz+ef\nf07jxo155513UCgUFb7NFFp9meoohBDikXz99dds2bKFTZs26YaeCwoKGD58OMHBwXp5Tl999RXR\n0dFMmjSJgoIChg0bxrhx43jhhReA/19dzsDA4JHDWK1W/6fff5p8fHykhy6EEJXZd999x7p169iw\nYUO5+8j6rHnz5hw/fhw/Pz/q1q1L69atadu27f/vrSoUGBoa/qcw/q+//7TJPXQhhKikTp06xdKl\nS9m5c2eFn9H+sJydnfn2229JTU3F2tq6ws8JkEAXQgjxSM6ePcvs2bPZvXs3zs7OlfIcFQoFNWvW\nlCdbAl0IISqnixcvMmXKFPbs2YO7u7s0iAS6EEIIfXPlyhXGjh3L3r178fLykgapQmRSnBBCVBIh\nISGMGjWKXbt24e3tLQ0igS6EEELf3Lx5k2HDhrFz504aNGggDSKBLoQQQt/cuXOHYcOGERQUhJ+f\nnzRIFSX30IUQQo9FRkYycOBAtmzZgr+/vzSI9NCFEELom5iYGPr3709gYGCZDUaEBLoQQgg9ER8f\nT9++fVm3bh3NmzeXBhES6EIIoW+SkpLo27cvq1evplWrVtIgQgJdCCH0TUpKCv3792fZsmVlapcL\nIYEuhBB6Ij09nbfeeou5c+fSoUMHvdo4REigCyGEADIyMhg2bBiTJ0/m5ZdfljAXEuhCCKFvsrKy\nGD16NAEBAbz66qsS5kICXQgh9E12djbjx4+nX79+9O7dW8JcSKALIYS+ycnJYcqUKXTu3JkBAwZI\nmAsJdCGE0De5ubnMmDGDli1bMmTIEAwM5O1aSKALIYReyc/PZ/78+fj6+jJq1CgJcyGBLoQQ+qaw\nsJBFixbh7OzMO++8I2EuJNCFEELfFBcXs2TJEqytrZk0aZKEuXgostuaEEJUAEqlksWLF2NoaMj7\n77//VMI8NjaWK1euoFar5QnQ5565gQEFBQUotFqtVppDCCGeHZVKxbJlyygqKmLu3LkYGT35vpZS\nqeTYsWMYGhrKE1AJKBQKCXQhhHiW1Go1n376KampqSxcuBBjY2NpFPFIZMhdCCGeYZivX7+ehIQE\nPv74YwlzIYEuhBD6RqPRsHXrVu7evcvSpUsxNTWVRhES6EIIoW9hvnPnTq5cucLy5csxMzOTRhES\n6EIIoU+0Wi179uzhl19+Yfny5VhaWkqjCAl0IYTQN/v37+fEiRN88sknWFlZSYMICXQhhNA3hw4d\n4tChQ3z++efY2NhIgwgJdCGE0DdHjhzhiy++YMOGDdjb20uDCAl0IYTQN0ePHmXjxo1s3boVBwcH\naRAhgS6EEPrmu+++Y+XKlezatQtHR0dpECGBLoQQ+ubUqVMsWLCA4OBgateuLQ0inigp/SqEEE/A\nmTNnmDp1KgcPHsTFxUUaREigCyGEvrlw4QLvvPMOhw8fxt3dXRpESKALIYS+uXLlCgEBARw8eBAv\nLy9pEPHUyD10IYR4TK5fv86wYcM4cOCAhLl46gykCYQQ4r8LDQ1l6NCh7N27Fx8fH2kQIYEuhBD6\n5u7duwwePJidO3fi6+srDSKeCRlyF0KI/yAqKopBgwaxadMm/P399eZxazQaUlNTUSgU8iRWEjIp\nTgghHlFsbCz9+/fns88+o2XLlnr12AsKCrCwsGDOnDloNBp5MvU5yBUKFi1aJIEuhBCP4v79+/Tr\n148VK1bQtm1bvXv8BQUFjBgxgr1798qTWQnUq1dP7qELIcTDSkpKYsCAASxbtkwvw7yU9OcqD61W\nK4EuhBAPIzU1lf79+7Nw4ULat28vDSIqDAl0IYR4QOnp6bzxxhvMmjWLTp06SYMICXQhhNA3mZmZ\nDBo0iEmTJtG1a1dpECGBLoQQ+iYrK4uAgABGjhzJ66+/Lg0iJNCFEELfZGdnM27cOPr160f//v2l\nQYQEuhBC6JucnBymTZvGyy+/zFtvvSUNIiTQhRBC3+Tm5jJ79myef/55hg8fLhXVhAS6EELom/z8\nfBYsWEC9evUYOXKkhLmQQBdCCH1TUFDAkiVLcHFx4Z133sHAQN4mhQS6EELolcLCQpYtW4a1tTXj\nx4+XMBcS6EIIoW+Ki4v55JNPMDU15b333sPQ0FAaRUigCyGEPlEqlXz22WcolUqmTp2KsbGxNIqQ\nQBdCCH2iVqtZs2YNWVlZfPDBB5iYmEijCAl0IYTQJ1qtlnXr1pGQkMDs2bMxNTWVRhF6y0iaQAhR\nVW3YsIHIyEg+/vhjzMzMpEGEBLoQQuibzZs3ExISwrJlyzA3N5cGERLoQgihb3bs2MH58+dZvXo1\nlpaW0iBCAl0IIfTNnj17OHHiBGvWrJEwFxLoQgihjw4ePMihQ4dYv3491tbW0iBCAl0IIfTNkSNH\n2LlzJ4GBgdjZ2UmDCAl0IYTQN8ePH2fDhg1s3boVBwcHaRBRKck6dCFEpfbDDz+watUqNm3aRK1a\ntaRB9FBeXh4ZGRnSENJDF0JUVadPn2bhwoXs2rULZ2dnaRA9NWfOHM6cOcPp06exsLB4bMf96aef\nOHbsGIWFhbRo0YL+/fuXqUdw5MgRLC0t6dSpk/TQhRDiWfnll1+YPn06O3fuxMXFRRpET8XFxXHk\nyBHi4uL47bffHttxr127Rr9+/Xj55ZcZNmwYO3bsYN++fQAkJibyzTffMHnyZNLS0qSHLoQQz8qv\nv/7KuHHjOHToEG5ubtIgeuzs2bMMHjyY2rVrs2fPHjp06ACARqPhwIED7N27F3d3d3x8fHBycqJH\njx4kJiayfPlybt++zXPPPcfUqVOpWbNmmeOeOHGC2bNn88orrwAwY8YMli9fztChQ7l79y4RERF4\neHjoVVtJD10IUalcuXKFoUOHcuDAAb17Q34aiouLiYyM5OrVqxV+IxqlUsmePXt4+eWXad++PV98\n8QVJSUkAnDt3jhUrVjBjxgwGDBjAli1bSE1Npbi4mLfffpsmTZqwdetWPDw8GDduHBqNpsyxR44c\nyYgRI3R/5/jx47Rv3x6Ajh07MmHCBN2HBwl0IYR4ykJCQhgwYABHjx7Fy8urSrdFTk4Ot27d4pdf\nfuHAgQNMnTqVjh074ufnR79+/WjXrl2Fr18fHh7OkSNHcHBwwMLCAjs7Oy5fvoxWq+XMmTNMmDCB\nZs2a0bJlSwYOHIhWqyUyMpKjR4/i6+tLcnIy/v7+7N+/n6ioqDLHtrOzw9ramrCwMIYNG0Z8fDyj\nR4/WfV+hUKBQKPTqOZchdyFEpRAWFkbv3r355ptvqFu3bqU/X61WC0BqaipxcXFkZ2dz7949fv75\nZy5dukRRURGNGzembdu2PPfcc/Tv35+JEyeSmJjImDFjOH78ODt37qzQ5/jVV19hZ2dH7969AVCp\nVGzcuJFu3boRERHBiy++qPvZ0nr8SqUSgODgYAwMfu+zzpgxo9xOeiqViu3btzNjxgw2bdrEq6++\nirGxsV5fExLoQgi9Fx4eTp8+fTh8+DD169evNOel0WjQarVoNBoSEhKIjY0lJyeHu3fvcubMGUJC\nQrC3t6dRo0a0bt0ab29vmjRpQu3atXF0dCxzLLVazY4dO/jmm284fvw4tra2bN26tcKee0ZGBrt2\n7eL06dM0bNgQ+H2CnJubG+Hh4fj7+3Pjxg1at26NUqkkJCSEpk2b6oJ7xowZ2NjYkJWVxa5du7Cy\nsipz/NOnT7Nnzx6uXr2Kk5MTWq0WrVard71yCXQhRKURHR1N3759+fLLL3Vv/PpGpVKhVqtRqVTc\nu3dP1+MOCQnh3LlzpKen4+bmhp+fHy1btqR9+/Z069aN2rVrP1AJ28zMTBYvXoydnR07d+7E3Nyc\ngoKCCt0mISEhmJmZlRltcXV15fXXX+eXX37htdde0y0ni42NZffu3TRt2pR69eoxadIkFixYQMeO\nHTl06BANGjQo105nzpzBxMSEDRs26EY7atSowaRJk8p9oNIXCq0+PVohhPiD2NhY+vTpw7p163j+\n+ecr/OMtLi5GpVLpJqbFxcWRmZnJlStX+O2337CwsMDNzY0GDRrQrFkzHB0dsbS0xNHR8ZHvd0dE\nRDBx4kRGjx5Nr169dF8vKChg+PDhBAcHV8i2ioyMxNDQsNzExri4OHJzc/H29iY2NpaUlBRsbGw4\nfPgwzs7ODB06lKKiIs6fP09YWBj+/v60aNGi3JD73bt3KSwsLNvDNTLCz88P+P2WRlRUFGZmZjg5\nOVX4a8vHx0cCXQihn+Lj4xk4cCBLliyhbdu2FeZxabVaCgoKUCqV5Ofnc/fuXeLi4khLS+PixYtE\nRUXh6emJq6sr9erVo3HjxtjY2GBpaYmDgwNGRkaP7XGcOHGCJUuW8Pnnn+uCSl8C/d8cOHCAH374\ngUWLFpGdnc3gwYNZu3YtTZo0qZKvBx8fHxlyF0Lon4SEBIYOHcqiRYueWZir1Wry8vIoKSkhKyuL\n0NBQ4uLiSExM5LfffqOoqIiGDRtSu3ZtfHx86Nq1K/369dPN1n6S92oLCwvZvHkzUVFR7N27t1LW\nr+/cuTNRUVGMHDmSWrVqMWfOHBo3blylXxfSQxdC6JXk5GSGDh3KtGnT6Ny58xP/eyUlJWRnZ1Nc\nXExKSgo3b94kLi6O+Ph4bt26RY0aNWjYsCGOjo54e3vj4+NDtWrVMDc3LzcR62lISkpi3rx5tGjR\ngqFDh/7tWnN976GXKioqwtTUVK8ns0kPXQhR5aSmpjJy5EgmTpz42MO8oKCAzMxMioqKiIuLIyQk\nhNjYWKKiokhKSsLX1xdfX19q1KhBp06d8PLywtjYGDMzswqznvvKlStMmTKFBQsW6F1RlEdVrVo1\neWH8n2ca6PHx8eTl5REbG8uVK1eIjY2lpKQEpVKJSqVCoVBgZGSke9H4+PjQvHlz7OzssLW1lW0Q\nhahC0tPTGT16NCNHjuS111575ONkZWWRkZFBUVERERERXL9+nejoaKKiojA0NOT555+nbt26uLq6\n8uKLL+Ls7IyRkRGmpqYVtrKaRqMhODiY4OBgtm/fjru7u1wwEuhPVmJiInFxcXz77bf88ssvZGVl\n0alTJxo2bEiTJk3o0aMHZmZmmJubY25ujkajIT8/n8LCQnJzc4mLiyMsLIwLFy5w8eJFvL296dSp\nE23btsXd3f2ZDG8JIZ68rKwsxo4dyxtvvKErMvJvkpOTycjIID8/n9DQUK5cuUJUVBSxsbG4u7vT\nrl073NzcaNOmDYMHD8bR0REDAwOMjY0xNDTUm7bJyclhxYoVGBoaEhQUJO+DVdgTv4eenZ3NtWvX\nCAoKIjMzk4EDB+Li4oKfnx/m5uYYGho+9ItHpVKh0WhITU0lLCyMyMhItm/fTpcuXejevTuNGjV6\nbDNFhRDPPrDeffddunTpwpAhQ8p8T61Wc//+fdLT08nNzeXq1atcvnyZ8PBw8vLyaNasGe3ataN2\n7drUqFEDLy8vbGxsdKN/+n7fNSYmhjFjxjBy5Ej69+//UOdTWe6hi9890WVr8fHxfP3112zdupX3\n3nuPFi1a4O3trSvF9zhptVpUKhXXrl3j1KlTHD58mBkzZvDiiy9SvXp1eaaF0FN5eXm89957NGzY\nkNdee4309HQyMzP59ddfuXjxIuHh4djb29OmTRvatGmDnZ0dDg4OeHp6Ym5ujkKheCLvORXBiRMn\nmDVrFtu2bSu3JE0CXQL9sYiLi2PLli2cOXOGGTNm0K5du6c+aSEtLY1vv/2WJUuW8P7779OnTx8J\ndiEqsNK3oZycHO7du0dGRgYpKSkEBgZy5coVqlevToMGDWjTpg3NmzfH2toaBwcH3N3dyxUMqexK\nSkrYvHkz165d46OPPnrkuUQS6JUv0B/buHROTg779u3j8OHDzJ49m3nz5j2z4awaNWowePBgevXq\nxcmTJ+nWrRvz58/nxRdf1Kt7Y0JUttAuLaWZnp5OdHQ0mZmZ3L9/nzNnznD58mVMTEzw8/PDx8eH\nTZs2MW3aNLZu3YqLi0ul7Wk/bGdlxowZPPfcc6xdu1bvNxMRj9d/7qFrtVrOnj3L7Nmzee+99+jR\no0eFu8jS09PZtm0boaGhzJ49u8pvqyjEk1Rak1yj0ZCYmEhMTAxZWVlERERw9uxZoqKicHZ2pl69\nejRv3pz69etjYWFBzZo1cXBwoKSkhIULF2Jvb8/EiRMlyP9PSEgI48aNY968ebz00kv/+XjSQ5ce\nehm5ubkEBgaSmprKvn37qFmzZoU8UXt7e6ZNm0ZoaCjvvvsu48aN49VXX5XeuhD/QekSU7Vazb17\n94iJiSEzM5PQ0FAuXLiASqXC3d0db29vmjVrRteuXenTpw8ODg5/u6FIUVERS5cuxdramgkTJkiY\n8/sk4KNHj7Jt2za2bt1aJbaGFU+5h3737l0++OADhg4dSo8ePfQmHEs/hKSnpzN16lTs7e3lKhDi\nHxQWFlJcXExJSQmRkZFER0eTkZHBtWvXuHnzJo6Ojnh4eFCnTh0aNWqEk5MTZmZm2NvbP1TBleLi\nYlatWgXAtGnTZDj5/96v1qxZQ25uLtOnT3+gndWkhy499Idy7tw5Vq1axaJFi/D19dWrk65evTpT\npkzh+++/55133mHFihW4ubnJ1SAkPHJzKSoqorCwkLCwMKKjo0lNTeXKlSukpKTQoEEDXFxc8PLy\nom3btrq6Eba2tv85fJVKJZ9//jlKpZLp06dLmPP7BONp06bRs2dP3njjDRlRFP/qoQP92LFj7Ny5\nkw0bNuhtpTaFQkGXLl1wcnJiyJAhBAYGUq9ePbkaRKWn0WjIysqisLCQ7Oxsbt68SXR0NElJSYSE\nhGBsbEzjxo1xdHTE09OTl156iVGjRmFmZoaVldUTGQJXq9WsW7eO7OxsZs6cWeVmrf+Vs2fPMn/+\nfJYuXUqzZs3kwhWPP9C//PJLvv/+e9avX4+NjY3en/xzzz1HYGAgI0aMqNLb7onKp6SkhIyMDAoK\nCkhLS+P69etERUURHx9PZGQk7u7uNGnShBo1atCiRQvq1atHtWrVqFatGhYWFk/1A8bGjRu5f/8+\n8+fPrzA10Z/l8/bll1/y448/EhQUhIuLi1zM4vEH+sGDB/nuu+9Yu3YtlpaWlaYB6tWrxxdffMGb\nb77JF198gY+Pj1wVQm/k5+eTlpZGQUEBCQkJXL16lYiICKKjo8nLy6NJkyb4+/tja2tLnz598PLy\nwsTEBBMTk2feE9ZqtWzatInIyEg+/PBDzM3Nq/RzmZmZydy5c3F1dWXjxo2y6Yh4MoH+3XffsWPH\nDrZt21apwryUl5cXW7duZdCgQRw4cEDuqYsK+WafkpJCYWEhkZGR/Pbbb9y5c4d79+5hbW1N27Zt\nadCgAX5+fnTv3h0XFxcMDQ0xNjausGWQt27dSkhICIsXL66U7ysPIyQkhPHjxzNnzhyplyGeXKBf\nvnyZxYsXs3v3bmxtbSttQzRs2JBPPvmEgIAAgoODsbOzk6tDPHVJSUmkpKToNhS5ePEiYWFhpKSk\n4OvrS4cOHfDy8qJr166MHTuWWrVqoVAoMDQ01KslXkFBQZw/f55Vq1ZV6SqOWq2Wo0ePsmbNGjZt\n2iQjhOLJBXpGRgbjxo1j+/bt1K5du9I3Rrt27QgICGDp0qUsXrxY1sCKJ0KtVhMXF0dKSopuQ5Hz\n588TGhqKkZERLVu2pF27dri7u9OoUSM8PDywtbVFoVDo/umzXbt2cfLkST7//PMqvTNYYWEhn376\nKampqezdu/exLkkTEuhlqFQqPv74Y2bMmEH9+vWrTIO88cYb/Pbbbxw+fJg+ffrIFSIeqdcFv6+r\nLl36lZWVxa+//sovv/yim5TWqlUr2rRpQ8uWLenWrRvu7u6Vfug5ODiYw4cPs3HjxkoxsfZRJSQk\nMHnyZLp168b7778vu0OKJxvoX3/9NQYGBnTv3r1KNYihoSEzZ87kpZdewt/fX6oyib8N7dK65Lm5\nubrgTktL4/z585w/f57i4mJ8fHxo0aIFzZs3p2fPngQEBODq6oqJiUmVa7OvvvqK7du3s3379ip7\nS0ur1XLp0iUmT57MihUraN26tbyYxJMN9Pv377NkyRK++eabKvnJ0d7ensDAQKZPn05wcLBMUKnC\nNBqNri55RkYGkZGRpKWlkZiYyNmzZ7l58yb29vbUqVOHpk2b0rhxY5577jlmzJiBk5OT3g+PPy7f\nfvsta9asYdeuXdSoUaNKtoFSqeTAgQPs37+f3bt3y+Rb8diVK/2q0WhYsGCBbhiwKnv//fd56aWX\n6Nq1q1wplZxKpaKkpAS1Wk1ycjIRERG6HcF++eUX0tLS8PDwwNPTk8aNG9OgQQPMzc2xs7OrsgH1\noE6dOsX8+fMJDg6mVq1aVbINsrOzWbRoETY2NkyZMqVCrLcvKCjAwsKC7du3o9Fo5ELV5yBXKBg+\nfHj5QL9x4wYfffQRO3bsqPIVm+Lj4+nSpQvnz5+v0pN3KpPi4mKKiopQqVTExsYSERFBWload+/e\n5fLly5ibm1OnTh3c3d3x9/fHy8uLatWqYWtrK9fAIzhz5gwffPABwcHBODs7V8k2uHPnDlOnTmXM\nmDG89tprFWayrVqt5u7duzL5t7L20JVKJWPGjGH8+PE0bdr0gQ4QGxvL+fPndReFsbEx9evXx8fH\n5x8vFJVKhVKpxMzMjISEBEJCQnjllVceaYiyqKgIIyOjv709UFJSQlZW1iPtBrdt2za0Wi0BAQFy\nteiRgoICCgoKKCkpISIigoiICFJSUrh16xYRERF4eXnh6emJq6srDRs2xMXFBRMTE2xsbKp8tbLH\n5dy5c0ydOpXg4OAqObys0Wj46aefWLJkCcuXL6dRo0ZyUYgnqkwC3r17F41G81AXXnR0NG+//TYz\nZ87E2NiYgoICevfuzZEjR+jRo8ff/t6NGzc4duwYH3zwAVlZWVy4cIFXXnnlkV40H3/8MQMGDKBh\nw4blvq/Vajl58iRTpkwhNDT0oT8w9OzZkxdeeIF+/fpJD60CysrKoqCggMLCQkJDQwkPDyc5OZlb\nt26Rn59Po0aNcHFxwdXVle7duzNs2DBMTEywsrKSDUCeoEuXLjFlyhT27NlTJcO8uLiYjRs3cufO\nHXbv3i27OoqnG+gajYa9e/cyZsyYh5oEVrrRyeTJk3VD9E2aNGHBggV0794drVZLTEwMOTk5VK9e\nHTc3N7RaLREREdy9e5eEhARcXFwYOnSoLmzT0tJIS0vDxMQENzc3jIyMyMjIQK1Wo1KpyMzMpHr1\n6ri6upKWlsatW7eIiorC09Oz6Q1okwAAIABJREFUTB3qmJgY5s2bx44dO+jQocMjNZCtrS2jRo3i\n7NmzvPrqq3LFPKOeTnp6Onl5eeTm5nLjxg3dtXPnzh1sbW1p0aIFjo6O1K9fn169emFtbY2JiQkW\nFhYypPiUXb16lbFjxxIcHIynp2eVO/+EhARmzZpF+/btWbVqlWw2I55+oCcmJnL+/Hlmzpz5SAf6\nY89XrVbrluXs3buXwMBA+vTpw5dffknfvn0JCAjg8OHD3L59m59//hkfHx/mzJnDt99+S0hICEOG\nDKFnz56cP3+eTp068f7773P69GmWL1+Ovb09Pj4+bN68mfPnz5OYmMjt27f56quv8Pf3LxPotWrV\nYuHChfTr14/ly5c/8nn17NmTMWPG8NJLL8mL8wkqKSkhJSWFvLw8MjIyuHr1Krdv3yY2NpakpCQa\nNGhA8+bNsbe3p3Pnzvj4+GBmZoaxsbEMk1cQN2/eZMSIEezdu5c6depUufP/7bffmDZtGgsWLKBd\nu3ayykE8m0A/e/Yso0ePfqQNAX744QfmzZuHsbExcXFxBAUF8cMPP6BQKIiJiWHjxo34+PjQpk0b\n3n//fcaOHcu4cePYvXs3AwYMICQkRNeLmjJlCqtXr+aFF14gKyuLVq1a0bt3bxQKBRqNhh07dmBj\nY4NarebWrVv06dOH559/nnHjxuHu7l7mcVWrVg03NzeSk5P/UyO5uLjg5+dHZGSk3u3/XhHl5+eT\nlJREXl4eiYmJXL58mVu3bhETEwNAy5YtadasGW5ubrRp00a3oYiRkZEMk1dgd+7c4c0332Tv3r1V\nbjtitVrNvn37CA4OJigoqNx7kRBPLdBVKhXbtm1j/fr1j3QQLy8vWrVqhampKQYGBsyaNQsvLy8A\nBg4cyMmTJ5k/fz5XrlzBy8sLrVaLgYGB7l+pjIwMTp48iaOjI/v370ej0ZCZmcmdO3cwNDSkd+/e\n2NjYoFAo8PX1JT8/X1cK08DA4Il9GjY0NOTll1/m6NGjEugPITMzk4SEBPLz84mOjubXX3/lxo0b\nxMfH4+bmRvv27WnYsCFt2rRhwIABuLm56WqSy9p//RIZGUnv3r3ZvXs3fn5+Verc8/Ly+PjjjzE2\nNmbHjh1VfqMZ8YwDPSYmhurVqz/y/S5PT09eeeWVcsOeOTk5tGvXjkWLFjF37lwAhg8f/o/BCdCv\nXz/dJgX9+/enXr16XLp0iWrVqj2zIaw2bdqwZMkSxo4dW6U3k/griYmJJCQkkJeXx507dzh37hzX\nrl2jsLCQJk2a0L59e3x8fOjfvz9Tpkyhdu3alaYuuYB79+7Ro0cPdu7cWeVmckdFRTFp0iSGDx9O\nz5495YOoePaBfvPmTYYOHfrYD146kemFF17AycmJdevWUb16dbRaLYaGhqSlpaFSqXQ/b21tTa9e\nvcjNzcXPz4/o6Gh69OhBSEgIf1ouX4axsTGZmZlPtKEsLS1p164dqampVSrQS9tdpVIRFxdHYmIi\nubm5XLt2rUyltBYtWtCuXTvq1avH888/j6urq8zsrQLi4uLo0aMHQUFBNGvWrEq9Ls6ePcvUqVPZ\nuHEjTZo0kYtBPPtA12q1HDt2jEmTJj3SAQwNDf92W1V3d3cmTJhAq1atqFGjBuPHj8fMzIwvv/yS\nPn36EB0dzdKlS3nttdd01bZWr17N2LFjmTdvHubm5uzatQtPT09CQ0PL3N83NTXF2NgYAwMDunTp\nwsCBA/nxxx9p0KBBucdhYGCAo6Pjf2ooQ0NDGjZsyPnz53W3EyoTtVqNVqvVbSiSlJREdnY2ly9f\n5ty5c6SkpODh4UGzZs1o1aoVL7zwAr169cLFxUWGGKuo+/fv06dPH9avX8/zzz9fZc5bqVSyceNG\nLl++zLFjx6RSoKgwFCUlJdrGjRtz+fLlJzZTODExETs7O0xNTVEqlajVaqpVq4ZSqUSr1ZbbqEKj\n0ZCQkIC1tfUD94bz8/MxNzd/okO4ERERrFu3jlWrVultr0KpVKLRaMjPzyciIoLk5GTS09O5cOEC\nly9fplq1anh6etKoUSOaN2+Ora0tNjY2ODk5VckNRcTfv6b79+/PwoULefHFF6vMeaempjJv3jx8\nfX15++23ZdWLqFg99NDQUJo3b/5El/38cS91Y2Nj3Uzlv5uxbGBggIuLy0P9jT8uV3tSHBwcuHTp\nEpmZmX87KlERaDQaiouLUalUZGdnEx4eTlJSEklJSZw/f56oqChcXV1xc3Pjueeeo3HjxrRu3Rpr\na2tq1aol97XFP0pKSmLo0KHMnTu3SoX5rVu3mDhxItOnT3+kIlhCPPFAT01NpX379tISD6BatWp4\neHhQWFhYIQJdpVJRUFCASqUiJSWFu3fvkpSURGxsLL/++ivFxcXUq1dPt+yuS5cuuqIrVXX7SvHf\nJCcnM2rUKCZNmlRlQk2j0XDkyBE2btxIYGBglVxfL/Qo0OvXry8t8QBMTU3x8/MjJycHJyenp/Z3\ni4qKyM/PR6lUEh8fz+3bt0lKSiIyMpKQkBAcHByoV68eTk5O+Pr60rlzZ9555x2srKxkRr54bFJT\nUxk3bhwjR47ktddeqxLnnJuby2effUZRURG7d+/GxsZGLgRRcQP90qVL/ynQ8/PzKSoqws7OrtxQ\nrUajISUlhZKSEoyMjHB0dNQt6ygt4fpnCoWiQk8ysbOz4/bt20/kQ1BpadPi4mKioqIICwsjMTGR\nO3fukJSURP369albty61atWiQ4cOvPXWW5iYmGBpaflIBYGEeFAZGRlMmTKFN954g969e1eJc753\n7x4zZ87k9ddfp2/fvn+7+ZMQFSbQw8LCHnn4VavVsmXLFjZs2MCPP/5YZiZ5Tk4Oq1at4sMPP8Tf\n358bN24wc+ZM/ve//2FtbU1CQgJt27YlPj6+zDHr169PWFjYIz+eI0eOcOrUKVQqFR07dqRv374Y\nGhoSFhbGrl27dLO1R48e/UgfHFxdXYmOjv7Pb445OTkUFRURGhpKaGiori65gYEBzZs3x83NDScn\nJzp16oSDgwPGxsaYm5tLpTTx1GVlZfG///2Prl270r9//ypxzufOnWPWrFksX76cFi1ayEUg9CPQ\nMzMzH3nZUU5ODkuXLqWoqIhz587Rp08f3fcWLFhASUkJSUlJVK9enfz8fGbNmsX69ev54IMP0Gg0\nxMfHc//+/TIT2v7LhKwffviBxYsXs2LFCoyMjBg7diw2NjY0btyYQYMGMXnyZN566y2CgoJ0j+Vh\nN+6wtbXl1q1bD/ThIjk5mezsbPLy8ggJCeHmzZvExsYSExODm5sbrVq1olatWrRq1QofHx9sbGww\nMjKiWrVqsqGIqBByc3OZNWsW7dq146233qr056tSqQgKCuL06dPs3r27zIReISp8oOfl5T3ycO2F\nCxfo2bMn7du35+uvv+a1117D1NSUsLAwVq5cSUJCgq7Xbm5uzqxZs/jtt98oKirSHcPa2vqxzFDX\narVcvHiRkSNH0q5dO+D3qnRXr17FysqKuLg4Bg0ahIGBAaNHj6Zt27Z8+OGH1KpV66H+jqWlJSUl\nJbr/l5SUkJiYSHZ2NllZWVy+fJkbN27odphr0qQJrVu3pkaNGvTr14+6detiYWGBoaEhxsbGMqNc\nVFj5+fl88MEHNGrUiGHDhlX6azU9PZ1Fixbh5ubGxo0bMTc3l4tA6Fegl5SUPNKFq1QqOXjwID17\n9qRp06a89dZbzJs3Dw8PDzIzM2nRooUuLFNSUsjIyEChUNCwYcMybwyHDx8us2SuTp065cpHhoaG\nkpOTUy7AGzRooJukolAoGDt2rO7DSUFBARcuXKBXr140atSIsLAwXa/33LlzujXWD6t69er88MMP\nKJVKrl27RmpqKkZGRrRp04YWLVrQqFEjunTpgoeHByYmJlKXXOiloqIiZs+eTYMGDXj77bcrfZjf\nuXOHt99+m+nTp1eZCX8qlYpff/1VRgMrU6CXhuPDvmCTk5MJDAxk2LBh5OXl8eabb/LTTz8xfPhw\nMjIyygxVnTlzhqVLl6JWq7ly5QpxcXFl3jj+/EHhz65fv87t27fLfE2j0eDs7Fxm1qm9vT1arZbL\nly8zZ84cvL29ef311zEzM8PMzIykpCQ2btzI/v37CQ4OfqSiEAqFgh9//JEbN27QrVs30tLSuHnz\nJteuXaNevXq4u7vj5eVF3bp18fDwwNTUFFNTUywtLbGzs6NGjRoyuUZUaMXFxcydOxdXV1fGjRtX\n6cP8q6++YuXKlWzdupW6detWmee5pKSEt99+m9WrV6NWq+XC12MGBgYMHjwYI0NDQ/Lz8x96edPp\n06dxdHRk7NixuosjISGBAQMG4OTkxJEjR1CpVBgbG9O3b1/69u1LZmZmuQl4b7zxxr/ew3/zzTf/\nspb7n99oiouL+eSTT9i/fz/Lly+nQ4cOut7xjz/+yMCBA5k7dy5nz57F2tr6kRouJyeH3r17o9Vq\nsbS05PDhw9jb26NUKklLSyMjI4O8vDyKi4t1k94iIiKIjIzk3r17pKenY2pqiqurK3Xq1MHb25s6\ndepQvXp13Yx1W1tbatasqRu5KD1PGZ4XT5pSqWThwoXY2dkxadKkSn3NFRYW8vnnn5OUlMRXX31V\noYtFPSkNGzakc+fOcuFXAjY2NhiZmZlRWFj4UIFeUlLCokWLOHjwIG3atAF+X6Pq6OhIREQE7u7u\nPPfccxw/fpzu3btjYGCAWq3m2LFj5Y6l0WjKfTr88xD1ggULmD9/frnfDQ0NLVO7/euvvyYkJIQT\nJ07oAlur1RIbG0vv3r05deoUTZo0QavVotFoHmmoKScnh9atWzNo0CCGDBlC586d+eKLL2jQoAG1\na9d+oEk0mZmZuuAvKipCqVSSkJBAZGQk0dHRxMXFkZKSQm5uLtbW1nh6elK3bl28vb2pUaMGJiYm\nWFhYYGdnR82aNXXn+jS2khWVl0qlYvHixZiamjJt2rRKPRSbmJjI5MmT6dy5M1OnTpXbYqJSMCqd\ngf4wQkJCMDAwKBOmDg4OTJs2jZMnTzJ58mSWL1/OwIEDmTBhAn5+fly6dImwsDBat25d5ljTp08v\nsxTLxMSEhQsXlrmv/s477xAQEFDm97RabZkJbSqVip9//pni4mJWrFih69E3a9YMa2trtFot+/fv\nZ//+/cDvRWImTpz40J/KMzMzMTExwcnJiU2bNjFixAj8/Pz45ptv6Nq16wO9Mdja2j7Q3y0oKCAz\nM1O3Nl2lUpGWlkZUVBT37t3j/v37pKamkpmZSUlJCTVr1qROnTrUrVsXZ2dnXfCX9vhr1KihC/vS\ne/vyRibg9815VqxYgVqtZvbs2ZX6ttClS5cYP348n3zyia5DIkSlCHQfHx+ysrIe6pecnZ05ePBg\nuVB67733yMrKQqPR0KVLF65du8aNGzeIj4+nR48ezJ8/n+TkZOzt7dFoNFy/fr1cT1KhUJRba12z\nZs1/fUwKhYIxY8ag0WjKfN3KygqAs2fPlvm6gYHBI9Wv/+PMfS8vL12oDxo0iOnTp/Pee+89ttmx\n5ubmD3QslUpFVlaWrsevUqnIy8sjJiaG8PBwEhISSE9PJysri9TUVIyNjXF2dqZu3bq4u7tTrVo1\nXfA7Ojri6OiIiYkJCoUCQ0NDjIyMZP17JQ/z1atXk5OTw9y5cyvtc61UKgkODubo0aMcOHDgofeL\nEKKiU2zZskVbv359+aT6gJYtW0aXLl3KzMS/ffs2o0aNwszMDGdnZz788EPc3d0r5OPPycnRVfdT\nqVQUFxcTHx+v2+u8tOhNQkICOTk5WFtb4+7ujqenJ1ZWVpibm+uG+p2cnDA3N9f19k1MTDA1NZXh\nfj2i0WhYt24d9+/fZ86cOZV2qVZmZibLli3D2tqa8ePHy5a//D4COHz4cIKDg+WFUAn4+Phg5Obm\nRmRkpAT6AygpKeHu3bsMHjy4zNfr16/Pxo0bGTNmDG5ubowePZoPP/yQVq1aVbhzsLKy0o1alGrY\nsGG5nyssLKSgoEA31K9SqUhKSiI+Pp7o6GiuXLlCfn4+SUlJJCQkoFKpqFmzJh4eHtjb22Nubq7r\n8bu4uGBjY4OBgQFGRkaYmJhgZmYms/2fMa1Wy6ZNm4iOjubDDz+stGEeERHBlClTCAgI4PXXX5dl\nWqLSMrK1teXAgQMMGTJEWuNfFBUVER4e/pfL3fz8/Fi3bh2jRo1i1qxZBAYGEhYWxpAhQ/QyuEqX\n+v2Rl5dXuZ9TKpUUFhZSUlKCSqVCo9GQkZHB/fv3SU5OJjQ0lEuXLpGWlkZMTIxulr+zszOOjo66\n5XylwV9a79/IyAhTU1PMzc2lTv0TCvNt27Zx48YNFi9eXGl7rCdOnGDlypWsXLkSPz8/eeJF5Q50\nX19fzp07h1KplPuk/yIzMxN/f3/s7e3/8vv+/v66nvrnn3/OxYsXmT59OjNnzvzb39F3f9zfvpST\nk1O5Xn/pHu0lJSWo1Wq0Wi15eXkkJCSQnJxMZmYmly9fJjs7m6ioKGJjY8nPz8fW1pbatWtjZWWF\nnZ0dtWvXxtXVVTfpr7RUroWFhews9xBh/sUXX3Du3DlWrlxZbsSmMigsLGTr1q2EhYWxY8eOB5qH\nI4TeB3q1atVo0KABUVFR1KtX76F7rJs3b9YVhymd+d62bVusrKyIiori4MGDtG3btszs9tzcXLZu\n3YqHhwevv/46d+7c4dy5cwQEBPyn+68qlYq7d+/i7e1dJmQiIyPJyMjA0tKSevXqPfKQ26VLl/D3\n9//Hn2nSpAnr1q1j/PjxBAUFce/ePQYNGsSnn35apbepLZ2E+Mdev729/V/ONSjt7ZcGf2l53ZSU\nFDIzM3VL/GJiYoiKiiIhIQGtVouDgwM2NjbY29vj5OSEm5sb7u7uWFhY6ILf0tISGxubKj27f+/e\nvRw/fpy1a9dWyu1AU1JSmD59Os8//zyrVq3CxMRE3ulF1Qh0hUJBnz59iI2NfehAV6lUTJgwgcDA\nQBwcHFCr1ezatYsDBw6wceNGUlNTef/993nzzTfLBHpYWBiTJk1i7ty59OjRg4yMDHbt2lVuadrD\nOnPmDJMnT+ann37SvVF99dVXTJgwgUmTJrF7925GjBjBu++++9DH1mg0hIaGMmDAgH/92ebNm7N6\n9WpGjRrFzp07+eyzz3jnnXeYMmVKlSkr+V+YmJiUexP+q53x1Go1arUajUaDVqtFrVaTmpqqC/78\n/HwiIiKIj48nPDyc6Oho0tPTdff3a9SogYuLi27Sn52dXZngt7W1faSVEBXZwYMH2bt3L5s3b66U\nhVRKl6StWrWKNm3ayARNUbUCHX6f1LVq1apHrhjUpUsX3NzcAPD19cXX15fPP/8chUKBlZUVV69e\nJSYmBg8PDwBOnTpF06ZNy7zY/stElaysLDw8PMjOzqZTp066r6enp/PBBx9w7Ngx/Pz86Nq1K//7\n3/8YM2bMQ/fQCgoKOH78OBMmTHign2/ZsiVLly4lICCAHTt2cODAAebPn8+tW7eYOHGi3Bd+DP5q\nHb2lpSWenp5lvlZaSKh0SaNWqyUrK0sX/Hl5eRQUFHDv3j3u3r1LREQEMTEx5OfnY2VlRc2aNXF1\ndcXT05M6depQu3ZtXfBXr14dOzu7R648+DR98803bN68mR07dlS6W0AajYbg4GDdv4q6ykSIJx7o\nvr6+3Llzh9jYWF0wP4z09HTMzMzIy8vjzJkzvP7667oh79q1axMQEMD169fx8PAgKyuLnTt3MmTI\nkIcuaPN3rK2tuXfvHtHR0WV6+cnJydSsWRONRsPp06cxMzPj8OHDjzTceuHCBV555ZWH6tW0bduW\nefPmMWrUKIKCglixYgU7duxg1KhRLF++XLZmfEpK19P/8XmvWbPmX95X1Wq1ZcoM5+fnlwn+0uH/\niIgIwsPDiYyMJD4+HrVajYODw9/W8i8Nfnt7+2cySfL7779n5cqVBAcH/+Vohz7Lzc1l8eLFGBsb\ns3Pnzseye6MQehvoxsbGvPvuu1y/fv2hA93Z2ZlevXphbGyMWq0mJiaGjRs36nrcZmZmdOjQgU8/\n/ZSePXty+/ZtOnfuTN26dbl+/fo/Hnv9+vXMnTu3zNfS0tK4ePEiLVq0KPOGbW1tjZWVVZkysqmp\nqfz8889MnTqVbt26sWfPHrp3717umP9GrVZz+vTpBxpu/7MXXniBkpISRo8ezZYtWwgICMDHx4du\n3bqxdetWmjZtKldhBQv/P44cVa9e/YEm2ymVSl3VvtJa/oWFhdy6dYuIiAhddb/09HSqVatWrpa/\nhYVFmU18HBwcHlst/59++on58+dz6NAhHBwcKtXzFRMTo6sk2bdvX1mSJiTQATp06MDEiRPp0qXL\nQ00iuX//PnFxcbi4uKDRaIiIiKB///74+flhbGyMVquladOm7N+/n6ioKH788Uc6der0QL3kN954\ngx49epT7+oP2MEqHWIODg7G1taVLly506tSJkSNH4uzs/MDnmJKSwtmzZ5k5c+YjNfIrr7yCUqlk\n1KhRbNu2jXbt2nH06FHGjx9P7969GTx4sLwR6TljY2OcnJxwcnL615/NyMjQBX9pLf/4+HiioqJ0\ntfxTU1PJy8vDxsYGDw8PvL298fb2xs7OrtwmPv9Uy//MmTNMnz6dw4cP6yocVgZarZbTp08zZ84c\n1qxZU27LZSGqdKC7urri6+vL9evXy/R+HyY4DQwM8PHxoU6dOmRlZel6AyYmJixZsoRvvvmGbdu2\ncebMGS5cuPCvxy29l/nnF3KbNm1wdXX919+vVasWrq6uuoIZHh4epKSkUFxc/FDnd/ToUSZOnPif\nJki99tprZXrqzs7OfPHFF6xevZpZs2Yxffr0SjnjWJRnZ2dXbtfBv5Kfn09WVlaZWv6pqallavmX\n7vD3x1r+3t7eODk5UVhYyIgRI9i9ezdKpZL8/PxKUcu/qKiIL774gvPnz7Nv374yezoIIYHO7xOM\nBg4cyLp162jWrNlD9RhDQ0PJzMxEpVJx5coVzp49y4oVK0hLS9P9TPv27WnVqhUTJkx44J5C6eYk\nf/7w8Od67X/HyckJFxcXfv75Z1q2bMlXX31Fz549H+q2Qk5ODqtXr+bMmTP/ubF79+6NUqlk7Nix\nBAYGYmVlxcyZMzl48CBDhgxh9erVf1m8RVRNFhYWD3Q/WKlUkp2drevxq9Vqjh8/zq5du1i0aBEX\nLlzg6NGjZGVlkZaWpqvlX6dOnb+s5V+rVi2MjIx0oV+RavmnpaUxZ84c6tevz/r162VJmhB/FegA\nDRo0oKCggFu3bvHcc8/96y8bGBgwbdo0jh49ikajQaFQYGdnx88//4yXlxcFBQW8+uqrwO9FV8aP\nH8/rr78O/L7jWOlsZEtLS9q1a1fu+C+88AIvvPDCA5+MiYkJr776qq7XYW1tTWBgIKtXr2by5Ml0\n7NiRNWvWPNSkpCNHjjBhwoTH1nseMGAASqWScePGsX79eiwtLenTpw8+Pj6MHj2aGTNm0KlTJ1lu\nIx6YsbExNWrU0N2KunbtGnv27GHfvn3UrVu33AfU0lr+arWaoqIiYmNjiY+P5+bNm7pa/omJieVq\n+VtaWuq27XV0dMTJyQkzM7OnVsv/xo0bTJkyRV4jQvwNhfaPU3qBq1ev8sknn7B58+ZK8+lXo9GQ\nk5Pz0KGcnJxMp06dOHfu3GMdDtdqtWzfvp2ff/6ZNWvW6G4JpKSksGDBAnx9fRkxYkSlWwMtnryb\nN28ybNgwdu3a9Z8KGf1TLf/k5GQyMjLIz88nMTFRV8vf0dERDw8P7OzsygS/i4sL1tbWj1zLv3TE\nYdOmTSxbtuyh62WIvyabs1QuPj4+5QNdo9Ewb948OnTo8Mjr0iuLuXPn0rJlyydSDEaj0bBlyxYu\nX77Mp59+qgvvoqIigoKCuH79OvPnz69UE5nEk3X79m0GDRrE9u3b/3LDnSfhj7X8Swv9pKenc//+\nfd1yv4KCAlJTU7l37x5paWllavmXBn/pfJeaNWuWqeUPsGnTJoqKipg2bZosSZNAFw8T6ADx8fEM\nHTqU/fv3P9Dkncro2rVrfPzxx3z55ZdPbN2wSqUiMDCQ0NBQVq5cqXsD02q1/Pzzz8ydO5dPP/2U\nJk2ayNUq/lF4eDhvvvkmmzdvrpDXS2ktf6VSiUql0tXy/3Pwl9byT05Opri4WFfsJyAgAC8vL6nl\nL4EuHjbQ4felXnfu3GHWrFlVbklVTk4OPXr0IDAw8IkP7ymVSjZs2EBMTAyLFy8uc5sjOjqayZMn\n89Zbb9G/f3+5Zyj+UnR0NH369GHz5s00a9ZM789HpVLx7bffMmvWLLZu3YqbmxtJSUllgr+goIDo\n6GgiIyNJSEgAfi8WZGNjQ40aNcrU8jc3Ny9T2a90K18JdAn0KhPoSqWSqVOn0rNnT1566aUq0yha\nrZYPP/wQPz8/+vfv/1T+plKpZPXq1WRkZDB//vwyoZ6Tk8OKFSswMDBg6tSp0gsRZcTFxdGrVy/W\nrl1Lq1at9P58lEolO3bs4PTp0yxduvQfqyn+VS3/lJQUUlJSyMrKIj8/n5KSEuLi4srU8jczM8PW\n1hYHB4e/reVfvXp1bG1tK3WJZgn0KhTo8PskrR49erBv375HKgmrj44dO8a3337LqlWrnmqJzpKS\nEpYuXYpGo2HmzJlllgmV1qkOCgoiMDCwyjwX4p8lJCTQs2dPVq5cSYcOHfT+fLKyspg9ezbu7u5M\nmDDhsYVpaS3/0rK+Wq2WzMzMMpv4KJVKkpOTCQ8P19W/KCgo0NXyd3Nz09XyL13Wp2+1/CXQq3ig\nw+81zFesWEFgYGClv59+/fp1Jk6cyP79+59Jiczi4mIWLFiAtbU1kydPLrf2NyQkhICAAJYsWVKl\nRk1EeUlJSfTr14/58+fz8ssv6/35hIaGMmHCBKZPn87LL7/8zIbE/6mWf2mPPzs7m/DwcF0t//v3\n76PRaKhZsybu7u7UqVNPK4ToAAAgAElEQVSHunXr4ubmhomJSZngr1GjRoUp5qNPgR4XF0deXh4N\nGjR4Is+5RqP5y+dFqVRWmBoMjyXQAQ4dOsQ333zDZ599VmlnmUZGRtKvXz+Cg4Px9vZ+Zo+juLiY\nWbNm4ebmxrvvvltulCA5OZmZM2fy/PPPM2LECCmsUQUlJyczaNAgpkyZoqvzoK80Gg0nTpxgyZIl\nrFu37om8YT8NJSUlulr++fn5ulr+0dHRulr+sbGxpKenY2Jigpub2z/W8q9Zs2aZEYo/7zFQ1QK9\nb9++HDx4kLS0tMe2U6BKpWLbtm0sWLCAnJwchg0bxrx587C3tyciIoJ3332XS5cuYWdnR2BgYIXv\nRD1woAO6pVRLlizRzcauLBISEujfvz9r166lcePGz/zxlC7R8ff3JyAgoFyol5SUsHbtWu7cucNH\nH31U6bbCFH8vNTWVkSNHEhAQQK9evfT6XIqKivj000+5f/8+CxcurDKlj9PT08uU9FUqlcTFxelq\n+cfHx5OWlvaXtfxtbGx0tfxL1/lXr15dF/h/ruVfGQL99u3b9O7dm+rVqzN//vwyH2I1Gg0qlUp3\n3oCup61UKnU97L/qZZ86dYqZM2cSFBSEtbU1CxYswNPTk6lTp+Lv78///vc/unfvzvXr13nppZdI\nSUmp0JsbPVSga7Va1qxZQ3h4OIsWLcLKyqpSvLiioqIYPnw4H330Ee3/H3tnHlfT9v//V8aM4Zq5\n5lzXPGeeI+6NiqR5IJpLpSSk0yiiQYM0Fw1cSoYrhJBIhjJV0lxI83hO55z1++PzvX6f+zFVTnVO\nrefj0T+nvddee62192uvtd7D0qV8U6+6ujoYGhpi8eLFUFJS+mI5iBCCGzduwN7eHidOnMCUKVOo\n2nUAIdDW1oacnBw2b94s8B/Re/fuxcqVK6GkpCQwy5qtyT+x/P8J6ctms/Hx40dkZWUhJycHhYWF\nKCkpQUlJCRoaGjBkyBCMGzcOoqKiGDp06BfCP2DAgH/F8mcymdi+fTvfC7q7uzuA/6TiDg4OxsWL\nFz9/EDo5OcHKygri4uIQFRXFvHnzoKamhmfPnmH79u0ghKBHjx7w8fH54h3p4eGBfv36QVFR8bPA\nOzk5ITw8HJs3b8bp06cxdOhQlJeX47fffsOtW7cwefJkvhb0Rlt9CQkJQU9PD6GhodDW1sbx48e/\nmk9akEhNTcX27dvh6emJuXPn8lXdevTogePHj0NXVxfdunWDnJzcv/YVhYSEIC4ujtGjR8PU1BS7\ndu2ChISEQCbboPyY0tJS7N69G5s3bxZoMSeE4MmTJzAxMYGNjQ1ffUTzG02J5f+PVf8/IX0rKyuR\nnZ2NjIwMFBUVfc7wV1xcjB49enz25ef3LdTq6mqEhYXh5MmT6Nu3L9TV1ZGZmYnx48cjNjYWr169\n+hyp0NDQEFwuF+Xl5di6dSsCAwMxd+5c3L59G6qqqrh3796/tjE0NTU/r2R8/PgRHh4ekJWVRb9+\n/XDp0iX06NEDbDYb8fHxqKura1QmxbamSWbcQkJCUFZWRt++faGsrAw3NzeBDMPI5XIRFxeH/fv3\nIyAggG9nt7169YK7uzu0tLTQtWtXyMjIfGEsNHHiRAQHB8PBwQGvX7+GlpYWevfuTd+G7Yjy8nKY\nm5tDQkICcnJyAnsfbDYbERERuHDhAkJCQhqVMZHyY7p27YpBgwZ9sRy8cOHCL44tKyvD/fv3oa+v\nj2nTpvH1EjIAvHz5Eg8ePMDHjx9RUVGBAQMG4P79+xg7diwSExOxbdu2z66N4uLiIIR8dlPMy8tD\nfn4+WCwWkpOTkZ2d/a9wyN26dUNDQwMuX74MHR0dGBkZQV5e/vOEKicnB25ubnj69CkSEhIEYkuo\nWaakmzZtwqFDh6CqqoqoqChwOByBGfxVVVU4evQogoKCEBERwfdL1X369IGnpyciIyMRFRWFr+2Q\n9OvXD3Z2dhgyZAh27NiB/Px8+pZrJ1RWVsLc3BxLly79/LIR1I8SS0tL5OfnIygoiIp5G5CZmYmj\nR4/C0dERXl5e8Pf3R1VVFV/X+a+//oKCggLu3r2L69evQ0JCAt7e3mhoaEBpaem/Pkj+0aH/zcbZ\nvXt3BAYGfmFrVFNTAwsLC5w5cwZXrlzB7t27P+fVuHv3LpYsWYLZs2fj4sWLrRZKmRdLYM3m/fv3\nRF9fn1haWpL3798Tfufly5dkw4YNxNPTk9TV1RFBorS0lMjIyJDo6OjvHvfw4UOyatUqcu/ePUIR\nbKqqqoi+vj7x8/MjHA5HYO/jzZs3ZNOmTeTSpUuEzWbTjm1l8vLyiKOjI5kwYQI5e/YsqaysJIQQ\nUlNTQ2RlZfm23kVFRQQAycnJ+dd7EAB5+vQpOXnyJLGysiJcLpeUlpYSWVlZ4ufnR7KysggAkpeX\nRwghJDMzk+jp6ZGKiop/lX/27FmioqJCioqKSHV1NamqqiK1tbWEyWSSrl27ktjY2M+/V1VV8f3Y\nFRUVJfjZQphMJgkPDyczZ84k58+f50uh/PTpEzly5AgRExMjDx8+JFwuVyAfzOLiYrJx40Zy+fLl\nHz7AioqKJCgoiLBYLPpGE0BqamqIiYkJ8fb2FlgR5HK5JDY2lqxevZqkpKTQTm1l3r9/T9zc3Mig\nQYOIv78/KS0t/WKM8bOgh4eHk82bNxMmk/mv33ft2kUOHDhAPn36RCQlJcmWLVvI2rVrycqVK4mf\nnx8hhBAfHx8iJiZGTExMyG+//UbOnTv3RflGRkYEAFm4cCERExMjYmJiREVFhbx48YIAILNmzfr8\n+9ixY8mbN2/4XtAbbeX+IwoKCnDq1Ck8fvwYBw8exNy5c9s8XnJVVRVu3rwJS0tLmJubQ0ZGRuD3\nlz98+AANDQ0YGhpi7dq13zyurq4Orq6uKCoqgrW1dYdxCWoP1NfXg8FgYPjw4dDS0mrViIW8gslk\nwsvLC2lpabC1taWula28vREdHY0DBw7A3NwcsrKyXzVg5ne3tZqaGggJCX1eBv/vdxubzUZtbS2q\nq6vRt29f9OrVCy4uLhgzZgwUFBTA5XJRUFCA3NxcjB07FkOHDv1Cj6qrq79YnhcSEkL37t1RX1//\nRX3+yQnAr0ycOBHg5RcCh8MhKSkpRF1dnUhJSZG7d++ShoaGVv9SKSsrI5GRkWTWrFnE3t6eFBYW\ntqsv74KCAiIuLk5u3rz53ePYbDaJjY0lK1asIC9evKBTFgGAyWSSgwcPkqNHjwrs6kpRURHZvn07\nOXXqlMBtbQkytbW15K+//iKTJk0itra2JD8//7urkfw+Q/8RV69eJevWrSOPHj0iV65cIWPHjiVZ\nWVkdtv95suT+LSF59eoVYTAYZOXKlSQ6OppkZ2e3+PLeixcviLe3N5kxYwbx9/cn79+/F9jl9R+R\nm5tLli9fTuLj43947OvXr4m4uDi5fPlyu22P9kBDQwNhMBjE3t7+i2VGQeHZs2dk4cKF5NatW7RD\nW4l/PtzFxMSIkZERycjIaNRzLuiCzmKxyPXr14mTkxMJDQ0laWlpHXoc8HTJ/RsGdyguLkZiYiJO\nnTqFrl27YseOHRgzZgx+++23n/aZrq2txatXr/DmzRu4ublh4cKFkJaWxvz5879YpmmPZGVlQUFB\nAcePH/9hpq3y8nIcPHgQI0eOhIGBQbvOIiWIcLlcHD58GEwmExYWFgIXjZEQgvDwcISEhMDb25sm\nEGqlNk9ISICtrS369++PvXv3Yvr06U16f7aH5CyEEJpaGk2MFMcLsrOzkZKSgpiYGCQmJmLNmjVY\nunQpREREICIigkGDBkFYWBjCwsLo2bMnuFwuamtrUV9fj5qaGhQXF6O6uhpFRUX4+++/8ebNG6xf\nvx5r1qzBjBkz2n3ymK/x9u1byMnJwcfH54e5sP9JTRkXFwdnZ2cMHTqUvhX55IXk7OyMkpISHDp0\nSODEvLq6GocPH4aQkBDMzMxoHIRW4J8w3JWVlbC0tMTChQubLGo02xoVdJ7B4XCQmZmJiooKVFdX\n49mzZ3jz5s3nzEZVVVXo3Lkz+vbtiwEDBmDYsGGYPn06Jk2aBGFhYQwePJj6sv4fb968gby8PAIC\nAhoVi/7+/fswNzeHq6srZs+eTb9u21jM3dzckJOTA3t7e4FbOcnJyYGenh4UFRUhJydHx1ILj5W3\nb9/ixIkTePz4MaytrbFy5cpmr3RSQW9/gg66A9U+SElJITNnziSpqamNOj47O5tISUmRiIgI6trW\nRnC5XOLp6Ul0dXVJTU2NwNX9zp07ZP78+eTJkye0M1u4rfPy8giDwSBTpkwhFy5cILW1tT9drqDv\noVO+3EPvRL9r2gfTpk2Dv78/1NXV8fr16x8eP3r0aJw+fRppaWmwsrJCZWUlbcRWnm35+/vjyZMn\ncHR0FCibDyaTiZMnTyI0NBQxMTGYNWsW7dAW4sOHD/Dw8MDixYsxevRo3L9/H1JSUujRowdtHMoX\nUEFvR8yaNQteXl5QU1NDenr6D4/v2bMnLC0tISYm1uhzKLwR89DQUNy5cwfOzs4Ctef88eNHmJiY\ngMvlws3NTeATNPErpaWl8Pf3x4wZM9ClSxckJSVBRUUFIiIitHEoVNA7CnPnzoWbmxtUVVWRmZn5\n4wHQqRM2bdqEgwcPQktLC7du3UIbmVV0GCIjI3HlyhW4uroKVBriFy9eQFlZGRs3boSOjg71lGgB\nKisrcfbsWcyfPx8lJSVITk6GlpYW/XCiUEHvqIiJieH48eNQUVFBdnZ2o86ZOXMmIiIiEB4eDg8P\nDzCZTNqQLcCFCxcQGRkJDw8P9O/fX2BWFKKiomBpaQlPT8/vRiikNI/a2lpcuXIFEhISSElJQWxs\nLPbs2YMRI0bQxqFQQe/oLFiwAE5OTlBRUWl09rVBgwbB1dUVwsLC0NXVRVFREW1IHnL58mX4+/vj\n5MmTAuNi+Y9LWlJSEgIDAzF+/HjakTyExWLh9u3b2Lp1K65duwY/Pz8cOnQI48aNo41DaTJdaBO0\nXxYvXgw7OzuoqKggNDQUw4cP/+E5wsLC0NDQgKioKBQUFHDs2DFq9MQDYmNj4erqitDQUAwcOFAg\n6pyfn4/du3dj48aNkJeXF8iY8vwKl8vF48eP4ezsDGFhYTAYDEybNg1du3aljUNpPtTYv/27vMTF\nxZE1a9Y0OcVtVlYWkZSUJBEREQKdvrOtiYuLI0uWLCFFRUUCU+f4+Hgyb9488vjxYxoumMekpqYS\nTU1NsmHDBnLv3r02C/NL3dban9sa/eRu5wgJCWHFihVgsVjQ0NBAUFBQo2eIY8aMwenTp3H48GGk\npqZi79696NWrF23UJvBPEJ+oqCiBiMzHZrMRGBiI27dvIzo6GsOGDaOdyCPevXsHHx8f3Lx5E7a2\ntlixYoXARQWk8Dd0D72DiPratWuhpaUFTU1NlJWVNfrcPn36wNraGtOnT4eiomKjjewowKNHj6Cj\no4O//vqrUdsdbU1ZWRmMjY1RVlYGX19fKuY8orCwEE5OThAXF8ecOXNw584drFu3joo5hQo6pfmi\n/ueff0JJSQna2tpNCiTTuXNnyMrKYv/+/diyZQsSEhKoa9sPePr0KVRVVREVFSUQIYrT0tIgKSmJ\ndevWYc+ePdQljQeUlJTAy8sLYmJiGDhwIJKTkyErK9shEkdRqKBTWkHUpaWlsXHjRujp6aG6urpJ\n58+dOxdXrlyBl5cXvLy8wGazaaN+hdTUVMjJyeHixYsYO3YsX9eVEILLly9DR0cH/v7++OOPP2gH\n/iRVVVU4c+YMZs+eDSaTicePH0NDQwP9+vWjjUOhgk7hYYd36oRt27Zh5cqV2L17N2pqapp0/uDB\ng3Hq1Cmw2WwYGRmhuLiYNup/8erVK2zZsgXR0dEQFRXl67rW1NTAxcUFN2/exNmzZ/+T3IHSbGpr\naxETE4PFixcjPT0d8fHxMDIywpAhQ2jjUKigU1pO1FVUVDB37lyYmZmhrq6uSecLCwvDwMAAf/75\nJxQUFBoVO74jkJaWBnl5eZw7dw6///47X9e1qKgIWlpaEBERgZOTU4dMPcwr6uvrcevWLWzcuBE3\nb95EZGQkDh06hNGjR9PGoVBBp7Q8nTt3hqamJiZNmoR9+/ahvr6+yWVISEjA09MTJiYmiImJAZfL\n7bDtmZGRAQUFBQQFBWHatGl8XddHjx5BXl4eOjo60NDQoP7lzYTFYiExMRFKSkoICQnBkSNHcPz4\ncUyaNIk2DqVNaLN86BT+gMPhwMXFBR8+fICNjU2zLG/Lysrg5OQEERER6OnpCVSyEV6QmZkJBQUF\neHh4YO7cuXxbz4aGBpw/fx4XL16EnZ0dxowZQx+AZj4zL168wIkTJ1BeXg4TExPMmTNH4ILC0Hzo\n7YuJEyfSGTqdqXeGoaEhREREwGAwwGKxmlxG//79YW1tjV9//RXa2trIzc3tMO2XnZ0NVVVVuLi4\n8LWYV1RU4NChQ3j79i18fHyomDeTtLQ0mJmZwcjICPLy8ggJCcGCBQtohDcKX0AFnYIuXbrAzMwM\nXbt2haOjIxoaGppcRrdu3aCoqAh9fX0oKSnhwYMH7b7dcnNzoaamBnt7eyxcuJBv65meno6tW7di\nwYIFNDhQM8nJyYG1tTU2btyI5cuX4/Lly1i1apXAu/d16kQloL0gJCREl9wp/x8WiwVra2uIiIjA\n2Ni42XurhYWF2LNnD1avXg0VFZV2uUdbUFAAZWVlWFpaYvXq1Xxbz2vXruHw4cNwd3fHlClT6CBv\nIkVFRQgNDYWbmxscHR0hKSkpUClvv0dtbS169eoFaWnpDm3/0l7EPCoqigo65d8wmUzs378fI0eO\nhJ6eHjp37tyscmpqauDl5YX379/D0tJSYFKFNoYPHz5AVVUVRkZGkJCQ4Nt+9Pb2Rnp6OqytrQUm\nIQy/UFZWhrNnz8LCwgKOjo7YsmVLuxrDwH9iEDTHGJbCx8JOBZ3yv9TX18Pc3By///47du7c+VPL\ncjExMThy5Aj8/Pz43i+7MXz69AkaGhrQ1NSEpKQkX9axuLgYxsbGWLRoEXbs2EH3d5s4a42JiYGp\nqSn09fWhoqIiEDH4KRQq6JRvUldXh927d2PBggVQUVH5KVFPT0+Huro69u7dy7ci2NhZ286dO7Ft\n2zZs3ryZL+uYnJwMfX19HDlyBIsWLYKQkBAdzI2AxWIhLi4Oe/bswfr166Gtrc33Uf4oFCrolEZT\nU1MDfX19rF69GvLy8j8l6qWlpbC2tsaoUaOgo6ODHj16CFRblJeXQ09PD3/88Qfk5eX5rn5sNhsX\nL15EYGAgXF1dqRg1Eg6Hg/v372Pfvn2YOHEiTExMqK0BhQo6pX1SVVUFbW1tSElJQUZG5qdEncPh\nwN/fH7dv34azs7PALGVWVlbCyMgIK1euhLKyMt/Vr7q6Go6OjiCEYN++fdSKvRFwuVw8ffoU9vb2\n6NSpE8zNzfna7ZBCoYJO4dnsdOfOnVBUVMTGjRt/ehn33r17MDMzg7u7O+bMmcPX915dXQ1TU1OI\niYlBXV2d7+qXlZUFExMTyMvLQ0ZGptlGjB1JyNPS0uDu7o63b9/C0tISS5Ysoe1GoYJO6TiUlJRg\n+/bt2LlzJ9avX//Top6bmwtzc3NISUlh8+bNfOnaVlNTAwsLC0yfPh3bt2/nq/1oLpeLhIQEWFlZ\nwcnJie8/jNoaQghycnLg7++P2NhYHDhwAKtXr6ZpYilU0Ckdk48fP0JNTQ1GRkZYu3YtT2a/rq6u\nYLFY2L17N1+ll6yrq8OBAwcwYcIEaGpq8tUMjsViISgoCAkJCXBwcKBW2D+goKAAp0+fRkhICPbv\n34/169e3G19yCuW/oWGCKI1m8ODB8PPzg5OTE27cuPHT5fXu3RsWFhaYMWMGVFRUkJmZyRf3yWQy\nwWAwMGbMGOzYsYOvxLy4uBiGhoZgMpnw8vKiYv6DtvLy8sKiRYswePBg3LlzB3JyclTMKVTQKRQA\nGDZsGAIDA8FgMBAXF/fzA7BTJ8jIyMDOzg7a2to8+VD42dmvvb09Bg0ahJ07d/LVVsDz58+hpKSE\nrVu3QldXly4Xf4Py8nKcPn0aixYtApvNxsOHD6GmpkZTxFLaPXTJndIscnNzoaqqikOHDmH58uU8\nKfPDhw+wtrbGtGnToK6u3uqCxWaz4eDg8Dnfe3Myz7UEXC4XMTEx8PX1xbFjx9pFgJ6WoLq6Gjdv\n3oStrS2kpKSgrKyMUaNG0YahUEGnUH5EdnY2lJWV4eDggCVLlvBshuzj44MnT57g8OHDGDRoUKvc\nC4fDwZEjR8Bms2Fqaso3s9/a2lo4OzujqqoK+/fvp8vF3xgzt2/fhoODA+bNmwdNTU2MHz+eJh6h\nUEGnUJpCZmYmFBUV4eLiggULFvBsRpqQkAATExN4enq2uAU3l8vF8ePHUVZWBktLS74JepOTkwNT\nU1Ns2bIF0tLS6NatGx1w/9NvDx48gKOjI4YPHw49PT1MnjyZuqBRqKBTKM0lPT0dcnJy8PX15an4\nZmdnw9DQEEpKSti8eXOLzLi4XC48PDyQk5MDBoOBnj178kWbJiYmwsTEBMePH8f8+fPpIPsfnj17\nhmPHjqGqqgp79+7FnDlz2mVWPwqFCjql1Xn16hXk5eUREhKC6dOn86zcf6KgCQkJwdzcHL179+ZZ\n2YQQnDx5EqmpqXBycuKLCGscDgcBAQG4ceMGXFxcqBX7/5CRkQFvb28kJCTA1tYWy5Yto8lnKJT/\ng24yUXjC5MmTERoaCmVlZbx584Zn5fbu3RsMBgNTpkyBvLw8cnNzeSbmgYGBePjwIQ4fPswXYl5W\nVgZTU1OUlpYiICCAivl/9VVeXh5sbW2xadMmLFmyBHFxcVi9ejUVcwqFX2boeXl5SE9P/2oErnHj\nxmHMmDG0hwSMJ0+eQFNTExEREZgwYQJPy3769CkMDAxgb2+PJUuWNDtyGyEEYWFhiImJgY+PD/r0\n6dPm7Zaeng4dHR0YGhoKdEY6Xgt5cXExwsPD4eLiAgaDAUlJSYiIiNDGofyL9+/f4+XLl199J/z6\n668dxzOEtCHnzp0jAL76FxoaSiiCycOHD4mYmBjJysriedmFhYVEVVWV+Pn5kfr6+iafz+Vyydmz\nZ4mUlBSpqKho87bicDjk77//JkuXLiUvX76kg+f/KCsrI4GBgWTEiBHEw8ODfPz4kTYK5ZvExsZ+\nU0s8PDw6TDu06ZL792ZYNI+z4DJ//nwcO3YMSkpKyM/P52nZw4YNw8mTJ1FRUYE9e/agtLS0SefH\nxMQgMDAQAQEBbe4CVldXh6NHj+LatWuIjo7G5MmTO/zYqa6uxvnz5yEmJoaCggIkJiZCR0en1dwX\nKQK61Ey1BADdQ6e0EIsWLYK9vT2UlZVRUFDA07K7d+8OIyMjSEpKQklJCampqY0679KlS/Dw8EBw\ncHCbx43Pz8/Hrl27MHToUBw+fBj9+/fv0OOltrYWV69exdq1a/Ho0SNcvnwZ+/btw8iRI+nDRKE0\nEurnQWkxli1bhoMHD0JVVRWhoaE8NfISEhKCuLg4Ro4cCWNjY+jr60NCQuKbrm2xsbFwdXXFmTNn\n2jwE6OPHj7F3714cOnSIZwF5BBUWi4XExEQcPnwY48ePh4+PD6ZMmUJX6CgUKugUfmPlypVgs9mf\nRZ3XS6e///47wsLCYGNjg5SUFBgYGHzhS37r1i04ODggLCysTZduORwOwsPDcenSJQQGBnbo2SeX\ny8WTJ09w/PhxCAkJgcFgYMaMGdSXnEKhgk7hZ8TFxf8l6ryeIffr1w+Ojo44d+4cVFVV4ezs/DmG\n971793DgwAFERES0qRtYeXk5nJycICIiwjeW9W3Fy5cv4eHhgYyMDBw8eBBiYmI0Ch6FIuiCPmLE\nCBw6dOiLZVJCCE2q0M6QkJAAm82Guro6goKCeL6H3bVrV2zbtg0TJkyAsrIy7O3t0blzZ5iamiIy\nMhIjRoxos3vPzMyEgYEBNDU1ISkp2WFDk759+xa+vr64dOkSjh49ihUrVtCMcRSeMGTIEBw8ePCr\nKzy8dp/lZ9rUD50Qgm9dXkhIiO6jtTO4XC4uXLiA8PBw+Pn5tZiVeWFhIfbs2YN79+7h+vXrmDhx\nYpvdc2xsLBgMBk6ePIkpU6Z0yH4vKCiAv78/vL294e7ujnXr1vFFIB9K+3u/dHQtaXO3tU6dOn31\nj4p5OxxsnTpBWloa0tLS0NbWRnV1dYtcp6SkBG/fvoWCggI8PDxQUVHR6vfKYrHg6uqK8+fPIyoq\nqkOKeUlJCU6cOIFJkyZh5MiReP36NWRkZKiYU1rs/dLRtYS6rVFa/aGTk5PDunXrYGBggJqaGp6W\n//r1aygqKiIwMBD29vaQkJDAli1beBqO9kd8/PgRWlpa6NatG9zd3TFw4MAO1cfV1dU4ffo0Jk6c\nCBaLhXfv3kFdXZ2mfqVQqKBT2hudO3eGoqIiFixYADMzM9TW1vKk3IyMDMjKyiI0NBS///47hISE\nsH79eri4uGDnzp24cePGN5fleEVKSgrWr18PJSUlaGtrd6hY4/X19YiOjsbs2bPx+vVrPH36FMbG\nxjQoDIXSStBsa5Q2g8PhwNPTE1lZWbCzs/upPOSZmZmQlpZGQEDAV1O4lpWVYf/+/RgzZgwMDAzQ\nvXt3nt4Ll8vF2bNnERISAg8PD4wePbrD9GNDQwPu3r2LvXv3YsGCBdDR0cGkSZPoAKdQOpKgv3nz\nBs+ePftij4MQgqlTp2Lq1Km0h9o5bDYbx48fR1lZGQ4ePNgsq+esrCxs3rwZnp6eWLBgwXeFJzg4\nGHfv3oW9vT2GDx/Ok3uorKzE8ePHQQiBsbFxh1laZrPZSE5Ohr29Pfr164fdu3dj5syZdFBTWp2s\nrCwkJiZ+NbCUqKgoZs+e3TEagl+Ts5w+fZpmHOggNDQ0EFtbW2Jtbd3khCtZWVlk3rx55O7du40+\n586dO2TFihXkyThO3SMAACAASURBVJMnP133rKwsIi0tTcLDwwmHw+kQ/cVms0lKSgrR1NQkUlJS\n5P79+4TNZtOBTGkzrl+//k0t8fT0pMlZWmV5gFqyUwB06dIF5ubmYLPZcHV1BYvFatR5ubm5UFZW\nhpOTU5NCqC5btgxBQUGwtbVFeHg42Gx2s+p9+/ZtqKur48CBA5CTk/tm2Nl29PGP9PR07Nu3D0ZG\nRpCVlUVYWBgWLVrUYX3rKRR+ghrFUfhG1A8cOIDS0lJ4enqioaHhu8cXFBRg+/btOHToEFasWNHk\n640aNQpBQUHIzMzEwYMHUV5e3uhzmUwm/Pz8EB4ejtOnT2PWrFntvn9ycnJgY2MDRUVFLF68GNHR\n0RAXF6eBYSgUKugUypd07doVDAYDubm5OHXqFDgczlePKyoqwq5du7Bnzx6sXr262dfr3bs3zM3N\nISYmBg0NDWRkZPzwnE+fPn22zHdxceHZPjy/UlRUBFdXV6xZswaioqK4fv06Nm7ciN69e9MBS6FQ\nQadQvk23bt1gb2+P169fw9/f/ws3s48fP0JPTw86OjpYu3YtT1YGNm3ahEOHDkFLSws3b978ZvTC\n1NRUbNmyBZKSktDT02vXs9PS0lL4+/tj6dKlEBYWxoMHDyAvL9/maWcpFAoVdIoAISwsDCcnJyQn\nJ8PPz++zwH769Am6urpQUVHBhg0beHrN6dOnIyIiApGRkXB3d/9iH//ChQswNzeHr68v1qxZ027t\nP6qqqhAZGYkVK1agpKQE8fHx2LlzZ4cLjkOhCCJt7rb28uXLr74cf/vttw4b+5ryH2pqamBgYIBl\ny5Zh48aN0NXVhYyMDLZs2dJi16yvr0dQUBCSk5NhY2ODPn364Pjx46isrISFhUW7naEymUzcuHED\n1tbWWLduHTQ0NDB69Oh2b+hHaR9kZWUhOTn5q+N17NixHcLOpc0FnUJpzIzR0NAQDx48wMGDByEv\nL98q142Li4ODgwMIIVBVVYWiomK7FDcOh4N79+7BxsYGkyZNgp6eHiZOnEiFnEIRQGg+dApf07lz\nZ3Tp0gWZmZmtGka1a9euqKurQ11dHbp169bultgJIXj69CmcnJzA4XBw+PBhzJo1iwo5hUIFnULh\nPfX19dizZw/mzJkDe3t7aGpqonv37pCUlGyxa7JYLISFheH69euIjIxEnz594OjoiNevX8PU1FTg\nrbu5XC7S09Ph4eGBFy9ewMrKCkuXLqV+5BRKO4AuuVP4EiaTib1792LcuHHQ1dVFp06d8PHjR6ir\nq8PAwADr1q3j+TXLy8thZWWFYcOGwcjI6LMVOyEEYWFhiIyMhJubG0aNGiWQQp6bmwtfX1/Exsbi\n4MGDEBcX53lMewqF0kEFncvlfjNKV+fOnemsoYPCYrGwf/9+DB06FIaGhv8aB4WFhVBVVcXevXt/\nygf9f3nz5g0MDQ1hYGCADRs2fHWJPTk5GXp6ejhy5AgWL14sMMvwRUVFOH36NHx9fWFtbY0//viD\n+pFT2hVUS/hA0G/fvg1/f/8vGpsQAgUFBZ74GVMECzabDSsrKwwYMACGhobo0uXLXaG8vLzPIVeX\nL1/+U9fjcDi4efMmXFxccOTIkR96Vnz48AGWlpZYsGABVFRU0K1bN75ty5KSEpw/fx5WVlZgMBiQ\nlpbGL7/8QgcZpd2RlJQEV1fXr9rZbNy4EdLS0h2iHdp0D720tBQhISFf/R8V844p5nZ2dujTpw/0\n9fW/KuYA8Ouvv8LX1xcqKiqwt7dvUhz3/6a2thYnT55EdnY2QkJCGiV2Q4YMgbu7O3x9fbF3715Y\nWFjwXb7v8vJyXL16FXv37oWhoSGSk5MxbNgwOsAo7ZaKigqcPn36q/+bP39+h2kHatJK4Qs4HA6O\nHDkCADAyMvrhzHfMmDEICAiAiYkJHjx40OTrFRYWYteuXejXrx+OHj3apJlrjx49oKenh7Vr10JR\nURGvXr3iizasrq5GdHQ0li9fjrS0NMTHx8PY2JiKOYVCBZ1CaT0xd3FxQVVVFczMzBodUnX8+PEI\nDQ2Frq4ukpKSGn29hw8fQllZGbq6ulBXV2+WO5yQkBAkJCTg4eEBMzMzXLx4EW21e/VPUBhJSUnc\nu3cP586dg5WVFUaPHk0HF4XSgaBua5Q2F3NPT08UFBTAxsYGPXr0aNL5oqKiCAsLg5ycHPz9/TF7\n9uxvHstms3H27FlERUXB39+fJ4InKiqK4OBgODk54c2bN9DV1UWvXr1ape24XC4ePnyII0eOoF+/\nfnB1dcWUKVOoMSmFQmfoFErrwuVy4e/vj9evX4PBYDRbCH/77TeEh4dDTU0NKSkpXz3mn9CtGRkZ\nCAgI4OnsdcCAAbC1tcXgwYOhoaGBgoKCFm+758+fQ0NDA7a2tjAzM4OPjw+mT59OxZxCoYLeNnxv\niZK6x7d/MQ8NDUViYiKcnJx+2o1q0qRJCAsLg5KSEl6/fv2v/6Wnp0NWVhYrVqzAvn370LNnT57f\nT5cuXaCqqoo9e/Zg06ZNuH//fou0W1paGnR1daGlpQUlJSVERUVhwYIF3zQgpFA6AlRL/kObuq0V\nFxcjLy/vq/8bMWIEhgwZQkdqOxXzs2fPIjo6GidPnkSfPn14VnZKSgpUVVURGRkJUVFR3Lx5EzY2\nNnB3d8e0adNa5f4KCwthamoKcXFxqKio8GTWnJubCx8fH4SFhcHFxQVr166lQWEolP+jrKwM7969\n+2psiCFDhmDEiBFU0CmUlviSjo6ORnBwMAICAiAiIsLzazx58gRGRkZYt24dcnJy4ODg0Or+1/X1\n9XBxccGHDx9gZWXV7CxtxcXFOH36NBgMBtzc3CAlJUWDwlAolK9C99AprSrmly5dwqlTp+Dn59ci\nYg4AI0eOxOjRo+Hm5gYzM7M2CaYiLCyMvXv3YtWqVZCSkkJGRkaTzq+oqEBQUBAmTZqELl26ICMj\nA0pKSlTMKRQKFXRK24t5bGws3NzcEBwcjP79+7fINVJSUiArKwsNDQ2cPXsW6urqKCwsbLP7lpSU\nhLe3N3bt2oWrV6/+cD+vtrYW586dw5QpU5CXl4fnz59DT0+PRnijUCg/hC65U1qFuLg4MBgMREZG\nYvDgwTwvn81m49KlS/Dx8YG7uzvGjx8PALh58ybs7e1x5syZNrXJKCkpAYPBwPjx47Fjx44vDPPq\n6+tx584dWFhYYO3atdDU1Px8DxQKhUIFncIXxMfHw9LSEpGRkS0Stay6uhqHDx8GAJibm3+xLP33\n33/DxcUFoaGhGDhwYJu1A5vNRmBgIBISEmBvb4+hQ4eCxWLh0aNHcHBwwKhRo6Crq4upU6fSQUOh\nUKigU/iL+/fvw8zMDBERERg5ciTPy8/MzMSePXugqKgIKSmpb1qUx8TEwMfHB0FBQRgwYECbtklC\nQgKcnJygpKSEq1evor6+HoaGhpgzZw71I6dQKM2GOq9SWoyHDx9iz549CAsL47mYE0Jw7949MBgM\nODg4YO7cud89XlJSEiwWC9u3b0dgYGCLGeQ1pt79+/dHdXU1ZGVlYWxsDGtra2rsRml1uFwu8vPz\n0akTNaWiM3QK5Ts8fvwYurq6OHPmDM/3ghsaGuDv74+kpCTY29s3ek+eEILw8HBcuHABfn5+PPV/\nb+xqgp+fH+7duwdLS0vMnj0bnp6eaGhogJmZGfr27UsHDqXVqK2tRa9eveDs7Awul0sbRJCFXEgI\npqamVNApvOfZs2fYsWMHwsPDMWHCBJ6WXVxcDGtra0yePBlqampNjvpGCMGZM2dw5coV+Pj4tErc\n9fz8fJw5cwaBgYGwsbHB2rVrP39McDgcXLp0CV5eXnB3d4eoqCgdQJRWE3R1dXVERETQxmgHTJw4\nkbqtUXhLamoq1NXVERISwnMxf/HiBbZt24ZNmzZBS0urWSFchYSEIC8vjzVr1kBfXx81NTUt1hbF\nxcXw9vbG0qVLMWjQINy/fx+bN2/+18pA586dsWnTJhw9ehQ7d+7E9evX6SCitBp0Pte+oIJO4Rlv\n3ryBkpISgoOD8fvvv/P0pRMVFQUTExOcPHkS4uLiP7Xv16lTJ6iqqmL+/PkwNTVFbW0tT9uhsrIS\nISEhWLhwIZhMJhITE6GmpvZd3/upU6ciMjISf/31F9zd3cFkMumAolAoVNAprU9GRgbk5eUREBDA\n05jpdXV1sLOzw/379xEZGcmzWX+nTp2gqamJSZMm4cCBA6irq/vpMuvr6xEVFYVFixYhJycHcXFx\nMDAwwJAhQ74aY/p/GTRoENzd3SEkJIRdu3bh06dPdGBRKBQq6JTW4927d5CXl4eXl9d385E3lfz8\nfKipqWH06NFwdHTkuWV6586doaenh6FDh8LOzg719fXNKofNZiMuLg5r1qzB7du3cf78eezfvx+j\nRo1qlJD/N127doWuri40NDQgJSWFZ8+e0QFGoVCooFNantzcXKiqquLYsWNYsGABT8okhODRo0eQ\nlJSEkZERlJWVW8w/u3PnzjA2NoawsDCOHj3apKVuDoeDpKQkbN26Fb6+vjhx4gRcXFwwceLEn6qT\nkJAQli1bhqCgIFhaWiI6OhpsNpsONgqF8v13B7VypzSXgoICqKiowNLSEqtWreJJmWw2G0FBQYiN\njYWLi0uLRJb71nVtbGzQp08fGBgYoFu3bt8V8levXsHd3R35+fnYt28fFi1a1CL+vNXV1bC1tUX3\n7t1hZmbWKlb5lI5BbW0t1NTUEBkZSRujHUCt3CnNpqioCDt27ICZmRnPxLykpATm5uYoKytDQEBA\nq4k5AHTp0gWWlpYoKSnByZMn0dDQ8MUxXC4Xb9++hYWFBfT09CArK4uoqCgsWbKkxYJz9O7dG3Z2\ndpg8eTJUVVWRnZ1NBx+FQvkqVNApTebDhw/YtWsXDAwMsG7dOp6UmZaWBiUlJaxcuRKmpqbNckn7\nWbp164ZDhw4hNzcXAQEB/xL1/Px8ODo6QkZGBgsXLsSVK1cgLi7+3Zk8r+jcuTPk5ORgbm4ODQ0N\nPHjwgLobUSgUKuiUn6O4uBg7duzAzp07sX79+p8ujxCCq1evQl9fH8eOHcOff/7ZpvfXvXt3MBgM\nvHr1CsHBwSgqKsKJEycwb948jBw5Enfu3IG0tHSbLH3PmzcPp0+fhpeXFwICAsBiseiApFAoVNAp\nTaekpATa2trQ1NTkifDW1tbC1dUVcXFxOHPmDE9913+GHj16wNDQEMePH8fw4cNBCMHz58+hoqLS\nInncm8KwYcPg4+ODqqoqGBsbU9c2CoVCBZ3SNMrLy6Gvrw8FBQVISkr+dHmFhYXQ19dHv379YGdn\n16ZpTf+bqqoqREZGYt26ddDQ0ICamhoGDRqEQYMG8U1fCAsLQ19fH1JSUlBWVkZqaiodoBQKhWZb\no/yYyspKGBkZQUpKClJSUk32rf5fkpKSYGxsjMOHD2PRokV8cY/19fWIi4uDtbU1xMXFce3aNYwZ\nMwaVlZUwMDAAAGzbto1/vsQ7dcKaNWswatQo7N69G1paWvjjjz9o5iwKhQo6hfJ1ampqYGpqijVr\n1mDz5s0/JRhcLhdnzpxBVFQUzpw5g19//bXN74/L5eLevXuws7PD2LFjERgYiN9+++3zfYqIiMDF\nxQW6urro3LkzZGVl+ap/Jk6ciDNnzoDBYCAlJQXGxsbo0aMHHbgUChV0CuX/U1dXB3NzcyxYsADb\ntm37qeAuFRUVcHR0RK9evRAYGMgX+b+TkpJw5MgRsNls2NvbY+bMmV+9x/79+8PNzQ1aWlro0qUL\npKWl+aqfRERE4ODggHPnzkFdXR1Hjhzhi48lCoVCBZ3CBzCZTOzbtw9TpkyBiooKunRp/lDJysrC\n9u3boaOjg82bN//0kv3P8urVKzg7OyMzMxM2NjZYtGjRDz9WBg4cCE9PT2zfvh1dunThiR0BL+nW\nrRsUFBQwduxYbN26Fa6urpg/fz4dyBQKFXRKR4bFYuHgwYMYM2YMNDU1f0rMr127hoMHDyIgIACT\nJ09u0/vKzs6Gt7c3Ll26BCcnJ4iLi6Nr166NPn/w4MHw9fWFsrIyunXrxjMffF6ycOFCnD9/Hvr6\n+vjjjz+goqLSYmFzKRQKf0EtaCj/oqGhATY2NhgwYAB0dXWbLeb19fU4ceIEoqKicPny5TYTc0II\n3r9/j6NHj2L58uWYOXMmHj58iA0bNjRJzP9hyJAhCAgIgIODA+Li4viyD4cNG4bg4GB8+PAB5ubm\nKC8vpwObQqGCTulIsNlsODo6okuXLjA2Nm62mBcXF0NXVxdCQkJwc3NrM5e00tJSnDp1CnPmzEHf\nvn3x9OlTbNu27aeDwowYMQJBQUE4cOAA7t69y5d92bNnT+zduxdLly7F1q1bkZGRQQc4hUIFndJR\nxPzo0aOoq6uDhYVFs2avAPD8+XNs2bIFSkpK0NXVbXY5P0NlZSXCwsIwc+ZMVFdXIykpCTt37sSA\nAQN4do3Ro0cjJCQE5ubmSExM5Nt+3bRpE44fPw4NDQ1cv34dXC6XDnYKhQo6pb3C4XDg5uaGjx8/\nwsrKqlnxydlsNs6fPw8bGxv4+/tj5cqVrX4fNTU1uHTpEpYvX460tDTEx8fD2NgYw4cPb5HrjRs3\nDkFBQTAyMkJycjLf9u+UKVMQHR2Ny5cvw9XVtdl53ykUChV0Ch/D5XLh7e2NrKws2NnZoXv37s2a\nETs4OODZs2fw8/PD+PHjW/Ue6uvrcevWLcjIyCAuLg5hYWE4dOgQxowZ0+LXFhUVRUBAALS1tfH8\n+XO+7ecBAwbAyckJ/fv3h76+PgoKCujgp1DaGdTKvYOLuZ+fH1JTU3Hs2LFmBSTJzs6GiYkJ5OTk\nsHnz5la1qG5oaEBycjJcXV3Rq1cvHD58GNOmTWt1q+7ff/8dAQEBUFNTQ2BgIKZMmcKX/d2tWzeo\nqalh3LhxUFJSgrOzM2bPnk0fBAqFCjpFkCGEIDQ0FA8ePICbm1uz0pXevn0bNjY2OHr0KGbNmtWq\ndX/+/DlOnDiBqqoq7N69G7Nnz26VVKbfYsqUKfDz84OKigrCwsIwceJEvu37ZcuWISgoCCYmJpCR\nkcHWrVupaxuFr6moqACTycTgwYNpY3wHIUITK3dIMQ8PD8eVK1fg6emJPn36NOl8FouF4OBgJCQk\nwM7ODsOGDWu1uqelpcHHxwfPnj2DpaUlFi1aBGFhYb5p22fPnkFDQwORkZGYMGECX4+DyspKnDhx\nAnV1dTA1NYWIiAh9ODoQtbW1UFNTQ2RkJN/XVVdXF7du3cKjR494FmWSzWbj0qVLuHDhArp06QIJ\nCYnP4a0/ffoEDw8PZGVlYerUqdDU1OT752PixIl0D70jivn58+dx4cIFnDhxosliXlpaCgMDA1RW\nVsLLy6vVxDw3NxcMBgNbtmzB8uXLcfHiRaxatYqvxBwAZs6ciVOnTkFaWhpZWVl8PRb69u0Lc3Nz\nzJw5E4qKinxfX0rHJDs7G1evXkVVVRUeP37Ms3IfP34MbW1t6OvrQ1FREZqamkhMTASXy4W1tTUa\nGhpgamqKiooKODk5gc1mC8QLntJB4HK55OLFi2TTpk2ktLS0yec/f/6cLFq0iMTFxREOh9MqdX7/\n/j05fvw4+fXXX0loaCipqKgQiLZ++PAhmTlzJsnNzRWI+qamppIFCxaQ69ev0welg1BTU0NkZWX5\nvp7BwcHExsaG+Pr6ku3bt3/+nc1mk+DgYLJ8+XKiqalJjh07Rv766y9CCCG5ublEQ0ODzJ8/n2hp\naZHCwsIvyvXz8yOHDh0iXC6XEEKItLQ0uXr1Knn//j0BQLKzswkhhBQVFREApLi4mK/bSVRUlFBB\n70Bi/vfffxMJCYkmD0wul0uio6OJhIQEycjIaJX6lpWVET8/PzJixAji4eHB9w/T17h37x6ZN2/e\nV18m/MjHjx+JpqYmcXNzIywWiz407ZzS0lKydetWvq5jfX092bBhA7l//z559eoV6dOnD8nPzyeE\nEHLr1i2ydOlSkpKSQp48eUKWLFlC/Pz8SF1dHVm9ejWJiIggnz59In5+fkRSUpKw2ex/lZ2dnU1m\nzpxJGAwGMTMzI9OmTSNFRUXkwYMHZOXKlZ+PLy8vJ6KioiQzM5PvBZ0uuXeQZfY7d+7g8OHDCAwM\nbFLkttraWtjZ2SE+Ph4REREtvi9cW1uLc+fOYfbs2fjw4QMePXoEHR2dNos29zMsWrQITk5O2Lp1\nKz5+/Mj39R00aBA8PDwgJCQEHR0dFBcX04ennZKTkwN1dfUmb7m1Nunp6bhy5QpERETQtWtXDBgw\nAI8fPwYhBHfv3oW2tjamTZuGWbNmYcuWLSCEIDMzEzdv3sSoUaPw7t07jB8/HjExMXj37t0X24c1\nNTXo3r07REREUFRUhPfv34PFYkFYWPizoWinTp0wcOBA1NXV8X2/UkHvACQkJMDS0hKhoaEYMmRI\no88rKCiAmpoaRo0ahcOHD6Nv374tVkcWi4XY2FgsW7YMiYmJuH79OiwsLFosKExrICQkhOXLl2P/\n/v1QUFDAp0+f+L7OXbt2hZ6eHrZt24Y//vgDL168oA9QO/u4T0hIwObNm6Gvr4/a2lq+rm9UVBQm\nTZoELS0tqKmpoX///vD29gaXy0V2dva/Yk38Y0/zz173tWvXcPXqVdy5cweHDh36wi33r7/+wr59\n+2BmZoZ9+/bB1dUVzs7OEBERQXV1NRoaGj6X9+DBA/zyyy9837/Uba2d8+jRI+zevRvnzp1rtDgS\nQvDo0SOYmpri6NGjEBMTa7H6NTQ0ICkpCba2thg6dCgCAgIwbdq0dtP+QkJCWLt2LVgsFtTU1BAc\nHMzTELQtxerVqxEZGQldXV2oq6tDRkYGnTrR739BpqGhAb6+vkhISMCVK1fQp08fnDx5km/r++nT\nJwQFBSEmJga///47AKCwsBAjRoxAWloaZsyYgSdPnmDhwoVgMpl4+vQp5s+f/1m4DQwM0L9/f5SW\nliIoKOiL1Qgmk/k5lTMhBD179kSvXr0wbtw43L17F2lpaZg6dSoKCwsxbty4ZsXpoIJO4RmPHz+G\nrq4uIiIiMGrUqEbPlM+ePYuYmBicOXMGv/76a4vUjc1mIzU1Fc7OzmhoaICVlRXmz5/f5rnSW0rU\n//zzT7BYLOzcuROnTp1C//79+b7eY8aMwZkzZ3D06FG8efMGBgYGLbpKQ2k5iouLwWAwICoqilOn\nTkFYWJjvZ+fPnj3DkCFDMHbs2M+/DR8+HJs3b0Z8fDwkJSUhISGB+vp65Ofn4+rVq5g/fz4mTpwI\nCwsLWFhYYPny5bhw4QIWLVr0hduZlJQUlixZgrq6OtTX18PKygrXr19H7969ERAQgB07dmDXrl3w\n9fWFg4ODQLh1Uj/0dsrTp0+xY8eOJu17l5WVgcFgYOjQoTAwMGiRL1Iul4v09HR4enoiPT0de/fu\nxeLFi9skiUtrQwhBREQEYmJi4OnpKTB+31wuFxEREbhw4QKOHj3a6I9DCn/w6tUrGBkZwdjYGBIS\nEp9/53c/9Ly8PHTq1AkjRoz41+/v379HdXU1Ro4cicLCQlRWVqJv376IiIjAyJEjoaysDCaTiSdP\nniA9PR2TJ0/GjBkzvhp4KjMzE0lJSQCAuXPnfn5XcrlcPH/+HCkpKZg6dSpmzpzJ98GXJk6cSN3W\n2iNPnz4ls2fPJq9fv270OS9fviTr1q0jV65caTGXtKysLHLw4EEyZ84ccunSJVJTU9MhvQ2CgoKI\nurq6wLjg/UNycjJZvXo1iY+Ppw+ZAMDhcEh0dDRZv349SU9PF1i3tW9x/vx5oqamRgoLC8mrV6/I\n7NmzSUpKSoftb1FRUUJn6O2M1NTUz3u1jYkpzuVycePGDbi4uMDJyQlTp07leZ0KCgoQFhYGFxcX\nODs7Y/369R166ZYQAl9fXzx+/BjOzs48i3zVGhQUFODAgQNYtmwZ5OXlm5XMh9LyVFdXw8PDA5WV\nlTA1Nf3qFo8gRYr71j2GhITgwYMHGDZsGCQkJLBixYp2uW3X2Bk6FfR2xMuXL6GmpgZfX1/MmDHj\nh8fX19fDw8MDOTk5sLa25vm+bnFxMc6ePQtLS0scOXIEMjIyAmEQ1lrL2F5eXkhPT4eDg0OzYum3\nFXV1dfDy8kJeXh4OHDhA+5QPl6r37duH9evXQ1ZW9pvbWYIu6P/AYrHaNI8DPwk6NVttJ7x58waq\nqqo4efJko8S8oKAAmpqaGDRoEI4cOcJTMa+oqEBISAhmzpwJNpuNtLQ07Nixg774/4tOnTpBW1sb\nY8eOhZWVlUDlKO/RoweMjIywYcMGSEtLU9c2PuLBgwdQUFCAvr4+FBQUOoRtChXz/0+bWbkTQpCd\nnY3Kykrk5OTg4cOHePfuHZhMJlgsFhoaGiAkJIRu3bqha9eu6NWrF6ZMmQIxMTH88ssvGDhwYKsm\nBeFn3r59CxUVFXh5eTUqHWZSUhJMTU3h4OCAhQsX8myJqq6uDrGxsdi/fz9kZWWRmJiIkSNHdtgl\nsMaIuoGBAY4cOQIbGxtYWVkJzMupU6dOEBcXx5gxY6CnpwcdHR1s2rSJdmobweFwEBwcjNjYWERE\nRAh0/AZK82nVJfe8vDxkZWXh8uXLuHXrFoSFhbF27VpMnToV/fv3x/Dhw9GjRw/06NEDPXv2BJfL\nRU1NDerr61FdXY2CggKUlZXhwYMHuHXrFgYNGoT169dj6dKlEBUVRb9+/TpcB2ZnZ0NeXh7Hjh3D\nwoULf/jQh4eH4/z583BxceGZSxqbzUZ8fDysrKwwe/Zs6OnpYcKECVTIm/AydnR0BJvNxr59+wRu\nVlVeXg5bW1v88ssvMDQ0FKjtg/ZAWVkZ7OzsMHjwYOjp6TW6/dvLkjvlP7TKHnppaSmSkpJw6tQp\nCAkJQVlZGcOHD8ekSZPQs2dPCAkJNfnFz+VyAfwn8EB6ejrevXsHDw8PLF++HFJSUpg7d26HWIYp\nKCiAgoICH0+0BAAAIABJREFUbGxssGzZsu8eW1VVBVtbW/Ts2ROmpqbo1asXT+rw4MEDMBgMDBw4\nECYmJpgxYwYV8mZ+FNnY2KBHjx4wNTVFly6CFSKCw+EgJCQEFy9exIkTJ+gMsZV4+/Yt1NXVYWpq\n2uQVEiro7U/QW8xtLSsrixw7doyIiYmRyMjIFg9sz+FwyPPnz4mrqyuZOXMmiYiIIGVlZe3WRaGg\noICIi4uTGzdu/PDYt2/fkj/++IOcP3+eZy5pKSkpRElJiUhISJCEhITPGYsozYfFYhELCwty7Ngx\n0tDQIJD3kJSURObPn0/u379PO7SFiYmJIQsXLiRpaWnNOl/Q3dYoX7qt8VTQuVwuyczMJGZmZmTj\nxo0kPj6+TbI2lZaWkrNnz5I5c+YQb2/vZqUK5Wc+fPhA/vzzT3LlypUffuTcvn2biImJkadPn/Kk\nf9++fUtMTEyImJgYuXbtmsAKD79SX19PTE1NyYkTJwS2bfPy8si2bdtIaGgozdrWAtTW1hJnZ2di\nYGBASkpKml0OFXQq6N+kpKSEuLu7k23btpEnT57wzcC/evUqWbFiBYmJiSFMJlPgO+3Tp09ERkaG\nXLx48bvHMZlM4uLiQrZv304+ffr000Kel5dH7O3tyZQpU8i5c+c6ZFCY1hy3hoaG5NSpU1+kfBSk\ne7CzsyO7d+8m5eXltFN5RFFREVFSUiI+Pj4//cFHBZ0K+hew2WwSGxtLVq5cSS5fvsyXL6CysjLi\n4eFB5OXlmxQ9jd8oLS0lcnJy5K+//vruce/fvyeamprEy8uL1NfX//RqgLu7Oxk3bhwJDAxs19sY\n/ERVVRXR0dEhQUFBLRa5r6XhcDjk4sWLZP369c1eFqb8fx4/fkyWLFlC7t69y5PyqKBTQf9CKG1s\nbAiDwfjpWWBrkJGRQWRkZEhERITALQWWl5cTRUVFEhER8d3jnj9/TlatWtWovfUffTwEBgaSMWPG\nEA8PD/Lhwwf6xLQyFRUVZMeOHeTMmTMCbaOQmppK1q1bR27cuCGwHydtCYvFIuHh4WTbtm0kOzub\nZ+VSQW9/gt7swDIvXryAoqIi5s2bB0tLS4HIFTthwgQEBwejpKQEpqam+PDhg0BYL1ZXV8PQ0BAb\nNmyArKzsN62Mz507h/3798PX1xerV69u1rWqqqpw/vx5rFixAkVFRYiPj4eOjg4GDx5MzUhbmb59\n+8LJyQlXr17FuXPnQAQ0qOPUqVMRGhqKCxcuwMPDA3V1dbRzm+ASyGAw8PbtW/j4+GD06NG0USjf\npjlfAjdu3CDy8vIkIyNDYL9m7ty5Q2RkZMjbt2/5up41NTVkx44d3116raioINbW1sTa2rrZ+5U1\nNTXk2rVrZOXKlcTCwkKg+7a9UVxcTLZu3frDrRZ+h8lkklOnThEVFRVSWFhIO/YHvH37lkhKSvLU\nO4XO0OmS+2e4XC45d+4cUVdXbxeW42/evCHLli3j2ww9tbW1RFtbm/j7+3/TAObdu3dk69atJDIy\nsllGMkwmk9y5c4ds3LiR6OjokNTUVIE1xGrPfPjwgWzZsoVERUUJ9H1wuVxy7949smrVKvLw4UPa\nsd/g+vXrRFxcnKSmprboZIEKegcVdC6XS3x9fYmWlhaprKxsN42QmZlJlixZQhITE/mqXvX19cTQ\n0JB4e3t/U6jv3LlDli1bRpKTk5v1Yn306BFRUFAgysrKJCkpiboY8Tnv378nkpKS5NKlSwJ/L1lZ\nWWTLli0kLCyMfkD+z3Pv4eFBtLS0yPv371v0WlTQO7Cgnz59muzatYvU1ta2u4bIyckhYmJi5MWL\nF3xRHyaTSfbs2UNcXV2/KrINDQ3E29ubqKqqNuuhf/HiBdHW1iZr164l8fHx7cKdr6NQWFhI1q5d\n+8MYBIJAdXU1sbKyIubm5u1qktBciouLibq6OnFzc2uVZ5IKegcV9OjoaCInJ0cqKirabWO8fv2a\nzJo1q8Uj2v0IFotF9u/fT5ycnL76UH/69Ino6ekRFxeXJn9cvXv3juzbt49Mnz6dXLlyhdTV1dGn\nQADJz88nS5YsIdevXxf4e2loaCDR0dFk/fr1HdpuIzk5mcybN4/Ex8e3micAFfQOKOgJCQlEXFy8\nQ7gtJSYmksWLF5OPHz+2mZgzGAzCYDC+6j/++vVrsnDhwibPzgoLC4mjoyMZOXIkCQ8PJ9XV1XT0\nCzh5eXlk3rx55M6dO+3ifp49e0aWLl1Kbt261aHCCHM4HHL27FkiKSlJ3r1716rXpoLe/gT9u25r\nxcXF0NXVhaenZ4dwWxITE4ORkRFsbW3B4XBaPTmHi4sLamtrYWZmhu7du//r/xcuXMCuXbsQEhKC\n9evXN6rMsrKyz/nR/x97Zx5W0/b/8XezSpQm0qBSIkUoleqGLqKui2+UKUMXRZlCphBRSIYSudE1\nFIrMQ6avqVkhzSmV5rlTp1NnWL8/7s95bl9cpOFU+/U85w85Z++19157vddnrc8gIyODt2/fYs6c\nOW1WmIWi81BUVER4eDhWrVqFmJiYLn89I0aMwNWrV3H27Fn4+fmhubm52z/DhoYG7Nq1C4mJiQgJ\nCYGqqirVsSl+iq8KOpPJhIeHB/bs2YPBgwf3mBsyc+ZMiIuLIywsrEPFPCAgAEVFRdixY0cLMW9o\naMC+ffsQHR2N69evQ11d/bvi1i9evAhtbW3QaDS8ffsWS5cuhZSUFNXjuxHKysq4du0a/vjjDyQm\nJnb565GRkUFAQAD4+Pjg7OyM8vLybvvs8vPzMXfuXGhpaWHPnj3o3bs31aEp2i8O/eLFi2T79u09\n0gO1urqaGBoadkiaWBaLRQIDA8mKFSs+29P++PEjsbGxIcHBwd/1HBobG8nt27eJtrY22bx5M8nN\nzaXWoXoAmZmZREdHh2fDL1vDgwcPyC+//EKSk5O71RI8h8Mhz58/J6ampiQhIaFT20ItufeQPfS8\nvDxibGz8U5V8ujqvXr0i06ZNa9eKVywWiwQHB5MlS5Z85uAWHR1NjI2NvyucjsFgkMePHxNzc3Pi\n5OREUlNTqd7dw0hNTSUjRozoVs8+KyuLTJ48mVy7dq1bGBYMBoOcPn2a2Nvbk8LCwk5vDyXo3U/Q\n+QhpmU+Sw+Fg27ZtGD9+PH799dcevXqxbds2jB07FtbW1m1+bA6HgwsXLiAyMhInTpzg7ms3Nzfj\n4sWLePToEfbu3YuBAwfi37ZFEhMTceDAAUhISMDZ2Rl6enrg4+Ojlp56IMnJyVi8eDEuXLiAIUOG\ndItrotFo8Pb2hrCwMNavX99l/T8qKyuxY8cOqKurw8nJ6TMfmc6ATqdDXFwcfn5+4HA41AvUhfm0\nTfWZoCcmJsLX1xdBQUEQFhbu0TepuLgY5ubmiImJadP9Z0IILl++jIiICJw6dQoSEhIAgKqqKnh5\neUFOTg6Ojo5fHbw4HA5SUlLg5+eH0tJSbNy4EQYGBhAUFKR6dg8nKSkJy5cvR2ho6Hf5W3QFWCwW\nIiIicOXKFezduxdqampdbqK1bt06bNy4ERYWFjwz4Waz2cjPz6cMgO66h97U1ETmzZtH3rx506ol\nv9OnT5MzZ86QM2fOkNDQUPLu3btvxlQ2NTVxa2tnZWWR27dvt3rJgUajfXGJ/P79+63eDz937hwJ\nCAho06WR8PBwMmvWrBZ51zMyMsjUqVPJzZs3//WeZWZmkg0bNhBjY2MSGRlJxZJTfEZsbCwZO3Zs\nm1bm4gXi4uKIubk5efHiRZdoL5vNJrdu3SLW1tbUNhhFh9DCyz09PR2ioqIYPnz4D08MMjMzcezY\nMdDpdDQ1NaGwsBDDhw/HgwcP/vV3L1++RGBgIAghoNFoyM3NbdXEpLm5Ga6urvj48eNn/5eXl4fa\n2tpWHdfKygp+fn6oqalpkwnU9evXcfbsWZw6dQp9+/YFANy9excuLi44ePAgrKyswM/P/8Vr2LNn\nD6ZMmQIjIyNERkbi119/Ra9evahZKUULDAwMcOjQIdja2n7xfeiq6OvrIzQ0FKdOncKpU6fAZDJ5\ntq10Oh379+/H06dPce7cOQwdOpTqmBQdZ6GzWCzi5ubW6oIJ169fJ46Oji2cVy5dukRmzJhBCPk7\naUpKSgqJjo4mqamphMlkkvr6ehIQEECcnJxIXl4eqaysbJEtqqCggCQlJZH09HSu1VpUVETKyspI\ndnY2efXqFdcKycnJISYmJuTOnTufOZi9f/+elJeXt+q6OBwOOX78OLl+/fpPz55u375Npk6dym0L\nnU4nvr6+ZPXq1V9tX3FxMfHx8SH9+vUjFy5caHU1NYqex9OnT4mxsTEpLi7uVtfV0NBAjh49Slxc\nXFr9XrcneXl5xMbGhoSEhFD1ESg6x0IvKirCmzdvMGLEiDabLNTX10NUVBQAEBAQgF27diElJQVL\nly5FUFAQamtrERUVhbS0NLx9+xZv3rzBnj17AADPnj3Dr7/+iocPH2LVqlU4dOgQOBwO7t69i5kz\nZ2Lz5s24cuUKTE1NkZ2djbS0NBQWFiIyMhL19fUt2uHr64uEhIRWOxtYW1vj6NGjYDAYrb4XDx48\nwOHDh3H69GnIyMigtLQUDg4OEBMTw8GDByEjI9Pi+9XV1QgKCsLw4cMhJiaGzMxMzJ07l2vVU1B8\nCzMzM3h4eGD27NkoKyvrNtclJiaGlStXYurUqbC1tUVaWhrPtC0qKgrz58/Hhg0bYGtrCyEhIaoj\nUnQYXC+qFy9e4I8//vgp78s7d+5ASEgIAgICyMjIwJ07d5CQkAAWi4XGxkYcOnQIAwcOhJqaGk6c\nOIFFixbBzs4OcXFxsLS0xPPnz8HHxwc2m425c+fi1q1bGDlyJBYuXAhlZWXMmTMHACArK4uzZ89C\nWFgYhYWFeP/+PSZOnAgtLS2sXLkSsrKyn4nyzzBgwACMGTMG2dnZrdqO+O9//wtPT0+EhIRAXl4e\nsbGxcHJygr+/P8aOHduifXQ6HXfu3IGbmxuWLl2K5ORk9O/fn3JcoWgVEydORHNzM+zs7HD58mVI\nS0t3i+vi5+fH5MmToaamhhUrVmD16tWwtrbutPeExWLh/PnzuH//Pi5evAgFBQWq81F0jqCzWCz8\n+eefCAoK+qmDDRs2DFZWVhAUFMSMGTMQGBiIgQMHghCC6dOn4+LFi3j48CHevHmD8ePHtxDbf76I\n5eXlKCwshKenJ3r37g02m42mpibk5OSAn58fVlZW3L3jYcOGgU6ng4+Pj/tpawQEBDB+/HjcuHHj\nhwU9KioKO3fuxLlz59C/f3+EhITg6tWruHbtGpSUlLjfYzKZePjwIbZv347x48cjMjKyy3nzUvAm\nlpaWYLPZmD9/Pi5cuIB+/fp1m2vT0NDAlStX4OHhgZSUFKxdu7bD/Upqa2uxY8cOyMvL4/Tp09xV\nSQqKDp/oAkBOTg7k5OQwaNCgnzqYiooKzM3NMX78eJiamnJjqMvKyjB06FCoqakhICAAt2/fRmNj\n41ct6E/hVytWrMDmzZuxdetWPHz4EDo6OuBwOC1WETpqRm5kZIR79+6hrq7uu38THx8PV1dXnDlz\nBpKSktiyZQuysrJw9uxZrpiz2Wy8ePECkydPxpUrV3DmzBkcOHCAEnOKNsXKygoODg5wcHBoMwdP\nXkFSUhIHDhzAgAEDsHjxYhQVFXXYuTMzMzF79mz8+uuv2LRpEyXmFJ0v6AkJCViwYEG7naSyshKK\niorQ19eHtLQ0wsLCICAgADabDUFBQZSWloLFYnG/LyUlhQkTJqCmpgbq6upobGyEhYXFvyY/4OPj\ng6ioKCorK/E/ofVtQu/evWFubv7dXsNJSUlYuXIl/vrrL/Dx8WH27NnQ19eHu7s7xMTEwOFw8Pr1\nayxYsAC+vr7w8vLCqVOnoKOjQ/VKinZh5syZsLGxgaOjY6ujPngVAQEBLFq0CKtWrYKVlRUSEhLa\nZRz4BIfDwaNHj7B06VL4+Phg2rRpX4xOoaDoUEHncDiIiIj46Uo/vXr1gry8/Bf/T0NDA/b29hg3\nbhwsLS2hra0NDoeDO3fuQENDAzk5OTh58iSEhYUxYMAACAgIIDAwEOfOncOgQYOwevVqPH/+HDIy\nMhAXF4eYmBj32H379oWoqCiEhIQwY8YMzJ49+7MZuqys7E9nZuLn54e2tjZevHjxze8mJyfjjz/+\nwOnTp1FYWIjFixfD29sbs2bNAiEEGRkZWLduHVxdXeHg4IBLly7BwMCA2ienaFf4+Phga2uLyZMn\nY+3atT+02tRVGDduHG7duoX9+/cjODi4haHQVjQ1NeHQoUO4cuVKq7bhKCja7R1nMBhET08PiYmJ\n7b73VFZWBmlpaQgICHBjSIWEhMBkMsHPzw8BAYEW32ez2SgrK4OUlNR3t43BYEBERKRdxDEnJwe+\nvr44duzYV7+TkpKChQsX4tixY0hNTUVCQgI8PDwgJyeHDx8+4OzZs7hz5w62bt0KCwsLaomOosMh\nhODPP//EmzdvsG/fPm6mwu5EQ0MDjh8/jvLycri5ubWZ30BRURG2bNkCU1NTLFiwoMdn06TgMQs9\nNTUVhoaGHeJIIicnxxVtISEhbkjHJ8/4/0VAQAADBgz4obb16tWr3SxdGRkZJCQkoKqq6ov/n56e\nDnt7e2zfvh0hISFobm7G0aNHwWaz4ePjA2trawwZMgT379+HtbU1JeYUnWapOzg4YNiwYdi1a9dn\nYZ7dAXFxcbi6usLExAT29vbIysr66WMmJiZi/vz5WLJkCZYuXUqJOQXvCXplZSVMTU2pO/GdkwVN\nTc0vxqNnZWXBwcEB06ZNw59//ok5c+bAxsYGf/31F8zMzCApKYknT55gzpw5VCw5BU+I+ooVK6Co\nqIh9+/aBTqd3y2v87bff4O3tjVWrVuH+/fut2ldns9kIDQ2Fp6cngoODYWZmRnUgCt4U9JKSEmhq\nalJ34jsQFhbGsGHDUF1d3eLv79+/xx9//IHevXsjLy8P27ZtQ2lpKczNzdHQ0IBnz55h6dKlnyWP\noaDo1Jefnx/Ozs6QlJTEwYMHfypxEi8zbNgwnD9/Hnfv3oWfn1+LCJtvUVtbC3d3d+Tl5SE4OBjK\nyspUx6Hg3Xc6Li6uhZPZj0Kj0VBaWvpFD/RP1XyysrLw4cOHFg4qTCYTRUVFn32Ki4t5+ob169cP\nGRkZ3H/n5eVh2bJlyMnJgaysLExNTbFp0yakpKTg9u3bWLNmDQYMGED1NAqeREBAAGvXroWgoCAO\nHz6M5ubmbnmdsrKy8Pb2hqioKFxcXFBSUvLN32RnZ2Px4sUYO3YsNmzY0C19DSi6GZMmTWp1VSYO\nh0N8fHyIoqLiZ/miq6qqiKurKwFAzMzMCADi4uJCKisrCSGEfPjwgUhISBAALT5qamo/Vd0oJCSE\nLFiwgNjZ2ZEzZ858Vn3Nzc2NpKent/ocd+/eJceOHePmbJ40aRKRkJAg5ubmZNiwYWTdunUtcs9T\nUHQFmpubyY4dO8i+ffu+WLGwu8DhcMjz58+JsbExSUhI+Or3Hj16RMzNzVtVeZKCotNyudfU1KB3\n796tmgzU1tbCy8sLwN+51/+Ju7s7+Pj4UFlZicjISNTU1IAQguPHjwMAt7paWVkZGhoauJ937961\nenJy7949nDx5Ei4uLtiwYQMCAwMRGRkJAHjz5g18fHzg7e2NpqamVp9DSkoKDAYDpaWlcHJyQmRk\nJGg0GtTV1REaGgpvb28MGTKEikml6FIICQlhy5YtqK2txeHDh/8150NXho+PDyYmJggNDcWePXsQ\nEhLS4lqbm5tx7NgxXLx4EWFhYdDV1aU6B0XXWXKvr69vtYd7VFQUbG1tcejQIdy4cYO7B5eSkgI/\nPz+4urqiX79+EBERQd++fbFx40aMGjWqxV6dmJhYi09rPb8JIUhKSsLChQsxZswY6OnpYc6cOUhO\nTua2SUpK6qeTTYiLiyMhIQFr167F7du3YWlpiaioKJw4cQK6urrcLHcUFF0NYWFh7Ny5E8XFxTh6\n9Gi7JmbpbJSVlXHu3DlkZWVh+/btXOPC2dkZQkJCOHr0KOXzQtH1JqwaGhokIyPjh0O9mEwmli9f\njjlz5kBPTw/y8vJ4//491NTU8PLlS7i6uiIqKgp8fHwoLi5GRUUFd4Y8ePBglJSUQFVVFUFBQS2S\nvmhpaWH06NEtzvX69esvpqvU1dVtEV9aW1sLERER9OrVCzQaDfb29rCzs4ONjQ1X9Pn5+fHmzZtW\nz7zz8vIwaNAgSElJYceOHVi+fDlVk5yiW9HY2IhNmzZBS0sLTk5O3fpaCSEICwvDkSNHICgoiO3b\nt8PCwoLqBBRd00IH0KrlteLiYpw5cwaioqKoqqqCra0tnjx5AgCoqalpUfEsJiYGa9euhbOzM3R0\ndLjiDoArwJ8+X4pHz87ORkxMTItPdHT0Z6E2ffv2hbCwMKKiojBt2jRoamrC2tq6bWdA/z/xaWxs\nxJo1ayAqKgpzc3O4u7vj5s2bePbsGTIyMsBgMMDhcLq1lUPRPREVFYWXlxfevn2LP//8s2dYNlSW\nRoru0I+1tLRIXFzcD3twnj17Fl5eXhgwYAA4HA6YTCZYLBYeP36M9PR0jB49Gk1NTS2SL9TU1EBK\nSgoFBQVgsVhQVVVFfX09xMXF//VcbDb7i8IoICDQ4kVsbGyEl5cXnj59ir1798LIyKjF/7eFhZ6c\nnIyrV6/CwsICrq6uiImJga6uLtTU1DBnzhxkZmYiKSkJ2dnZEBERgZGREYyMjCAtLQ05OTmoqalB\nTEwMfHx8n7WfgoKXoNFoWL16NczNzbFw4cJud3319fXw8fFBc3Mz3Nzc0NjYiO3bt0NPTw+LFi2i\nVt4oup6FLioq+sNJJZqamrB9+3acOXMGjx49wpMnT3D9+nVkZ2cjIyMDqqqqGD16NG7cuAE2mw3g\n7yX6iIiIz47FZDI/+/wvu3fv5maW++cnPT29xfdu3LiB/Px8XLt2DWPHjgWbzeaev62oq6uDqKgo\nxo0bhzt37mDXrl0oKSkBjUaDh4cHFBQUcPjwYSQkJOD69etYtmwZVFRUkJWVBX9/f8ycORP6+vpY\nvHgxTp48iatXr+LFixcoKSlBY2Mjmpqauq1DEkXXQkJCAocOHUJkZCQuXrzYra4tPz8fCxYsgIaG\nBnbv3g0JCQnIycnh2LFjYDKZcHFxabGSSEHRFRDs06cPGhoafuhHb968gbi4OLS0tLh/k5aWhoOD\nAyIjI7Fp0yYcPHgQ8+bNQ0xMDLS1tREfH4/q6mqYmJi0ONYnJ5RPiIiIwMfHp0VsvLOzM5YvX/5Z\nO/7ptMJkMvHkyROUlpZix44dXIveyMgItra2bWYJV1dXc2fuUlJScHd3h4WFBXx8fDB58mQICQlh\n+fLlGDhwIObPnw99fX307t0b48aN4x6jvLwc1dXVoNPpSElJwc2bN5GXl4fc3FzIycnByMgISkpK\nkJWVhaamJmRkZCAgIPDVLQkKivZCUlISR44cgYuLC4SEhDBr1qwufT2EELx8+RKbNm3C0aNHP/PX\nERYWhrOzMx4/fgwbGxscOXKE8nSn6DLwLV++nCxbtgyjRo367h9VVFSARqN9VqGttLQUNBoNampq\n4OfnR3FxMVJSUlBUVAQtLS3u/rmsrCwIISgoKPii0Kqqqv6wtzibzUZubu5nS/NiYmLcuuzA3/WL\nFRQUWh2qFxgYCElJScyePbvF32tqanDq1CmUlZVh6dKlaGxsxPnz5/H48WOsWLECU6dOxYABA/71\numpra1FTUwMGg4Hs7GwkJyfj48ePyMjIQHNzM8aMGQN1dXXIyclBU1MTCgoKEBISgqioKJVXmqJd\nKSsrg5OTExYuXIjffvutS15DU1MTzp07h9jYWOzevRv9+/f/1+9nZ2dj48aNWLRoEaZNm0ZNpil4\nX9CDg4PJ4MGDW1iQFF/H29sblpaWX521x8fHY/fu3Vi8eDGsrKxQVlaGR48eISgoCCNGjMCcOXOg\nq6v7Qz4LdDoddXV1aGpqQkFBAVJSUvDx40fk5OQgNzcXGhoa0NLSgry8PDQ1NaGqqgphYeHPSs1S\nUPwMJSUlcHBwwMqVK2Fpadml2l5eXg5PT0+oq6vDwcHhu8Nja2trsX//fkhISGDlypVUtjgK3hb0\nBw8ekMLCQtjb21N34xs0NzdjxYoV2LNnDxQUFL76vbq6Opw/fx6vX7+Gm5sb1NTUUF9fj3fv3iE8\nPByxsbGwt7fH5MmToaSk9FPtqa+vR3NzM0pLS5GWlobCwkLk5+fj9evX6N27N7S1tdG/f39oaWlh\nyJAh6NWrF3r37k0ViKFoFZ/GCjc3ty4T3pWamoq1a9di3bp1mDRp0g9vv7HZbFy+fBnh4eHw9fWl\n8rlT8K6gJyYmkoCAAAQGBlJ34xvU1dXB2toaERER31VfOTk5Gbt27cLMmTMxa9YsiIiIgM1mo7i4\nGA8ePMDBgwcxadIk2NnZQVtb+5ve/j8yANHpdDCZTFRXVyM9PZ2bJz8+Ph41NTXcJfuhQ4dCR0cH\n4uLikJCQgIyMDOV5T/GvfPz4EXPnzsXu3bvxyy+/8Gw7CSG4efMmTp06hUOHDkFDQ+OnjpeYmAhX\nV1fs3LmTqrhGwZuCzmAwyJgxY/Dq1StqH/Yb5Obm4vDhwzhy5Mh3/4ZGo+Hy5ct4+PAhPDw8Wgwq\nDQ0NiI+PR3h4OJKTk+Hg4IAJEya02PNv6wGOwWCAxWKhvr4eWVlZKCoqQkVFBWJjY5GWlgYFBQUo\nKSlBW1sbenp6kJSURJ8+fSAvL09lwaPgkp+fDxsbGxw6dIgnt+saGhpw/PhxlJeXw83N7bsm4N9D\nUVERtmzZAlNTUyxYsIAaMyl4S9A5HA6ZP38+tm/f3sJr/XtgMBg4cuQItxyhgIAAtLW1MX78eEhJ\nSSE7Oxvnz5/HhAkTWsxo6+rq4Ofnh8GDB8PGxgapqal48uQJVq5c+VPWIZPJxLt376CtrQ1hYWGU\nlpbBmc0mAAAgAElEQVSiqKjos2Oqqqq2asn58uXLqK+vx5IlS374t+np6dixYwcsLS1hZ2fXIjse\ni8VCYWEh7t+/j/379+O3337DvHnzMGLEiA4TUSaTCTabjaamJmRnZ6OoqAjV1dWIj49HbGws+Pn5\noaqqCh0dHejr60NGRgZ9+vSBgoJCq9P1UnRt8vLyMH36dAQGBsLAwIBn2lVUVIQ1a9bA0tISCxYs\naPN3qKmpCceOHUNOTg48PT0hJSVFdQYK3oAQQi5fvkzu3bv3w5VdaDQaAUDOnj1L7t27R27fvk3+\n+OMPMnfuXMJisUhsbCwBQP7zn/+0+F1UVBQBQHbs2EHYbDZ5+fIlmTBhAuFwOD9Vae3evXtEX1+f\nVFdXE0IIiYyMJAYGBsTExISYmJiQMWPGEAAkOTm5Vcd3d3f/qUptdDqd/PXXX2T69OlfPA6HwyEN\nDQ3k4cOHxNbWlpiampLw8HBSVlbWadV7WCwWaW5uJo2NjSQ9PZ08evSIREREEHd3dzJhwgSioaFB\nJk6cSNatW0fCwsJIdHQ0effuHfcZUHRvcnJyyNChQ0liYiJPtOfFixdET0+PxMfH/9R48j3jwcOH\nD4mJiUmrxhMKivYAhBCSnJxM5s6d22pBz8vL4/4tLS2NACB0Op3ExsYSSUlJMnToUPL+/Xvud/bs\n2UPGjh3bQtAnTpzY6hewurqaW351woQJXDHhcDiEzWYTNptNOBwOCQwMJD4+Pq06R11dHTEyMmoT\noXr//j2ZNWsWCQwMJA0NDV8t85ienk58fX2JoqIicXd3J0lJSe06SP1oGUo2m01YLBbJy8sjUVFR\n5N69e+Tw4cNk1qxZRF1dnejq6pJFixaRP//8k/z3v/8lr169IiUlJdRb183IysoimpqanSpsLBaL\nnDlzhtja2pLCwsIOO29GRgaZNGkSuXXrFlUymaLTEQSAIUOGID8/Hx8+fMCgQYN+2MovLS2FkJAQ\naDQaHj58iDlz5nD3luTl5eHg4IDXr19DTU0N1dXVOHv2LJYuXQoajfavx/1axjQ+Pr4Wy+h9+/ZF\nfX09cnJyWqSo/Of3UlJS4O/vj5cvX7ZqJSM6OhpTp06FpKTkT6+KqKmpISQkBJcvX8aMGTPg6+uL\nYcOGfXaNQ4YMwZAhQ7BkyRJER0fDzc0NLBYL69atw7hx49CnT59Oc2D7571VVlbmev5OnjwZq1ev\nBvB37HJhYSFoNBpyc3Px8uVLvHr1CtXV1dDR0YGpqSmGDx8OCQkJDBw4EIqKiuDn5//s+VLwNoMH\nD8aNGzfw22+/ISIi4rO+3N7U1NTAw8MDsrKyOHPmTIembNXU1MTly5exY8cOvH37llvfgYKi05bc\nCSHk3Llz5OrVqz9soSsoKJCRI0cSQ0NDMnbsWCIjI0P8/PwIk8kksbGxZOTIkSQuLo677P7ixQuy\nbt06Eh4eTrZt2/avFrq/vz8RFhZu8QFA4uLivmr56urqfmZFczgcYm1tTR48eNDq2f+mTZvIu3fv\n2mXJ0t7enhw/fpzQaLRvfv/Nmzdk165dRFlZmfj4+JC0tDTCYrG61Cyyrq6OpKamktjYWBIREUE2\nbdpEJk+eTLS0tIiZmRnZvHkzCQ8PJw8ePCApKSmkoaGBNDc3d7nr7GkkJyeTYcOGkYyMjA47Z2Zm\nJpkwYQK5fv16p65eMZnMTlkhoKD4bMmdEELy8/OJpaUlYTAYP7zk/vHjxxYCpaenR54+fUpiY2PJ\niBEjSFNTExEXFydZWVlk9+7d5Pbt2+TmzZvfFPSamhpSUlLS4lNcXEyampp+SNDj4uLIiBEjCJPJ\nbNVN+vjxI5k4cSJpbGxst8EgNDSUWFtbk9evX3/XwFRWVkauXr1KLCwsiK2tLbl79+53TQh4mcbG\nRpKTk0Nev35NHj16RLy8vMi8efOIiYkJ0dfXJ46OjuT06dPk2rVrJDY2llRWVhI6nf7V/kDR8SQm\nJpIRI0a02GJrDz75zEycOJGkpqbyzPW/fPmSmJqakri4OJ7ZHqPoYUvuAKCgoIBRo0YhKSkJhoaG\nP2Tl/3NpXFVVFcrKyqDRaNylL2FhYezZswfXr1/H2bNnsWzZMkRHR3/zuMnJycjOzv7sXBMmTPih\nrYHTp0/Dzc2tVd6uhBDcuHEDq1evbrelPEFBQdja2sLQ0BD79u2DtrY2Fi5c+K/L+7KyspgxYwas\nrKyQkpKCS5cuYdWqVVi7di2mTp0KRUXFFjnyuwK9evVqkU54woQJAP6Oq/+UVri2thavX7/G48eP\nUVBQgIKCAqirq8PQ0BCysrJQUFCApqYmJCQkICgoiF69eoGfn59aiusg9PT0EBgYCFtbW4SFhUFF\nRaXNz0Gn0xEUFITs7GxcvHixRU2HzsbY2Bjnz5+Hq6srZsyYgf/85z9d7j2k6LpwFU5AQAB2dnbw\n8fGBvr7+D+UtTkpKQnFxMZhMJl69eoX4+HgcOXIEpaWl3O+YmJhAX18fq1evhpyc3Hcdl5+f/7N2\n8PPz/5Aw19bW4unTp1i7dm2rblBtbS0CAgLw4sWLdn8YgwYNgp+fH+7cuQN7e3ts27YNY8aM+df9\nZCEhIYwcORIjR47E6tWrubnj1dXVMWvWLOjr66NPnz5dupMKCAi0yMz3zxCpyspK0Gg00Ol0vHv3\nDs+ePUNoaCjS09MhJiYGAwMDDBw4EAoKCtDS0kK/fv0gJCQEMTExKq6+nTAwMICvry/mzp2LS5cu\nQVFRsc2OXVJSAjc3N4wbNw4HDx7kSbFUVlZGcHAwjh49iq1bt2Lr1q1UZkaKjhkrd+7cufPTP6Sk\npHD79m0MGjTom4ULgL/jp0VFRZGfn4+MjAxkZ2dDSEgI+/fvh6qqKurq6iAoKAhTU1P069cPfHx8\nsLa2hoqKCqqqqiAkJIThw4dzneOMjY1biJeSkhJGjBjx2edrAtXY2AgWiwUjIyOuU96nsqQzZsxo\nlaUWGhoKIyOjH161+Bnx0tLSgomJCU6ePIm3b99CW1v7uxxtevfuDR0dHcyaNQvKysq4desWXF1d\nISQkBFlZWUhISHQ7a1VMTAySkpKQlZWFtrY2zM3NYW1tjVmzZsHS0hI6OjrgcDjIzs7G8+fPERQU\nhFOnTiExMREpKSnIzc0Fk8kEHx8fmpqaICAgQAl9G4mampoaXFxcMGXKlDbJgR4fH48VK1Zgw4YN\n+P3333m6WIqQkBCMjY3BZDKxdetWjBw5ErKyslTHoGhX+AhpWZ4sOTkZnp6e+Ouvv1okP+mJFBUV\nYcqUKXjx4kWnWLksFgsPHz7E0aNHsXnzZpiYmPyw93dxcTEiIyMRHBwMbW1t2NnZ/XBxmO5EY2Mj\nGhoawGQykZeXh4yMDJSWliIzMxOpqalQVFTE4MGDMXDgQGhra2PQoEEQERFBnz592iw1b08iMjIS\n3t7eCAkJgby8fKvfg4iICFy5cgV79+6Fmppal7oHycnJWLduHTZu3AgLCwueieBgs9nIz8+nIkq6\ns6ATQrBnzx6MHj0aU6dO7dE3Z9OmTZg4cSImTZrUqe0oLi7G0aNHISwsDBcXF0hLS//wMRoaGvD2\n7VuEh4cjJiYGS5YsgYWFRbvscXZFmEwmGhsbwWQyUVxcjMzMTJSXlyM3Nxfx8fEAAA0NDSgqKmL4\n8OEYMmQIREVF0bdvXypT2De4e/cuDh8+jAsXLvzwfjeNRoO3tzeEhYWxfv36LjupqqysxI4dO6Cu\nrg4nJyeeMJbodDrExcXh5+f31RBhii4i5Hx8cHZ2/lzQPwmIra0trly5wlMOJx1JfHw8jh49iuDg\nYJ5Y2mOxWIiKioKnpyc2bdoEc3PzVi2fs1gslJaWIjIyEocOHYKpqSkWLVoEXV3dDo3f7Sp8SofL\nZrNRVVWFzMxMlJWVoaioCFFRUfj48SNUVFSgoqICHR0d6OrqQlxcHJKSkq22SLsj169fR1BQEIKD\ng787r3p2djZWrVoFR0dHWFlZdfl65E1NTQgJCcHTp0+xd+/ef63Y2FGCvmjRIly+fJnqoN0ATU3N\nLws6AFy7dg3x8fHw8PDo8i/Sj1JTU4NJkyYhNDQU6urqPDfTP3LkCOh0OjZu3PjdDoZforGxEXFx\ncTh//jzS0tLg5OQEU1PTnyrp2lMghIDJZILD4YBGoyE7OxulpaUoLy/Hy5cvkZSUhL59+0JNTQ16\nenoYNWoU+vTpAykpKSgoKPTIffqwsDBcunQJp06d+uaqxsOHD7Fnzx74+flBW1u72ywLE0Lw8uVL\nbNmyBb6+vhg9ejQl6BTtL+gsFgtubm6YOHEiLC0te9RAvXXrVowdOxbTp0/nWasxNjYWbm5u2LZt\nGywsLH7K2Y3NZqOgoAAPHjzA7t27YWNjwy0O09Mmc231fDgcDpqampCTk4OSkhLU1NQgJiYGUVFR\nqKurg5qaGkaPHg1DQ0P069cPUlJSUFJS6vZZxs6fP4/79+/Dz8/vi57fTU1NOHXqFJKTk7Fnz55u\n60iWn58PZ2dn2NraYs6cOZ3irEoJeg8S9E/W4NSpUxEaGtrlHFFay7Vr1xAVFQUvLy+e9wivqamB\nv78/t0Tk90QmfM9LHhUVhZMnT6KwsBAbNmyAqalpj916aevJIgCuQ15JSQloNBpevXqFFy9eID09\nHQMGDICBgQGMjY0xcOBA9O3bF8rKym2ScphXOH36NKKiouDr69vCObOiogIbN26Enp4eli9f3u1L\nkzY0NGD//v1gMpnYsmULevfuTQk6xU8JOr6VeSYuLo5YWVl1asWvjiIhIYFMmTKFVFRUdKl2R0VF\nERMTE3L37t1WZ8P7Eunp6eTgwYNEQUGBeHh4kLdv31LpV9sRDodD8vPzSVxcHHn8+DHx8/MjdnZ2\nRFtbm2hqapJ58+aRkydPkvv375OYmBhSVFTUovhQVyIgIICsXLmSm93w9evXxNTUlDx58qRHZVhj\ns9kkLCyMWFtbk5ycnA49d0NDA7GxsaFevG6ChoYG+VcL/RO3bt1CaGgoTpw40W3DnTIyMjBv3jyE\nh4e3qkBNZ1NXV4fjx4+juLgYGzZsaNNkHjU1NYiKioKfnx8AwMnJCWZmZpCQkKBCXjqIiooKFBcX\no6GhAdnZ2YiNjcW7d+9QXFwMdXV1mJqaYujQoVyLfuDAgeDn5/9iciZewc/PDx8+fICRkRGCgoJw\n9OhRDB48uEc+38TERKxYsQI+Pj4YN25ch6wOUhZ6D1ty/yehoaF49uwZfHx8ICYm1q1uRH5+Pmxs\nbHDmzJkOrxTV1sTFxcHNzQ1r167FlClT2jSTFofDQVpaGi5duoSzZ89yz6Gmpkalt+wkaDQaSkpK\n0NDQgIKCAsTHxyM9PR0fPnyAqKgoDA0NoaurC0lJSSgpKUFVVRWCgoIQFBTs9GdGo9EgKysLTU1N\n3L9/HwMGDOjxk7aO3HKgBL0HCzohBEFBQYiJicGBAwe6TextRkYGFi1ahCNHjrRIKdrVB/ng4GCk\npqZi48aNLfKjtxUlJSWIjo7G6dOnISEhgUWLFsHQ0LDLp5ntLjAYDJSXl6O+vh7l5eVITExERkYG\nCgoKUF5eDh0dHYwePRr9+vWDkpISNDQ00KtXLwgJCXVI+OKHDx+wYcMGzJgxA2VlZaDRaNiwYUOP\nD51sampCUFAQkpOTsXPnznYNfaQEvQcL+idRv3LlCkJCQnDs2DEMHDiwS9+AV69eYcWKFQgKCoKu\nrm63e8CJiYnYtWsX7OzsMHPmzHaZ8Tc3NyMtLQ2XL1/G1atX4eTkBCsrqy5ZHKYnwGazUVFRgYaG\nBtTW1uLNmzfIyspCYWEhMjIyMGDAAOjr60NWVhbKysrQ0tKCuLg4hIWF0bt375/eYiGEICoqCu7u\n7ti3bx8MDAzAYrFw8OBBcDgcrF+/vsdnqAT+Dtvbv38/Dh06hOHDh1OCTtH2gv6JyMhIeHp6ws/P\nDzo6Ol1yULt79y68vb1x+vRpaGhodNuHXF9fj4sXLyI6Ohpubm7teq3FxcV4+vQpgoODoaKiAjs7\nO4wcObJbeWh3VwghqKmpAZ1OB51OR0pKCrKzs1FSUoK3b9+CxWJh1KhRUFBQgLKyMrS1tSEpKclN\ni/s9cfXNzc04e/Ysnj9/Di8vrxZL7M3NzfD29kavXr2wZs0aajII4P3791i7di0WL16M6dOnt/m+\nOiXolKBzSUpKwpo1a7BixQrY2Nh0mUQZNTU18PX1RWlpKdzd3Ts9W1NHkZqaij179mDKlCmwsbFp\n13hnOp2OtLQ0XL16Fffv34e9vT1+++03Ks1sF54UNjY2oqmpCVlZWcjOzkZ5eTnevn2L/Px8aGpq\nQllZGSoqKhg+fDgGDBgAERERruADf+8P79y5E0OGDIGDg8MX+x+DwcDevXshLS2NlStXUkVy/n+8\n8vHxQa9eveDi4tKmTsmUoFOC3oLKykocOHAATU1NcHV15fkl+E+TkCVLlmDu3Lk9zgqg0+m4cuUK\nbt68iV27dmHo0KHtej4Oh4OysjI8fPgQvr6+MDQ0xNy5c3t0cZjuBIPBQFNTE5hMJnJzc/H+/XtU\nVlYiLS0Nr1+/hqSkJNTV1dG/f388fvwYJiYmsLe3h4yMzFdjrul0Ojw8PKCkpARHR0eqlj3+zltw\n9epVXLt2DV5eXm02MaYEnRL0z2CxWLhz5w62bNmCzZs3Y8aMGTznBV9SUoITJ07g2bNnOHz4MHR0\ndHp0uFVWVhY8PDxgYmKCBQsWdMjzotPpeP36Na5du4anT5/ijz/+wK+//kpZ7d1UgJqbm8FkMnHi\nxAk8efIE8+bNQ25uLl6+fIm6ujqoqqpy0+JqampCVFQU0tLS6NevHxgMBtzd3aGuro5ly5ZRoZH/\nz6tXr7BmzRrs27cPJiYmlKBTtL2gf6KsrAzBwcG4efMm9u7dCyMjo05fMqutrcWdO3ewfft27Nu3\nD9bW1lQBkn9YV9euXcOFCxewb9++dnO8+V/YbDZKSkoQGRkJLy8vWFhYYOHChdDT0+v2mcF6Eo2N\njfD19UVlZSXc3d3Rt29fcDgcMJlMEEJQXl6OnJwcVFZWoqCgAM+ePUNGRgb69+/PXcI/cuQIdu3a\nBSsrqx6zNfY9xsmGDRtgZmaGxYsX/9QYSwk6Jej/CiEEmZmZOHbsGFJTU7F161aYmpp2+EBdWVmJ\nO3fuwMPDAytXrsT8+fOp1KVf4cOHD3B3d4ehoSEWLlzYoeknGQwGYmNjce7cObx+/Rrr16/H+PHj\n2ySFLUXn8fHjR2zYsAFWVlaYPXv2N7e2CCHgcDgghKC2tpYr9GVlZfD390dVVRUkJSUxaNAgGBgY\nYPTo0ejTpw+kpaWhpKTU4/baGxsbERAQgLy8POzYseO7q9dRgk4JeqvJzs7GtWvXEBYWBldXV+jp\n6bVrFig2m423b9/i2bNnOH36NDZv3oxJkya1urP3JFgsFq5evQo/Pz8cOXIEenp6Hd6G9+/fc632\nOXPmYPbs2Rg1ahS1h9rFSEhIwMqVK+Hr6wtjY+OfPl5tbS2cnJxgYGCAX3/9FeXl5aiqqkJ0dDSi\noqJQUFAAdXV1GBoawsjICFJSUpCWloaKikq3S4D1v9y6dQt79+5FcHDw33m8KUGnBL29BP0TVVVV\nSEhIwJ9//on6+no4ODhAXV0dQ4cOhaCgYKsGbEIICCGor6/Hu3fvkJqaCn9/f0yePBnTp0/HqFGj\nqFjWVpCfnw9PT08MHToUS5cu7RTHtYaGBsTExODYsWOoqKjA+vXr8csvv0BKSoraS+Vh2Gw2zp07\nhxs3bsDPz69Nl8hramqwdOlS7kTvnzCZTOTn56O8vBw0Gg0JCQmIiYlBWloaxMTEYGRkBBMTE8jL\ny6Nfv35QVVXlVnnj4+Pr8n0qOzsbixcvhqur6w9Xh6QEnRL0n+Ljx49ITU3FnTt3EBsbi1GjRsHY\n2Bh9+/aFlJQU5OTkICoqClFRUYiJiYHD4aChoQEMBgMNDQ3c6lTFxcV4+PAhKioqYGFhgfHjx0Nb\nW/uL5RgpfgwOh4ObN2/i2LFj2LdvH8aMGdNpg15qaipu376NgIAA/PHHH7C2toaWlhYVzsRj1NTU\nYM+ePZCWlsbq1avbxTKurKyEvb09li5dihkzZnzXbwoLC1FeXo6GhgYkJycjPj4eaWlpqK6uhq6u\nLszMzKCsrAwpKSmoqqpCVlYWfHx8EBAQ6FIrQ9XV1fD09IScnBxWrVr13fefEnRK0NsMQgg+fPgA\nGo2G+vp6JCcnIzMzE5WVlairqwOdTgc/Pz/ExcUhKSkJOTk5aGtrY8iQIRAREYG0tHSPz/3cnhQU\nFGD//v1QVFTE8uXLOzU5zKcl1sDAQPDx8cHBwQGmpqbo06cPZbV3MllZWVi1ahWcnJx+2EL8UcrK\nyrBw4UK4uLhg6tSpPzU5KC8vB51OR0ZGBl69eoXs7Gzk5+dDXl4e48aNw+DBgyElJQU1NTUoKCiA\nn58fQkJCPDuZZLPZOHv2LCIjI+Hj4/NdKySUoFOCTtGDYLPZuHfvHgICArB161aMHTu2Uy0XNpuN\ntLQ0hIeH4/Tp01i7di2srKygoqJCech3wkrOo0ePsGfPHvj7+3dYlERJSQkWLFiATZs2wcLCok2P\nTaPRUFFRgcbGRuTn5yMpKQnv379Hbm4umpubMWbMGGhra3Mt+k+FboSFhXlmiy86OhobN26Ej4/P\nN2tTUIJOCTpFD6SoqAi+vr7o27cvHB0dIS0t3eltKisrw7Nnz3D+/HlISUnBxsYGxsbGVJrZDuCT\nl3VBQQG2b9/e4Y6nhYWFmDdvHjw8PGBmZtbu52MwGKiqqgKDwUBJSQnevHmD3Nxc5Ofn48OHD9DU\n1MSIESMgIyMDNTU1DB48GCIiIhAREYG4uHiHP5+CggJs2bIFlpaWsLGx+WqUASXolKBT9GBr/dmz\nZzhw4AA2btwIU1NTnqizzWAwkJWVhfDwcJw7dw5r167F1KlToaKiQu21t5OYbt++HWZmZrCzs+s0\nyzQ/Px/z5s3D/v37YWRk1CltYLFYqKmpAYPBQHV1Nd69e4cPHz6gsLAQycnJ6N27N/T09CAnJwc1\nNTUMGzYMYmJiEBUVbfftovr6evj7+6Ourg6urq5frI5JCTol6BQ9nNLSUvj5+XHT/crJyfFU2x4/\nfoyzZ89CSUkJtra2GDVqFGW1txGJiYnYuHEjduzYAVNT005vT25uLubOnYtjx45hzJgxPHOfCCGg\n0WhcZ960tDR8+PABZWVlePXqFWpqaqCtrY2BAwdCTU0Nurq66Nu3L0RFRdGvX782myhzOBzcunUL\nJ06cwJEjRz4rzEQJOiXoFBRgs9mIiYmBl5cXnJ2dMXHiRJ6w1v85UKWnpyMiIgI3b97E8uXLMXny\nZKipqVEPr5XCcOnSJURERODgwYNQVlbmmba9f/8etra2OHXqFEaOHMnz95JOp6O5uRkMBgOZmZnI\ny8tDVVUVkpKSkJGRgYEDB0JJSQmDBw/GyJEjIScnBzExMcjIyLR6NSQ1NRVr1qzBunXrMGXKlC4j\n6BwOB6WlpeBwOAD+DjOUlJTkevEzGAxUVFRAUlKyRUIsDoeDkpISCAsLQ0ZGBg0NDaivr2/X2vKU\noFN0eaqrq+Hv74+ysjK4ubnxXHrOfxaHOXbsGIYPH44lS5ZAT0+v2ycdaStqa2tx8OBBiIiIwMXF\nBX369OG5NmZmZmLWrFkIDQ3tMOe8tuZT7vtPhW7y8vJQU1ODt2/fIi4uDgICAlBRUYGmpiZGjx4N\nRUVFiIuLQ05O7ruyO5aXl8PDwwMaGhpYtmwZevXqxfOCXl1djXnz5uHdu3cA/vYNsLCwwPr16zFl\nyhS8evUKY8aMweHDh7F69Wru73JycjB9+nRMmTIFBw4cwMOHDxEUFITQ0NAvnqeiogKurq4ICgri\nGiYNDQ04fvw4Xrx4gb59+2LNmjUYNWoUzws6CAXFT8BisUhcXByZNGkSuXnzJmEymTzZTgaDQV6+\nfEmcnZ3JmDFjSFBQEMnNzaUe4L+Qm5tLpk6dSsLCwgibzebptqakpBAtLS2SlpbW7d4vBoNBGhoa\nSGZmJnnw4AEJCwsjHh4eZNKkSWT48OHE0tKSrFu3joSGhpL4+HiSkpJCKioqPjtWc3MzOX78OJk/\nfz4pLS0ldDqd2NjY8Oy1V1ZWEm1tbZKSkkIYDAZpbGwkERERZPjw4YTBYJDY2FgCgEyaNInU1tZy\nf3fhwgUCgKxatYoQQsj9+/fJ9OnTPzs+nU4nUVFRxNHRkQAgLBaLEEIIh8Mhx44dI6tWrSJ5eXnk\n1q1bpF+/fqS6upqn+4qGhgahvIYofgoBAQHo6+sjLCwMgYGBuHHjBtzd3aGoqMhT7RQREYGxsTEM\nDQ1RWFiIR48eYeLEiZgyZQoWLlyIUaNG9bhyuv/Go0ePsHnzZpw+fbpLWL3Dhg3DpUuX8Ntvv+Hu\n3btQV1fvNu/XJ6tRQ0ODuw/O4XC4S9GlpaVciz4uLg7Pnj1DUlISJCQkMHz4cBgaGkJbWxvi4uKw\ntrbGiBEj8Pvvv2P37t087zhKCOFGDACAqqoq1z8BAEaPHg0RERHk5eVBR0cHHA4HV65cgbOzM761\n+NzU1ISsrKzPxqqmpiY4OzsjKysLysrKGDBgAK5fv94lso9Sgk7RJvTp0weurq5ITEzEkiVL4Ojo\nCGtra54bMPj5+aGkpIRFixbBzs4OsbGx8PPzQ0pKCtzc3GBmZtaji8MwmUycPHkSb968we3btyEr\nK9tl2q6rq4uLFy/C0tISkZGRGDRoULd9Tvz8/NycEAMHDsTAgQO5/+fo6Mhdsv4k9Pn5+Xjx4gVi\nYmJQU1MDMTExTJkyBfb29jw/roSEhEBeXh6lpaW4dOkSdu/ezc0KqqWlBRMTE0RGRkJHRwe5ubl4\n+/Yt7O3t8eDBg389tqSkJBYuXIi0tDRs3bqV+/f8/HwoKyvj4sWLOHPmDJSUlLBv3z6IioryfpA7\nZOIAACAASURBVL+gpIiiLRk1ahSuXr2K3NxcODo6Ii8vj2fbKiIiAjMzM5w7dw5hYWEoKyuDoaEh\ntm3bhsTERLBYrB717MrLy7Fy5UoQQnD8+PEuJeb/7H/nzp3DtGnTUFBQ0CPfwU856vv16wc9PT2M\nHz8e8+bNQ0BAABITExEaGgo1NbUu4STKYrGgrq4OLS0tGBoawsrKCj4+PigpKQHwt++BhYUFt1bI\ny5cv4erq+lM+MvX19dysgU+fPsWGDRtgbGyM8vJyStApeh69e/fGunXr4OTkBAcHB4SHh4PJZPJ0\nm9XV1bFq1SokJyfD3Nwc+/btg7m5Oa5du4aamhru8mZ35d27d/jtt98we/ZsODs7d+nth7Fjx+LE\niROYPn06CgsLqRcSf0emJCQkwNHREZs3b4aTkxNevHiB2tpanm43nU6HgYEBzMzM8Ouvv8LLywsS\nEhJ49+4d+Pj4wOFw0L9/f+jp6SElJQW3b9+GkZHRT2W0lJCQwIABA2BlZQVFRUWYmppCX1+/S0wQ\nKUGnaDf09PRw7do1VFRUYMWKFcjJyfnmvlZnIyEhAQsLC4SFhSEoKAgpKSkYM2YMDh48iHfv3qG5\nubnbDfRXrlyBm5sbQkJC2jydamdhamqKgwcPYs6cOSguLu6x72BTUxOePXsGW1tb7N27F3Z2drh3\n7x4sLS0hLi7O87UQ+Pj4wGKxwGKxwGQyUVFRgVevXrXIwCcmJoapU6fC0dERHz9+hKam5k+NMwoK\nCiguLkZVVRX3HsbHx/NUzo2vQe2hU7Qr4uLiWLFiBUxNTbFmzRrY2NjAxsYGvXr14vm2DxkyBFu3\nbsWyZcsQHx+PnTt3gs1mY8mSJTA1Ne3yCWvq6urg5+eHxsZGXLhwodtVK5wwYQLYbDYWLFiA8+fP\n9yjfiPr6ejx//hwHDx6EqqoqNm7ciFGjRvFUvojvFfQdO3ZARkYGDAYDERERcHV1hZ6eHt68eQMG\ngwE2mw0zMzMsWLAAAQEB6NWrFzgcDnfyzeFwQKfTv3qO/xV/MTExXLp0CQ4ODlixYgX++9//wtPT\ns0v0HyoOnaLDaGhoQFhYGCIjI+Hu7g4tLa0u1X4mk4nMzEyEh4cjJCQEy5cvh5WVFQYNGtTlisPk\n5+dj/fr1mDlzJmbPnt3lBvof4VMJ3qCgoG6fXKSqqgqPHj2Cr68vTExMsHDhQgwbNuyLS9C8HofO\nZDJRUFDQYrtLSEgIioqKEBAQQF1dHaqqqqCiogI2m438/HxISkqiX79+qK6uBo1Gg7KyMqqrq1FT\nUwNVVdWvjkvFxcVQV1fnrlgQQpCamorU1FRuNj9e34aiEstQdAoZGRnw9PTEL7/8Altb204pYPGz\nlJaWIioqCsHBwejbty/mz5+PMWPGdHihktbw7Nkz7NixAz4+PjyfLKOtuHbtGs6ePYuTJ092SWe/\nb1FSUoJbt27h8OHDmD9/PmbPnv1Npzcq9Wv3QlNTk9pDp+h4hgwZgpMnT0JMTAz29vbcTFBdCXl5\necyYMQMXL17E+vXrER0djfHjx8PHxwc5OTk86UTX3NyM4OBgnDt3DufPn+8xYg4Av//+O+zs7LB6\n9Wru3mh3IC8vDwcOHMDIkSPBZDIRGRkJNzc3Ks1xD4Wy0Ck6ldzcXHh6emLUqFFYsGABJCQkuuR1\nEEJQUVGBx48f48SJExg0aBDs7e2hp6fHE3vTVVVV8PDwgIqKChwdHbuED0N7EBISgsjISBw+fLhL\n+0BkZGTg/Pnz+Ouvv7B3715YWlr+cFljykKnLHQKijZFVVUV/v7+kJeXx6xZs5CUlNQ1Z8Z8fJCV\nlcWcOXNw584drFy5Eo8ePcIvv/wCf39/5OTkdFrbUlJSMH36dEybNg2rV6/usWIOAHZ2djA3N8em\nTZtAo9G63KTx9evXcHZ2xuLFi6Gvr4+UlBTMnz//h8WcgrLQKSjalYKCAnh7e0NdXR1Lly7lyUIg\nPwKbzUZ5eTmePHmCffv2QV9fH4sXL8bo0aM7LOvU9evX4e/vD39//8/KZ/ZUCCH4888/8ebNG+zf\nv5/nC/WwWCzExsbC398f9fX1WL16NYyNjX+6D1EWOmWhU1C0G0pKSjhy5AhUVVUxZcoUxMTEdOnr\nERAQQP/+/WFnZ4f4+HgsWbIEFy9ehIGBAf766y98/Pix3c5Np9Ph5eWF58+f4/Lly5SY/89qioOD\nA4YOHYpt27Zx84LzGk1NTXjw4AEmT56MgIAAuLi44MqVK5g4cWKXSENK0fFQgk7BcyL4+++/Izw8\nHKGhofD29ub5bFbfg4iICMaNGwc/Pz/cunULbDYbEydOxKpVqxAbGws2m91m5youLsbChQshLy8P\nb2/vLh8v316i7uTkBGVlZezcuZOnRL2hoQHXr1+HgYEBrly5gkOHDuHcuXMwNDSkCghRUIJO0fVQ\nUFDA4cOHoaOjA0tLS0RHR3eba1NRUcGSJUvw+vVrzJ49G76+vhg7diwuX76M8vLyn8pyFR0djZkz\nZ2Ljxo1YvHhxt44vbwtRd3Z2hrS0NDw9PTs1CyAhBNXV1QgNDYWuri5iY2Nx+fJlnDhxAiNGjOD5\njG4UlKBTUHxzwJ06dSoiIiJw9epVeHh4dKuQI1FRUZiZmeHixYsICQlBSUkJTE1N4e7ujsTExB/K\nf9/c3IyQkBAcOXIEly9fhoGBAdWBvgMBAQGsXbsWoqKi8Pb27vCaA4QQFBcXIzAwECNGjOCW9t27\ndy+GDBlCPSAKStApuhfy8vLYv38/DA0NMXv2bDx9+rRNl6h5AU1NTbi4uCA2Nha//PILvLy8YGVl\nhYiICFRVVf2r1V5bW4vNmzcjNzeXW+6R4vsRFBSEq6srCCHw9fXtkCp7HA4HHz58wMGDB/HLL7+A\nn58fsbGxcHV17dZlXyna2QiivNwpuhKlpaU4evQoBAUFsWrVqm6Z9euT5Zaeno4bN24gMDAQy5Yt\ng7W1NdTV1SEiIsL9XmZmJtauXYsVK1Zg2rRpP1VlqqfDYDDg6ekJaWlpODs7t8t2BYvFQnZ2Ns6f\nP4/IyEi4urpi4sSJnRJ2Rnm5dy+o1K8UXRIOh4Pnz5/D29sbrq6uMDMzg6Bg960zVFFRgaioKJw/\nfx78/Pyws7ODiYkJkpKS4OvrCy8vL+jo6FAdo41EbteuXVBWVoajo2ObTZCampqQkpKC4OBgZGRk\nwMXFBaampp0amkkJOiXoFBQ8Q2VlJfz9/VFTUwNXV1coKCh06+ttampCTk4OgoKC4OPjAwB4+vQp\njI2Nu/WEpqOh0WjYvn07hg0bBgcHh58S9YaGBrx69QqBgYHgcDhYvnw59PX1eSL2nRJ0StApKHjO\nWo/9P/bePC6n9H/8fxayRbJnJ4qSwijK0hR5k4ksY6JMZF/KvjTZtyyFUTPZB9mXQdmyT0yhEJVW\nypLSSmm9u8/3j/dv+r3nM8xg0H3X9Xw85o/Rua9zzrWc1/V6Xa/l5k1WrVrFxIkT6du3b5kWbsnJ\nySxevBgTExNq1aqFr68vNWrUwM7ODmNjY6UoDqMMvHr1Cjc3Nzp27Mjo0aM/2Mv89evXBAYG4u3t\nTatWrXB0dMTAwOBPxyVCoAuEQBcI3vEB3r59O3FxccyfP5/mzZuXuXe8ffs28+bNY+XKlXTt2hUV\nFRXy8/OJiYnh5MmT7N+/n/Hjx2NtbY2Ojo6YFJ9gTv1xpOPg4PBev0lPT+fChQt4enrSt29fRo4c\niba2tkLGjwuBLgS6QKDQ2vq9e/dYvHgxTk5ODBgwoExo68XFxRw9epQjR46wfv36t3pBy+VyMjIy\nuHLlCt7e3jRt2pSxY8fSqVMnpS14owhkZmbi7OxM//79sbOze+d1L1684NSpU6xdu5aJEycybNgw\nmjZtqtB5AIRAL3sCXbjECsoMqqqqdOrUiQMHDvD06VPGjRtHQkKCUr9TTk4OP/zwA5GRkezevfud\nIU2qqqrUrVuXYcOGERAQwLRp0zhz5gxmZmb4+PgQHx8vJshHoKmpyaZNmzh69ChHjx79y98TEhLw\n8PCgc+fOSJLEjRs3SkLPRFIfwRf/BoouEJQ11NXVmTZtGjNnzmTq1KkcPHjwi8QWf2ri4+MZPnw4\n3bp1Y+HChVSvXv29fqempoaxsTGrV68mICCAmjVrMmDAABwdHblx4wYFBQViknwAtWvXZuvWrezc\nuZNTp04BEBUVxcKFC7GyskJLS4vw8HAmTJhAw4YNRVY3QakhTO6CMk1ubi6+vr4EBgayZMkStLW1\nFf6ZJUkiMDAQV1dXvLy8MDIy+tdtFhUVcevWLXbv3s3vv//O3LlzsbCwoEmTJmKSvCepqalYWVnR\nuHFjXrx4weLFi7G0tHzvjZYirg1hci876OjoIGJdBGWaatWqMX78eLp168bUqVOxs7Pju+++Q01N\nTSGft6ioiC1bthASEsKJEyeoW7fuJ2m3UqVKmJmZYWZmRmJiIgEBAVhYWGBjY8O3335Lx44dReGP\ndyCTyQgJCcHT05N69eqRkpLC6tWrsbKyEp0jUCiEyV1QLjAwMOD48eMUFRXh5OREbGwsimacSk1N\nZdq0aUiSxJYtWz6ZMP+/NG/enHHjxnH37l0GDBjA5s2b6datG0eOHCE1NRW5XC4mDP/NHHf58mVs\nbGzw8PDAxcWFs2fP4ufnh6urK9evXxedJFAoStXknpCQQERExF/OnCRJQldXl9atW4sREnxyoqOj\nmTdvHjY2NtjZ2SlEbenw8HCmTZuGq6srffr0+eL3j4uLw8/Pj+3btzN8+HBsbGxo166dQsVNfymy\ns7O5cuUK69evx8DAACcnJzp27Pin79Tz58/55ptv+PnnnzExMVHK9xQm97KFjo4OSKXI0aNHJeCt\n//n6+koCweciNzdX8vX1lYYNGyaFh4dLcrm8VJ5DJpNJJ06ckPr27SvFxMSUer9kZGRIAQEB0ogR\nI6S+fftKx44dk9LS0kqtf74kaWlp0t69eyUDAwPJzc1Nevjw4d9en5CQIHXs2FG6deuWUr7vmzdv\npG+//VZ8DMoIbdq0kUr1DP3vvEGFp6jgc1K1alVGjhyJiYkJK1asoEuXLowaNeqLxmzn5OSwfv16\nZDIZhw8fLtW83n+gqalJnz59sLCwID4+nuPHj+Pm5oaDgwM2Nja0bt26zGntL1684MSJEyxevJh5\n8+Zx6tSp96p41rx5c44cOcKwYcPYsWMHHTt2VLp3P3z4MCYmJuKYRdlN7SoqxMbGlq7J/fjx4wwZ\nMuStf9u3bx8jRowQIyX47BQUFODn58ehQ4dwdXX9Ih/mR48eMW/ePL799ltsbW0VOgFOWloaN2/e\nZM+ePQA4ODhgYmKi1JXuJEkiISGBw4cP4+Pjw4IFC7CxsaFhw4Yf3FZ0dDR2dnbs3r1bqYrkyOVy\nnj17Jir0lSVK00Rw7Nixd5rc9+3bJ2wogi9KQkKCNGHCBGnTpk1SVlbWZ7vP9evXpd69eyudqTY/\nP1+KiIiQ3N3dJR0dHWnVqlVSdHS0VFRUpDTvUFxcLEVEREjz58+XjI2NpYMHD0oZGRn/ut3w8HCp\nY8eOUmRkpFhIglJDbM0Egv8xoW7evJkWLVpgZ2fHrVu3Pmn7MpmM7du3s2PHDvbu3UuXLl2Uqn8q\nV66Mnp4e8+bNIygoCF1dXaZPn87333/PhQsXSE9PV9hnl8lk3L59m4kTJzJr1ix69uzJxYsXGT58\nOJqamv+6fX19fXbt2sWIESOIjY0Vi0lQOqZ3YXIXCP5KUlIS69evp0GDBkyYMIFatWr9q/bS09NZ\ntmwZ2trajB07ViHKZ36q44qoqCj8/f3Zu3cvTk5ODBo0iDZt2ijM8wUHB/Pjjz9SoUIFpk6dSpcu\nXT5bZENISAjjxo3j+PHjtGzZUiwkQfkR6KGhoVy6dOmtDnA9evSga9euYoQEpUZxcTGXLl1i2bJl\nrF69mh49enxUO1FRUUyePJk5c+bwn//8p0w6fMrlcjIzM7l8+TLr1q1DR0eHsWPH0qVLl1LJpJab\nm8u1a9dYs2YNOjo6jB8/HkNDwy+SPCcoKIgpU6Zw4sQJmjVrJhaSoHwIdOCdyT2El7tAUUhOTsbT\n05MqVarg4uJCnTp13vu3fn5+eHh4sG3bNoXRWj83RUVF3Lt3jyNHjnDixAlmz55Nnz59vojGmpWV\nxYULF1i+fDn9+vVj1KhR6OnpffHvSWBgILNnz+b48eM0btxYLCJB+RDoAoEyIEkSly9f5ocffmDN\nmjX07Nnzb4VEfn4+GzduJCUlhcWLF/9rk72y8vz5c65cucKaNWswNjZmzJgxdOnS5ZOn3n358iX+\n/v4sXbqUCRMmMHz48FLP23/p0iUWL17M0aNHP8p7XiAQAl0g+IykpaWxYcMGAGbOnPlWbT0pKalE\nKx01apQoo8l/z7JDQ0PZsWMHv//+O25ublhYWNCgQYOPDpuSJInnz59z7Ngx1q1bx/z587G1tVUo\njfjs2bOsXbuWgwcP0qBBA7GABEKgCwSKpq3/9ttvLF68mIULF9KrVy8qVqyIJEmEhoYyceJENm3a\nhJmZmeistxAfH8/Fixfx9vamb9++DBs2DCMjo/fW2uVyOY8fP+bAgQPs3bsXNzc3+vXr99ly3/9b\nTp48iY+PD3v27FHq2H2BEOgCQZklPT2dzZs3k5OTw+zZszl//jxnzpzB09NTnJu+Bzk5OYSGhrJ9\n+3YSExOZPHkylpaW1KlT561ae1FREVFRUezdu5fAwEDmzJmDpaUlGhoaCv+ux48fx9fXl23btn2Q\nD4ZAoDQCPTw8nJs3b771LLJjx45KmUpRUP7w9/fnm2++YciQIWzbtu2TxDWXN2JjYzlz5gw+Pj6M\nGDGCgQMH0qZNG6pWrUp+fj737t1j586dJCcnM3XqVMzMzJSuDvnhw4c5efIkmzdvpnbt2mLQBWVL\noB87doyhQ4e+9W8iDl2gLIJo9uzZjB07lsjISJ4/f868efOEhv6RZGRkcPv2bY4dO0ZOTg49evTg\nxo0bqKurM2rUKDp16kSVKlWU9v3279/PpUuX8PDwKLeOkoLPR6lmihOhaQJlRZIkzp07x5QpU1i9\nejXffPMNc+fOxcHBgalTp+Ln50dhYaHoqA+kdu3adOvWDQsLCw4cOEBUVBQ3btygXr161K5dW+m/\nGXZ2dpibm+Pq6srr16/FgAvKjkAXCJSRvLw8vLy8CAgIYN++fejp6ZVsULt06cKePXt4/PgxkyZN\n4tGjR6LD3pOXL1+yZ88eTExMiI6OJiYmBg8PD0JDQ+nSpQvLly/HwcGBkydPkpaWppTvqKKiwsiR\nI/nqq69YvHgxOTk5YuAFQqALBKVBSkoK06ZNo2rVqri7u7/Va7lGjRpMmzYNFxcXFixYwKFDh4S2\n/jc8ffoUb29vTExMyMjIICAggIULF9KmTRsqVqxI7dq1sbGxYefOnSxdupTo6GhMTExwd3fn4cOH\nKJtfr6qqKo6OjrRt25bFixeTm5srJoFACHSB4EsSGhrKoEGDcHR0xMnJ6W/DrFRUVOjQoQM7d+4k\nKysLR0dHoqOjRSf+D3FxcaxcuZLevXtTvXp1bt26hYuLC02bNn2rl3vlypVp164dc+bMISQkhLZt\n2zJjxgyGDx9OQEAAWVlZSiXUx44dS4sWLVi2bBkFBQViQgiEQBcIPjeSJLF//36WL1/OoUOH6N69\n+3uf5VavXp3x48fj5uaGm5sbe/bsKfcf7/DwcGbOnImtrS26urrcunULR0dH6tWr9179qqKigqam\nJoMGDcLPzw9XV1eCg4MxMTFh06ZNSrNxqlChApMmTaJu3bosX76coqIisdgE/4pS9XKPi4sjJibm\nrX/T1tZGV1dXjJCgVMnOzsbd3Z1KlSoxe/Zs1NXVP7qtvLw8Dh8+jL+/PytXrkRHR6dcbYpCQ0PZ\nvHkzUVFRLFmyBHNz809W9UySJNLS0rh69Spr1qxBW1ubyZMnY2xs/Nkqq30qioqKWLt2LTKZDDc3\nN5FZUKCcAl0gUGQSExMZN24cTk5OfPvtt5/MwzomJob58+eXFA+pXLlyme3DoqIigoOD8fT0JC8v\nj/nz59O9e3cqVqz42e5ZXFxMSEgI+/fvx9/fnwULFtC7d29atGihsP1UWFjIihUrqFKlCvPnz//o\ndLgfOjaXL18WGwgh0AWCss2lS5dwc3Nj69atGBgYfPL2CwoKOHz4MEePHsXd3Z22bduWqTDOvLw8\nfvvtN9asWUOzZs2YPHkyXbp0+eLv+OzZs5KSrj179sTOzo4uXboo5CaqoKCARYsWUadOHebMmfPZ\n+yo3N5cePXrg5eWFTCYTi16JUVVV/a/SIQS6QPD/k5+fz+7duwkNDWX58uWfvaBGfHw8bm5umJub\nY29vr3TZz/4vr1+/LvFS79+/P46Ojp9lQ/Qx43rnzh127txJUFAQP/zwAxYWFtSrV0+hNNS8vDxc\nXV1p3rw5Li4un1Wo5+bm4ujoyOHDh8XCLwPo6OgIpziB4A/S0tJwcXGhoKAALy+vL1IdS1tbmz17\n9lCrVi3s7OwIDw9XujCsP86vf/nlF9q0aUNkZGRJHXhFEOYAVapUwdTUlO3bt+Pv709WVhb9+vVj\n8eLF3Lp1i7y8PIV4zqpVq7JixQoSExP5+eefEfqW4EMoVQ29uLj4nfG5lSpV+qznbALB/3L//n1m\nzJiBq6srFhYWpWL+TkxMZPny5RgaGvL9999Ts2ZNhRfkz58/59dff2XdunXMnj0bW1tbmjZtqhRj\n/urVK8LCwtizZw+RkZG4uLhgYWFB3bp1S/34Izs7m3nz5tG5c2fGjBnzWZ5HaOhlT0MvVYF+6dIl\nfHx8/mLykiQJR0dH+vXrJ0ZJ8FmRyWScPn2a3bt3s2bNGtq0aVOqz1NUVMSZM2fYtWsXbm5udO7c\nWeHO1ouLi3n06BGHDx/m2LFjODs7079/f+rXr6+Uc0CSJB4/fsyvv/7K3r17GTRoELa2tujo6JSq\nh/yrV6+YOXMmvXr1wsHB4ZPPAyHQhUD/pBw/fpwhQ4a89W+iOIvgc5OTk8OmTZt48+YNc+fOVahi\nGc+ePcPT05NGjRoxZswYhajOJZPJiIiIYPfu3dy9exdnZ2fMzc3LVHW5zMxMQkJC2LdvHxkZGXz/\n/fd07979ixy/vOt5pkyZwsCBAz9ppIUQ6GVToIszdEG55MmTJ3z//fdoa2uzbNkyhat81aRJE9au\nXYuenh4ODg4EBgaW2rMUFBQQFBTEuHHjWLRoEf3798fPzw9bW9syVypWU1OTPn36sHXrVtavX09i\nYiI9evRg6dKlREREfPHkL5qammzevJkjR45w4sQJcaYu+FvEIbWg3PHbb7+xePFiPD096dixo+Iu\nzooV6d+/Px07dmTTpk1cu3aNSZMmUadOnS9y/9zcXIKCgti8eTOamppMmDABIyMjpS5f+r6oqamh\no6PDzJkzGTNmDDdu3GDRokUAjBkzBmNj47fm8f8c1KlTBx8fHxwdHVFTU8Pa2losYoEQ6ILyTVFR\nEXv27CEwMJB9+/bRqFEjpXhuLS0tVq5cyW+//cbgwYNZvHgxFhYWn+1+r1694sqVK3h6etK5c2eW\nLl2Knp4elSpVKpfzplatWlhbW9OnTx9iY2M5e/Yszs7OjBkzhiFDhqCrq/vZ/Rzq1q3Lzp07GTly\nJGpqavTp00csaMFfECZ3QbkgMzOT6dOnk5GRwZYtW5RGmP9BhQoV+Prrrzly5AjXrl1j/vz5pKam\nftJ7pKamsnfvXszMzLh//z7bt29n/fr1GBoallth/n+1dn19fWbNmkVoaCj6+vpMmTKFwYMHExAQ\n8Nnrm9evXx9fX1+WLVvGtWvXxKIWKJZAF+dBgi9BREQEAwcOxNbWllmzZil1qtX69euzZMkSrK2t\nsbGx4dy5c/96HSUlJeHt7Y2RkRGZmZmcOXOGhQsXoqOjI9KCvgUVFRVq1arFoEGDOH/+PIsXLyYw\nMBADAwM2b95MXFzcZ7t3gwYNOHz4MHPmzCEoKEgMhuDPc7M0vdwzMjJISkp6698aNmxI3bp1xQgJ\nPhq5XM758+fx9PRk8+bNtG3btsxZHby8vMjKymLu3Lkf7ImdkJDAwYMH2bhxIytXrsTGxuaLnQuX\nRVJSUkrS3erq6jJu3Di6du36WXwOnj17hrW1NVu3bsXExOSj2hBe7mULHR0dkASCMkhubq7k7u4u\nOTs7S1lZWWX2PeVyuRQUFCSZmZlJZ8+elYqKiv72eplMJkVGRkrz58+X2rZtKx04cEDKzMwUE+YT\nUlhYKN28eVOaPHmy1LJlS2nXrl1SYmKiVFxc/Env8/jxY0lPT08KDQ39qN+/efNGGjZsmBiwMkKb\nNm0kcYYuKHMkJSUxduxY6tevj4eHBxoaGmXa/Nu1a1f8/f25c+cOs2fP5sWLF3+5rqioiDt37uDi\n4sKECRMwNTXl9u3bfPfddwoXsqfsVKpUCWNjY7y9vbly5QoqKioMHTqUadOm8fvvv1NQUPBJ7tOi\nRQtOnjyJvb099+/fFx0vEMVZBGUHSZIICQlh9uzZrFq1CjMzs3LXB7du3WLJkiVMmTKFPn36IJfL\nuX37Nj4+PhQUFDB16tTPZgYWvJu8vDzu3r3Lzp07uX//PtOmTaNPnz6fpDhMVFQUw4YN4+DBg+jr\n6wuTezk2uQuBLigTFBUVcfToUY4fP46HhwfNmjUrt32Rk5PD0qVLyc/PJyEhgaZNmzJ69GiMjIyE\nt7oC8OjRI86fP8+OHTvo06cPQ4YMQU9Pj2rVqn10m+Hh4djb23Pw4MH39hURAr3sCXRhchcoPVlZ\nWcyfP5/ExER2795droV5RkYGZ8+e5e7du1SpUgVVVVXMzc3p0KGDEOYKQqtWrZg0aRKXLl2ib9++\nbN26lcGDB7N//35SUlI+qs327duza9cuRo4cSUxMjOjkcooQ6AKl5uHDh9jZ2WFpacncCzDkRQAA\nIABJREFUuXP/lZajzCQnJ7Nz50569uxJbGwsW7ZsYd26dezbt4/09HQmTJhAbGysmDAKhIaGBubm\n5vz888/89NNPZGRkYGpqyuLFiwkLC/vgkq4dO3bk559/ZuTIkcTHx4sOLocIk7tAKZEkiYsXL7Jh\nwwbWrFmjMHW3vzSJiYkcO3aMbdu24ezszMCBA9+aNCciIoIVK1ZgbW3N0KFDxRm6gpKVlUVwcDCH\nDx8mNTWVUaNG0atXrw+qZHfz5k2cnZ05dOgQLVq0eOd1wuRethAmd4FSUlBQwMaNG/Hz88PX17dc\nCvPo6Gh++OEH+vbtS4MGDQgMDGTSpEnvzICnr6/Pjh07KC4u5vvvvycyMlJMJAWkVq1a/Oc//8HH\nxwdPT0+eP39eorU/ePCA4uLif2zDxMQEDw8P7O3tefLkiehUoaELBIrJixcvWLBgAT179mTkyJFK\nnfXtY6wS9+/fZ+vWrVy/fp2lS5diaWlJjRo1PqidmJgYli9fTvfu3Rk1alSp1vwW/POYv379muvX\nr7N9+3bkcjnjxo3D1NT0H0vqXr58meXLl+Pr60vjxo2Fhi40dIFAcbh79y7ffvstTk5OjB49utwI\n8+LiYoKCgrC3t2fOnDkMGjSImzdvMmjQoA8W5n8s/G3btqGhoYGdnR0RERFicimqxqWigoaGBtbW\n1hw5coRVq1YRERFBly5dWL169d+OnYWFBfPmzWPs2LEkJyeLzhQaukBQ+sjlcg4fPsy+ffvw9vYu\nN17sBQUFBAYG4uHhQe3atZkyZQrGxsZUrPjpiiQmJCTwww8/0K1bN5ycnIS2riRae1ZWFoGBgaxd\nu5Y6deowbdo0unbtirq6+l+u9/PzY8eOHWzbtu1PqX2Fhi40dIHgi5KTk8OiRYuIiIjgwIED5UKY\nv3nzhpMnT2JiYsKvv/7K6tWr2bt3L6ampp9UmMN/s43t2rWL+vXrM2TIEMLDw0XRJCXQ2jU1NbGx\nseG3335j4cKFXLp0CX19fby9vf/i4f7NN99gb2/P1KlTycjIEB0oNHSB4MuTkJCAi4sL9vb2DBky\nBFXVsrv/lCSJV69ecfbsWebPn4+dnR2Ojo5ftKBMYmIiixYtonPnzowZM+at2p5AcUlOTubq1aus\nX7+e9u3b4+jo+KesgAcOHODs2bP8+OOP1KpVS+E19KKiIh48eIBMJivZyDRu3BgtLS1UVFTIysoi\nKiqKZs2a/ckZtKioiLCwMNTV1Wnbti0pKSkkJydjaGj4zrVXXFxcslmWy+XI5fK/XKeqqqrQ3yBR\nnEWgkBQXF0uBgYGSsbGxFBISUqbfVS6XS8nJydK2bdukRo0aSe7u7tLjx49L7XmKioqk48ePS1ZW\nVtKdO3ckuVwuJqSSUVBQIAUHB0tTpkyRdHR0pJ07d0pPnz6ViouLpb1790pOTk7Sq1evFL44S3p6\numRsbCxZW1tLI0eOlAYNGiSpqalJXl5ekiRJ0s2bNyVAWrhw4Z9+9+DBAwmQJk+eLEmSJJ07d06y\ntrZ+533i4uIkQJLJZJIkSVJISIgE/OW/c+fOKXxxlopiXytQJAoLC9m6dSt37tzB39+/zJbzLC4u\n5unTpxw/fpxt27Yxffp0bt++/c6wsy9FxYoVsbW1xdjYmOXLl9O6dWvGjBnzjx7VAsVBTU0NExMT\nTExMePr0KQEBAQwdOpTu3btja2vLy5cv+eGHH3Bzc1P4d8nOzubgwYO0bNkSgOvXr/P9998zatSo\nkmvOnDmDi4sLderUAeDKlSvUrFmzRJtWUVF561FVVlZWyfr7X3R1dYmKikJFRQUVFRWio6NZsmQJ\nHTp0UPj+EmfoAoUhJSWF6dOnU6FCBX766acyKcxlMhkPHz5kyZIlDB8+nEaNGhEYGMiECRNKXZj/\nL40bN8bb25u2bdtib2/PzZs332qGFCg2TZs2xcnJiYsXL5aklz18+DBeXl5Mnz5d4Y9VVFVVKSgo\noKioiLy8PIqKilBXVy8paGNhYUGHDh2IiooCID8/n+PHjzN37tz32vjo6+vj7Oz8p39XV1dHV1cX\nHR0dGjduzP79+/npp5/Q0tJS+PEWGrpAIXjw4AGzZs1i7ty59O7du0xaHsLDw9m1axexsbFMnTqV\n2bNnK3Rp1woVKjBgwAA6d+7Mxo0buXTpEuPHj6du3bpiwioZ6urqmJqaYmhoSFhYGB4eHhw8eJCx\nY8cq9HPXqlULNzc3NDU1SUpK4syZM/j6+pakeG7YsCFWVlacP38eMzMz4uPjqVixIkZGRpw7d+5v\n265WrRomJiZ/uwZPnz5N48aN6dy5s1KMsxDoglJFLpdz4sQJfvnlF37++We0tbXL1Pvl5uZy584d\ntmzZgkwmY+LEiXTp0kWpcs5raWmxatUqLl26xJgxY5g5cyY9e/Ys006KZYW0tDSys7O5c+cOkZGR\nBAUFIZfLMTc3x8bGhkuXLin08+fk5DBjxgxatmxJYWEht2/f5ttvv8XExKRko2xmZoadnR2zZ8/m\nwoULODo6flBq43f5hb9+/RpXV1f8/f3/dYlbIdAFZZ6cnBw2bdpEYWEhe/bsoVatWmXm3V6/fs2N\nGzfw8vKiefPmODs706FDB6VNhlOhQgWsrKwwNDTEy8uLixcvMm3aNBo0aCAmsoKQl5dHamoqycnJ\n3Lhxg4iICO7evUuPHj3Q19enT58+TJo0iapVq1K9enXy8/Px9/dX6HcqKiqiQYMGJcdRLVq0wMLC\ngri4OOrUqYMkSdSrVw9nZ2cuXLiAv78/27dv/yTFaa5fv06PHj1o3bq10swBIdAFpcKTJ0+YN28e\ngwYNYsiQIZ88vrq0SE9P59KlS2zcuBFzc3PWrl2Ljo5OmSld2qBBA5YsWcLvv//OqFGjmDVrFr17\n9xbaeimQkpJCZmYmDx484MaNG9y/f5/KlSvTr18/WrVqxZAhQ9DU1KRq1apvXV9yuRwVFRWF30i+\nfv2arKwsiouLefLkCZcvX2b9+vUUFRUBUKlSJfr27Yu1tTXDhg2jUaNG/1qgy2Qyzp8/j62trdJo\n50KgC0qF69evM3fuXDZv3qw0Z1P/RHJyMn5+fqxbt47x48ezf/9+mjZtqlQfgw/5yPbo0YMDBw7w\n008/cebMGRYsWCC09c9IQUEBiYmJpKWlcfXqVW7evElCQgJWVlYYGxszYsQIli1bRuXKlctUSuSc\nnBw6d+5MkyZNyM3NpXr16hw+fBgjIyNu3rzJq1evkMvldOrUCYABAwagpqZGcXFxSfnZ4uLiv02o\n8zaTe2FhIT/++CNTpkxRqv4SiWUEX4zi4mJ27tzJ1atX8fDwoGHDhkr/TomJiRw9epS1a9eydOlS\nBg0aRIMGDRRe8/lUyOVyQkJCmDt3LnPmzKFfv35CW/8EvHz5kqdPn/Lo0SPOnDlDSEgITZo0wdbW\nlpYtW9KuXTvq169PhQoVPnrTqOiJZSRJKtHC/5dKlSqhoqJCcXExxcXFqKmplVz7R38UFxcjl8up\nVKnSn6571xwuKir600ZIkiQKCwupVKmS0sxnHR0dIdAFX4aMjAyWLl1Ks2bNmDx5stLnDI+JicHX\n15ddu3axatUqrK2ty3Ws9qtXr/Dx8SEhIYGFCxcqVAieoiOTyYiNjeXly5cEBgZy9uxZ0tLSsLa2\nxsLCAi0tLdq2bUvVqlVLYqM/BSKXe9lCR0dHmNwFX0b4jR49mvnz5/PNN98otTb6R+jZ5cuXWbx4\nMZGRkR9V8aysoaGhwdy5c7l79y4ODg64uLjQr1+/MuM78Kk0Tvivn0VMTAxPnz7F39+f3377jfbt\n2zNo0CB69eqFvb09LVq0EB0m+GCEhi74rPxxrrxjxw7atGmjtBpUaGgoP/30E0lJScydO5cePXp8\nUGhMeSInJwcfHx9iYmJwc3MrN9Xx/i9/mHrj4uJITEzk3r17HDt2DICePXvSt29fGjZsSOvWrale\nvfoXfz6hoQsNXSB4L968ecPPP/9McnIyJ0+eRFNTU+neIT8/n99//x13d3c0NTVxdnbGxMSkzHjk\nfy7U1dWZPXs2d+/eZdy4cUycOJH+/fuX6fr1f5zD5uTk8ODBA549e8b58+e5c+cO7du35+uvv6ZP\nnz6MGDGC5s2bi0kiEBq6QDlISkpizpw5WFpaMmrUKKUTgNnZ2Vy9epWVK1diZGTEhAkTMDIyKjeO\nbp96Y/fLL79w7949FixYQKtWrcrEexUUFFBYWMjTp0+JjIwkJiYGf39/NDQ0MDIywtLSkiZNmqCl\npaWw2QCFhi40dIHgb7l16xazZ89mzZo1dOvWTamePT09nYCAANauXYu1tTW7du2iXbt2YlD/BdWr\nV2fKlCmEhYUxe/Zshg8fzsCBA5XquEKSJHJycsjNzSUsLIxHjx4RGBhIfHw8X331FR07dsTGxoZR\no0bRqFEj4eUvKDWEQBd8EgoLCzly5Ajnzp3D19dXqc5Nk5KSOHXqFJs2bcLJyYlff/1VOCV9YgwN\nDfH19eXAgQOMHz+eBQsWKOxmqaCggNevX5OSkkJISAixsbFcvnwZbW1t9PX1MTY2pn///mhoaCh0\nLn5B+UOY3AX/mszMTNauXYuGhgZTp05V+ApOf/D48WMOHjyIj48Pbm5uDBgwQCkqKik7Dx8+ZOXK\nlVhaWjJ8+PBSz2ufmZlJdnY2kZGR3L17l7CwMJKSkjA3N6d169aYmJigqamJpqZmmfLaFyb3soUw\nuQv+NbGxsbi4uDBp0iSsra0V3twoSRIPHz5kz549nDhxgiVLlnD37l1R7/sL0q5dO7Zt28apU6dw\ncHBgyZIlGBgYfJF7FxYWkpKSQkZGBjdu3CA8PJzQ0FA6dOjAV199Rbdu3XBycqJq1aoiHFGgdAiB\nLvhozp07h4eHB5s2bUJPT0+hn7W4uJh79+6xbds2YmJimDVrFm5ubkpjTShrVK1aleHDh9O5c2fc\n3d3p1KkT33///ScP38rIyODly5fEx8dz7do17t+/T35+PtbW1rRq1QorKyvq169PlSpV3plJTCAQ\nAl1QZsnPz2fr1q3ExMSwf/9+6tWrp7DPWlhYSFBQED/++CMVK1Zk8uTJmJiYiBhyBaF169Z4eXlx\n+vRpvvvuO5YvX46RkdFHtSWXy0lISCjRvm/cuEFkZCRmZmb07NmTb775hgULFlC1alUqV64sohYE\nQqALyjcvX75k1qxZdOvWjY0bNypsSFpubi7Xrl3D3d0dXV1dFixYgKGhochcpoBUqVKFIUOGYGxs\nzIoVK2jbti1jx479R5N3ZmYmiYmJPH36lPPnzxMcHEy1atUYPHgwurq69OvXjxYtWvyrfOcCgTIh\nnOIE701ISAgTJ07kxx9/pFu3bgqp4bx+/ZqAgAAWLVqEtbU1jo6OtGvXToQSKQkymYzTp0/j6emJ\np6dnSTU+SZKIj4/nxYsX3LlzBz8/P+Lj4+nduzf9+/dHS0sLPT091NXVP2m+87KMcIorWwinOMF7\nUVxczPHjx9m3bx9Hjx5VyJCutLQ0zpw5g6urK+PHj8fPzw9tbW0xeEpGxYoVsbGxQVtbGycnJ/r0\n6UNaWhoXLlygSZMm2NjY8NVXX9G/f39atWolNG+B4H/Xj+gCwd+RnZ3NmjVrUFFRYd++faWSc/pd\nSJLE8+fP+fXXX1m7di1z5swhKCiIpk2bioFTos2iXC7n6dOnxMfHEx0dzbFjx0hLS8PU1JTGjRtz\n+vRpNmzYgI2NjegwgUAIdMHHkJCQgLOzM/b29gwdOlRhzNbFxcU8fvyYQ4cOsW/fPubOnUtISAgN\nGjQQg6bA/FFjOj8/n/DwcJ49e8bVq1e5evUqbdu2pWfPnpiamv7p7Btg0KBBrFmzhoiICMaPH0+d\nOnVEZwoEb0GcoQv+glwuJzAwkCVLluDp6UnHjh0V4rmKioqIjo5mz5493Lhxg1mzZvH1118rZeGX\n8kBRUREFBQWkpKTw4MEDHj16hJ+fH6qqqhgaGmJubk6bNm2oX7/+PwppSZI4f/4869evZ9GiRXTv\n3l34RfxLxBl62UJHR0cIdMGfyc/Px9fXl+DgYJYvX64QmdPy8/MJCwtjx44dJCUl4ezsjJmZmUKZ\n/wX/LZuan5/Pw4cPiYqK4u7du9y8eZNOnTphYGBAjx49qF27NlpaWh8d852SksLmzZtLQhDr168v\nOl4IdAHCKU7wf0hNTcXNzQ0DAwO8vb1LvdxlTk4Ot27dwsfHh6pVqzJ+/Hg6d+4sYsgVRPt+9eoV\nmZmZ3Lp1qyRxi4aGBh07dqRTp0707t2bmjVrflITeYMGDVi2bBmBgYGMHj2aGTNm8PXXXwvnOIEA\nYXIX/H/cu3ePmTNnsnDhQszNzUs17CcrK4tr167h5eWFgYEBDg4O6Ovri0xepcjr16959eoV8fHx\nhISEEB4eTlRU1J/yndevXx8NDY0vtuF6+fIlW7du5c2bN7i4uNCwYUMxUEJDFxq6oPwil8vx8/Nj\n9+7d+Pj4oKOjU6oWgnPnzuHp6cngwYP56aefRGhSKSBJEi9evODVq1cEBwcTFhZGSEgITZs2xdTU\nFH19fUaOHEm1atWoWbNmqW3+6tevj6urKzdv3mTs2LFMnjyZvn37ivkiEBq6oHzu0D08PHjz5g2u\nrq7UrFmzVJ7j2bNn/Prrr6xbt46ZM2cyZMgQGjduLJyevqD2nZycXOJ1/uDBA168eEG/fv3Q1dVF\nT0+PFi1aULly5VI/hnkXmZmZbN++ncePH/PDDz/QuHFjMbBCQxcauqB8kJiYyPTp07Gzs8PW1rZU\nUqLGx8dz6NAhvL29WbZsGSEhIdSrV09k+frMPHnyhLS0NO7evcuVK1e4f/8+7du3p2/fvpibm+Ps\n7Iy6ujpqampKs6nS1NRk1qxZhIWFMXr0aCZNmsSAAQNEqt9/0ujEWitTY1lqGrpcLicuLo6srCwe\nP37M77//TnR0NHl5eRQUFFBUVISKigpqampUrlyZGjVqYGRkhKmpKXXr1qVBgwYigchHcuPGDebN\nm8emTZvo1KnTF1/UERER7NmzhzNnzrBw4UKsrKzQ0NAQH5fPpIXFx8eTkpJCQEAAgYGBFBQUYGtr\nS6dOnWjRogXa2tpUrFhRYfPyfyjZ2dns2LGD0NBQVq5cSbNmzcREeMfcqF69Op06dUIul4sOUXJh\nfvfu3S8r0B89ekR8fDwnT54kICCAli1bYmVlRfv27dHQ0EBLS4sqVapQpUoVqlWrhlwuJzc3l/z8\nfHJyckhOTiYzM5ObN28SEBAA/DfphLm5Obq6utStW1eM7N9QVFTEnj17uHz5Mh4eHl/ciej27dv4\n+PgQFRXFDz/8gLm5OdWqVRMD8wl5+vQpz549IzIyklOnThEWFoaJiQmDBg2iadOm6OjoULdu3TKf\n71ySJMLDw1mwYAHff/89AwcOFE6V71CsBGVIsH9OgS5JEqmpqQQHB7NlyxZq166Ng4MDDRo0QFdX\n9197w2ZkZBAXF0dCQgKbN2+mY8eODB48GGNjYyEo/g9ZWVksWrSIJk2a4Ozs/MU8kWUyGUFBQaxd\nuxaZTMa8efMwMzMTptBPsLby8/OJiorixYsXXL58GX9/fzQ0NBgwYABmZmY0bNiQNm3alOu+zs3N\nZceOHdy+fZulS5fSsmVLMXkEQqB/6K4vLi6Oo0ePcunSJaZPn46RkdFnN5E/fPiQwMBAtm7dirOz\nM/369SvRRsozUVFRODs7M336dPr16/dF+iMvL4/AwEBWrVpFo0aNcHFxwdjYWJjVP3I9yeVykpOT\niY6OJiEhgePHj/Po0aOSQiVt2rShadOmIv3tOwgPD2f+/PmMHDkSW1tbkctAIAT6+2gN0dHR/PTT\nT2RmZjJt2jQ6d+78xcNIXr9+zW+//YaHhwcDBw5k5MiR1KtXr9wNbnFxMZcvX2bVqlV4eXmhr6//\nRfr+4sWLrF69GjMzM5ycnDAwMBAr7QMoLCyksLCwRHjfvn2b06dP07RpU7p27YqFhQV169alZcuW\nCut1rojk5eXh6+vLb7/9xqJFi2jdurXYYAqEQH8bKSkp7N69m7i4OKZPn46enl6pv1xBQQFBQUG4\nu7vj5OTEgAEDqFq1arn5eG3atIkXL16wdOlSatWq9Vnvl5qayunTp3F3d2fEiBHY2dnRpk0bscLe\nY9OVl5dHVlYW9+7d48mTJ5w/f5709HSMjIzo0aMH+vr61KlTRyHS8JYFHj58yKJFixgwYADffvtt\nufkmCIRA/0dkMhlnz57Fy8uL+fPn06tXL4ULdcnOzub48eP4+fmxaNEiOnToUKYH9dmzZ8yfPx8r\nKyvs7Ow+2xmqJEkkJSVx7Ngx3N3dmTdvHoMHDxbRB3/DH06ecXFxREREEBERwZUrVzAwMEBfX58e\nPXrQsGFD6tevL/xAPvOG98iRIwQEBODq6qoQCohAUKoCPS0tDU9PT+rUqcPYsWPR0NBQ6Jd98uQJ\nbm5uWFpaYmdnVya9Xm/dusWCBQtYtmwZZmZmn+UecrmcR48ecejQIfbu3cvcuXMZMGCAKJTxlg1P\nZmYm2dnZhISEEBsby/Xr1ykuLuarr77CwMCAzp07U6NGDdF3pURMTAzLly+ne/fu2Nvbi4I/gvIp\n0ENDQ1mwYAFubm706NFDac6i8vPzOXToUEl50CZNmpSJgZTJZBw4cIDTp0+zfv36z/JeMpmMyMhI\n9u7dy82bN3FxccHS0vKzm/OVhTdv3pCVlcWzZ88IDg4mOjqa0NBQTExMaNeuXUnct7q6uhAcCvZN\nOH36NPv372fhwoUYGRmJThGUH4Hu7+/PsWPHWLp0qdImbbh16xarV69m1apVtGvXTqkHMSsri9Wr\nV1OvXj0mTpyIurr6J22/sLCQ0NBQtm3bRkZGBlOmTKFbt26f/D7KRkpKSsnZd2hoKCEhIVSvXh1L\nS0tatmxJp06dqFGjBjVq1BD5xZWAxMREVq1aha6uLmPHji21VMgCwRcR6JIksW/fPoKCgli9erXS\nT/hHjx7h5OTE+vXr6dy5s1K+Q0xMDLNnz2b8+PH069fvkwqOvLw8fv/9d3788Udq1arFhAkT6NSp\nU7kM+cnPz+fZs2ekpqZy5coV7t27R0xMDFZWVhgaGqKrq0ubNm1QU1MTTlZKTEFBAQEBAXh5ebFi\nxQq6dOkiOkVQ9gS6JEl4eXmRkJDA8uXLy4zDztOnTxk1alTJOZqyIEkSFy9e/CwhadnZ2Vy5coU1\na9bQpUsXxowZQ7t27cpVgpLk5GRevnxJZGQkFy9eJCwsjAYNGmBjY0OzZs1o3749devWpVKlSkL7\nLoM8f/6cdevW0bBhQyZOnCiOlQRlS6Dv2rWL+/fv4+7uXuZiX1+8eMHQoUPx9vZWivOzwsJCvL29\niY6OZuXKldSpU+eTtJuens758+dZsWIFw4YNY+TIkbRu3brMVz0rLCwkLi6uRPu+cuUK6enpDBgw\nAFNTU5o0aULbtm1RU1MrM/nOBf+MTCYjICAAd3d31q5dS9euXUWnCJRfoB85coTTp0/j7e1dZp15\n4uPjGTp0KIcOHSrVmuD/REpKCq6urhgbGzN69OhP4qmflJTEqVOnWLVqFS4uLgwdOpTmzZuX2Umf\nkpLC48ePSUhI4OTJkwQHB6Ovr8/gwYPR1tamdevWaGlplfl854L3ny9r1qyhdu3aTJ48mdq1a4tO\nESinQA8MDGTNmjXs3r37k2mCisqdO3eYNGkSp06dUsgUmg8ePGDMmDG4u7tjaWn5r9tLSEjg8OHD\nbN68mYULF2JjY/PFC7Z8TiRJQiaTERMTQ1JSEsHBwfz666/IZDKsrKzo06cPDRo0QEdHR8R8C/5x\nLl24cAFXV1c2btyoVMdzAiHQgf+eI/br14/jx4+Xm6IGp06d4syZM3h5eSmMeVUul3P48GF++eUX\ntm7d+q8iC+RyObGxsezZs4cTJ06wcOFC+vbti6amplKPm1wup7i4mKysLCIjI3n27BmnT58mJCSE\nDh060K9fP9q3b4+WlpYopyn4aFJTU9mwYQMVK1Zk2rRp5TKltEAJBXphYSFTp05l2LBh9OnTp1zt\nxJcuXUqrVq0YNWpUqT/P69evWb9+PaqqqsyaNYsaNWp8VDtFRUWEh4ezfft27t+/z9y5c/n666+V\nNvSssLAQmUxGfHw88fHxPHjwgJMnT6KhoYGxsTG9e/cuEd7lPbxO8Om5ePEiy5cvZ+nSpfTs2bPM\n+5kIlFyg7927l6dPnzJ//vxyN1lfv37NoEGD2LRpU6kWFnn8+DEzZsxgxIgRDBs27KPOc/Py8rhz\n5w5btmwhOzubadOm0a1bN6UKrZLL5eTl5fHmzRvCwsJITEzk8uXLxMXF0aFDB7p27YqxsTGampoi\n7azgi5GWloa3tzcFBQU4OzuXqeMqQRkS6I8fP2b06NGcOHGi3IZrPHjwgJkzZ3L69OkvniJWkqSS\nilAbN26kY8eOH9xGTk4O169f56effkJLSwsnJyeMjIyUIt1tfn4+ubm5PH/+nHv37hEXF8eFCxdo\n2rQp7du3p3v37jRv3py6deuK5B+CUufGjRusXLkSZ2dnevfuLSIhBIoj0IuLi5k7dy7ffPMN5ubm\n5bpzli1bRvv27Rk8ePAXFWa//PILYWFhLFmy5IOd8zIzM7l06RLr1q3D3NwcBwcH2rVrp9Cx0pmZ\nmSXad1RUFMHBwbx8+RJTU1PatWuHiYkJGhoa1KtXT8R8CxSSjIwMtm/fTlJSEnPmzKFx48YK/8wF\nBQWsW7dODF4ZoXLlyn8V6Ldv38bHxwcfH59ylUjkbbx8+RIzMzOCg4O/iIf/y5cvWbZsGXp6eowe\nPfqDzOIpKSn4+/uzfPlyxo8fz3fffUfLli0VLuyqoKCA9PR0UlNTCQoKIioqit9//x1DQ0MMDAww\nNDRER0eH6tWrC+1boFRIksTt27dZsWIFY8aMwdraWqG/obm5uQwePJi9e/cik8nZ1DwZAAAgAElE\nQVTEACoxqqqqdO3a9c8CvaCgAHt7e5YsWfJemccSEhLw8/OjQoUKSJKEmpoa7du356uvvvrbiVxU\nVERubi4aGho8e/aMq1evMmLEiI86q8/OzkZNTe0vyW4ePHjAlStXsLe3/1Pc6NatWxkwYACNGjV6\nr/YPHTrEy5cvmTZt2mcdkPDwcKZPn86CBQuwsLB4b0GcmJjIr7/+ioeHB/PmzcPW1lahtIP09PQS\nz/Pg4GAePHhAdnY2ffv2pVWrVnTq1Im6detSvXr1cr+BFJQNXr16xS+//EJkZCSurq4Km9MhNzcX\nR0dHDh8+LAatDKCjowPS/xAaGipNnDhRksvl0vsQFBQkNWzYUNq+fbvk6+srbd++XdLS0pJ27tz5\nt22EhYVJM2bMkAoLC6VHjx5JGzZskGQymfQxuLq6Srdv3/7Lv588eVICpLVr15Y8i1wulwApLCzs\nvdt/9eqVpKenJ6Wnp0ufi2PHjknW1tZSXFzce/8mKipKWrRokaSvry/t3btXSk1NlUqbP8bz9u3b\nkqenpzRy5EjJyMhImjx5srRnzx4pMDBQSk9Pl968eSMJBGUZuVwu3bt3T7KxsZGOHDkiFRQUKNwz\nvnnzRho2bJgYrDJCmzZtpBLvjeLiYvbv38+4cePeWztUUVHByMgIOzu7ksQcOjo6DB8+HAcHB1RU\nVIiIiCAjI4OaNWvStm1bKlSowIMHD4iLiyMmJobGjRtjaWlZop0/f/6cZ8+eUblyZdq1a0flypV5\n+fIlubm5SJJEcnIyGhoa6Onp8fz5c2JjY4mIiEBbW/tPsdQqKiqYmpqycOHCkgIa//u396VGjRrM\nmDGDq1evfvKz9Ddv3rBp0yays7Px9fV9LwfEe/fusWXLFkJDQ3F1dWXWrFmlZppOT0/n+fPnJCQk\nEBAQwJ07d6hcuTK2trbo6OgwcOBAtLS0qFSpknAUEpQrVFRUMDQ05MCBA+zevRsHBwdWrlxJ69at\nRecIPhslX9lnz54RExND+/bt//VHvnnz5qioqLB3716OHTuGvb093t7eNGvWjMWLFxMSEsKLFy94\n+PAhRUVFTJ06lWvXrhEWFsbQoUOZMWMGISEhqKur4+HhwZ07dxg7dizdunXD2NiYlStXEhAQUPLc\nYWFh9OrV6y/JUSwsLBg7diyzZ8/Gz8/vo3LQq6ioYG1tzYgRI+jXr98nC/d6/vw5U6dOxdbWFnt7\n+789bpDL5dy6dYuNGzeSnJyMm5sbGzZs+KJVz+RyOY8ePeLly5cEBQVx4cIF4uPj6d27N71792bk\nyJGsWrWKatWqUaFCBZEyVSAAqlWrxsSJE+nVq1fJkdiwYcPKXD0MgYIJ9MDAQMaNG/fBYU3nzp1j\n8uTJqKmpERsby9WrV7ly5QoVKlSgoKCADRs20Lp1a/T19XF0dESSJBwcHHj16hU2NjZER0cjSRIq\nKirMmjWLHTt28PXXX5OXl0ffvn2JiIhARUWFZs2asWPHDmrWrElubi5RUVE4ODjQqVMnhg8fTosW\nLf7ybDKZjGHDhnHixAmOHDmCvb39R3VS/fr16d69O7GxsXTo0OFfd/r169eZOnUqO3fupFOnTu+8\nrrCwkOvXr7N27Vpq1KjB9OnT6dq16xfx9M7KyiI2Npbnz59z5swZrly5QpMmTRgyZAgdO3Zk4MCB\ntGrVSuQ7FwjeQynQ09Nj37597N+/nxEjRpTUXRcIPrlALyoqYuvWrfj6+n5wAx06dMDBwYGqVaui\nqqpKkyZNaNKkCQCWlpb4+/vj7+/P48ePadq0Ke/KNJuRkUF4eDhr165ly5YtSJJEamoqsbGx1K1b\nFysrq5Isaa1atSI3N/e9BEnVqlVZsWIFHTp0oGfPnh/VSRUqVKBXr16cOHHiXwn0oqIi9u7dy+XL\nlzl79ixaWlpvve7NmzdcuXKFlStXYmhoyKpVq/5W8P8bJElCLpcTHx/P06dPuX//PseOHSM1NRUL\nCwv69+/PhAkTWL9+vfA6Fwj+BVWqVGHMmDGYmpri6urKwIED+fbbb7+opU1QDgR6XFwczZs3/6gc\n11paWnTt2vUvVdhev35Nly5d2LJlC9u3bwfA2tr6ne2oqamhqqrKpEmTMDAwQJIknj17hra2NuHh\n4aipqX20JmhgYICnpyfu7u4fHX5mYmLCkiVLcHFxQUND44N/n5mZydKlS2nUqBHbt2//yyKWJIlX\nr15x9uxZ3NzcGD58ODt37qRdu3afVHjLZDJyc3OJiIggKSmJgIAArl69SuvWrbGysqJr167Y2NjQ\nsmVLkc5SIPgMtG3blv3793PgwAFGjRrFsmXL0NXVFZYuwacR6KGhoR9tjn4X2dnZFBYW0q5dO2rX\nrs3PP/9MzZo1KSwspFKlSjx//pw3b96UXK+urs6QIUOIj4/HysqKJ0+e0KtXL2JjY/m7gnBVqlTh\nxYsXFBcX/60p2snJCUtLS3Jycj7qfapXr06fPn14+fLlBwv06OhoJk+ezMyZM/+yqfnDEnHy5Enc\n3NyYMWMGFy9e/CTFcIqKiigqKiIpKYmHDx8SHx/PiRMnkMlkdOrUCSsrK6ZNm8aaNWuUvjiLQKBM\nVK5cGUdHR0xNTVm0aBGWlpbY29srVUpmgeKhKpfLOXbs2EfFSlapUgVDQ8O37iwbN26Mp6cno0aN\n4rvvvqN58+Z06dKFQ4cO0bx5cxo0aICXlxeqqqqYmJggSRLLli3j0aNHdOjQgSlTpnD+/Hlat25N\n9erV/5QxrVatWiWx5dbW1mzZsoXIyMg/3V9dXf1P8dg1a9Zk48aN2Nvbf1T6U1VVVdq1a8eNGzfe\n+zdyuZwzZ87g4uKCt7f3n4S5XC7nyZMnbNy4ERMTE2QyGXfu3GH+/PkfLcxzc3NJT0/n2rVr7N69\nm8mTJ2NqasqKFStITEykZ8+e/PLLL1y9epUff/yRAQMGYGBgIIS5QFBK6OjosGfPHmrWrImDgwP3\n799HLpeLjhF8FCq5ubnSV199xd27dz9Lnu/MzEzU1dWpVKkScrkcmUyGmpoacrkcuVz+1nCmtLQ0\nqlWr9t41qv/Q+j+3ySohIQFPT09+/PHHf7z2zZs3bNu2jadPn+Lq6lpi6pfJZMTFxXHw4MESYW9l\nZfXBZRiLiorIyckhNTWVO3fu8OjRI86fP4+mpiYGBgZ069YNXV1datWqVebr2AsEZYH4+HhWrFiB\noaEhTk5OH11Z8UMUAJFYpmxtDitGRkZiamr62Yp2/K/2p6qqWnIfVVXVd57R1q1b94Pu8aUKjtSu\nXZs7d+6QkZHxp+xz/5ekpCTmzp2LpaUlU6dOpWLFihQWFhIeHs4vv/xCTEwMU6ZMYfr06e9d/Ob1\n69e8efOGqKgowsLCePDgAeHh4ZiZmdG2bVu++eYbRo8eTZ06dZSiAItAIPgz2trabNmyhbNnz+Lo\n6MiCBQvo3LmzOFsXvDcVMzMz6d69u+iJ96BKlSq0adOG/Pz8d14THBzMvHnzWLNmDV27diUvL4+g\noCC2bNmCXC5n/PjxdOnS5S9OhP+LTCYjNTWVrKysknznwcHBNG3alK+++go9PT2GDRtG9erVy201\nPIGgLKKmpsbAgQPp3Lkzq1ev5vLly4wfP16sc8H7CfSkpCTatGkjeuI9F1u7du3IyMj4Sy54mUzG\nkSNHOH36NL6+vtSuXZtz586xefNmmjVrxvTp0zEwMHhrQolXr16RlpZGQkIC169f58GDByQlJdG3\nb19at25dsluvVq2aSEghEJQDmjRpwoYNG7h8+TIjRozAzc2Nbt26CW1d8PcC/datW/8qtjorK4uc\nnBwaNWr0FxO6TCYjMTGRvLw81NTUaNmyZUkBjsLCQl68ePGX9v5IIqOo1K5dm+jo6D9l1Hv9+jUr\nV65EU1OT1atXc/PmTTw9PenVqxdr165FV1e3xFdAkiSePn1KZmYmN2/eJDg4mLCwMPT09DA3N8fM\nzIzJkyeX+BCIBSwQlF8F4j//+Q+GhoZs2LCBCxcuMHXqVOETI3i3QI+Jifnb8+C/Qy6Xs2XLFhYt\nWsTjx4//pLWmpaWxcOFCfHx8GDBgAP7+/owZMwZ3d3fq1avHixcv3prdTVtbm7i4uI96nsLCQrZs\n2YKfnx9qamo4OTkxcOBAVFVVuX//Pu7u7mRnZ2NtbY2Tk9NHVfdq2rQpsbGxJf8fExPDjBkz6N27\nN1WrVqVv376MGvX/2rvzqKbOhA3gT0IQwh4F3MAFJS6oyOoCKpUqKipqx6rAVMcFx6qtS231oLXT\nqaVuY9sBC3Z0LFQ7MiJWFBRppSMqqBUEqkJVZFgE2beAYbnfH37mmIIL2GkDPL9z+IOb5Ia8ufrc\nd38Dhw4dQv/+/VFVVYWMjAzk5eUhLi4OV65cQW1tLWbNmoUhQ4Zg/fr1sLKygkQi4W5jRNRMz549\nERAQgPj4eMybNw+bN2+Gm5sbC4aaEZeXlz+zP/dZKioqsH37dvTt2xfx8fGq44IgwN/fH2ZmZqis\nrERkZCSqq6vRtWtXBAUFqZ4DPFohTqlUqn5u3rzZ5g9z6tQpxMXFISgoCCtXrsRrr72GtLQ0lJaW\nwtfXFzNmzMDOnTuRlJSE0NDQNr2HiYkJamtrAQCxsbGYNWsWKisrERwcDADYu3cvxo8fj7Nnz2LW\nrFmws7PDJ598gtLSUsyYMQMRERFISEjAu+++Cy8vL9jY2EAqlTLMieiptLS04O7ujm+++QZnzpzB\n+++/j+LiYhYMqdfQa2pq2ryYQUJCAhYuXIgJEybgm2++wZw5c6Crq4v09HTs27cPRUVFqqkXEokE\n69atw40bN9QGlWlra/9qYVZSUoIxY8bA2toaFhYWmDZtmmpkuFQqxfz58yESibB48WJs374db7zx\nRqvf28DAAPn5+fjiiy/w5ptvQkdHB48HFu7ZswdGRkaYNWsWRo0ahYCAAAwcOBASiYRN50T00szM\nzFS1dQ8PD+zcuRMTJ05kwdCjnK2vr3/h+d5PUiqVOHLkCBYtWgRbW1vMnj1btT1gRUUFxo4dq+rr\nyc3NRWFhIYBH09ieXPktNDRUbaDXsGHDMGrUKLX3unLlCkpLS5v9DQ4ODmpT3MaMGQMPDw/U1NQg\nNzcXYrEYDg4OOHToEKZMmaIK1X79+iE1NRXl5eWtnv9taGiITz/9FPr6+pDL5XBycsLMmTPRt29f\nWFlZtfp8RESt5ebmhtjYWAQGBuLMmTNYv349zM3NWTCdPdABPHfZ1Jbk5+fj0KFDWLx4MQoKCjBv\n3jx8//33GDhwICorK9X65a9du4bQ0FAolUpERUUhJydH9Zi5ublaC0FLzf/5+fm4d++e2jFBEDB8\n+HC1Y+Xl5ZDJZOjbty8MDAwQEhKC7Oxs1NTUqM2H19bWVi0X21qCIMDX1xebN2+GlZUVm8qJ6HfR\nrVs3bN26FRcuXICXlxfef/99eHh4cA+GzhzoWlpaqKmpafVOWufOnYOzszOCg4PR1NSEhw8f4osv\nvoCPjw969OiBkydPQqlUQkdHBzNnzsTMmTNVgfukqVOnPrcPf+rUqS0uh/hkmCqVSgQFBWHPnj14\n9dVX8fhG5dy5c+jVqxcuXbqkem5paSkcHBzaNHagoqICgwYN4taHRKQRXFxcEB0djeDgYJw+fRrv\nvfdes2m11DmI9fT0VIO8XlRdXR3eeecdBAUFITw8HEePHkVoaCjKyspw8+ZNDBgwAKNHj0ZERATq\n6+sBAA8fPsSRI0daPNfDhw/Vfn7p8cj4J3+kUikyMzPVas4KhQJKpVLtmLa2NqytrXHw4EGUlZUB\nAJKTkzF69Og2La1YVVXFldiISKPIZDJs2rQJ3t7eWLZsmapCRZ2shm5kZITq6mq1zU+eJzk5Gebm\n5mq1VJlMBm9vb8TExMDBwQG7d+/GwoULcf78eQwdOhRXrlyBjo4OJkyYAEEQVP3ov5w+pquri5CQ\nELV+/bfeegsrVqxo8SJ+TEdHB6tWrcKCBQuwadMmFBYWIiIiAnFxcbCwsMDSpUvh5+eH0aNH4513\n3kFaWlqbCqy0tJT7FxORRho1ahQOHz6M/fv3IyYmBhs3boSlpSULppMQrVixQliyZAkcHBxe+EVl\nZWVQKBRqu5kBj0aZPz4uFotRXFyMjIwM3L9/H9bW1hg8eDDKy8tVyxgWFBQ8dae21vbpP66R37t3\nD9evX4euri7s7e1VA0Xq6upw69YtZGVlwd7evk27ywFASEgIunbtirlz5/LqISKNlZycjA8//BC+\nvr7w9PRsVhHh5iwdi1wuh2TMmDGtbnKXyWQtbrnZrVs3tVWMTE1Nm2208mRLQFtD9al3JyIR+vfv\n3+L2o7q6uhg5ciRGjhz5Uu9RVlYGFxcXXj1EpNHs7OwQGhqKsLAw+Pn5YevWrRgwYEC7+ftra2tx\n/PhxVT6JxWJYW1vDzs4Oenp6yMnJwenTp+Ho6Ag7OzvV66qrqxEZGQkzMzNMmTIFGRkZSEtLwx/+\n8IcW36exsRG5ubno06ePWgUzJycHCoUCBgYG6NWrV7uYeizu2bOn2spn9HRKpRK3bt1q88p6RES/\nJUNDQ6xYsQLvvfcetmzZgsOHDz9zcylNC/Tdu3fjwYMHEIvFUCqV+OSTT7Bp0yYAwP379+Hn54ew\nsDC1qdC3bt3CG2+8gaioKACPtr3+6quvnvo+SUlJ6Nevn2rgtSAIOHLkCFxdXbFz505YWFi0m1YM\nsampKRISEnjlv4C6ujpkZWW1eSEeIqLfmkgkgo2NDfbv34+amhr4+PggIyMDWlpaakGoiRQKBV5/\n/XUsWrQIfn5+2LZtG44dO4aSkhLVc44fP44HDx6ofo+KisLgwYNV0/dEIlGLXbiFhYWYP39+sxbX\nhoYGzJ8/HxEREQgODkZ8fDy2bt2KiooKzQ/0oUOHIikpqd3ctf2eioqKYG9v32J3AxGRJpNKpVi6\ndCkCAgLw4YcfIiQkROMrJxKJBOXl5aioqEB+fj6Sk5MxaNAgGBsbAwC8vLzw2muv4fLlywAedYke\nO3YMa9asee65u3Xrhs8//xznz59vdgMEAHl5eaioqMDPP/+MCRMmtGkBtt+8vLS1teHg4ICsrCwM\nGTKk1U0iAQEBUCgUAB6tNzxixAh4eHjA1NQUGRkZ+PLLLzFlyhTV3HDg0QIwO3bswNChQ+Hj44P0\n9HTExMRgw4YNL9VPoVQqce3aNdjZ2alWnxMEAampqXjw4AGMjIzg4OCg2vmsta5evfrSffBERL9n\nbV0ul+PgwYMIDw/X+EVoTExMMHHiROjq6qpWG92/f7/q/3CpVAp3d3fExMTA09MTd+7cgZOTE/r0\n6YP09PTn3iyYm5ur1fYfHz958iSmT5+OHj16oKCgACdPnmwXi4iJRSIRZs6ciaysrFa/uLGxEX/9\n61/h5OSEGTNmwMPDA0lJSVi2bBkaGxtRUVGB3bt3Y+/evWqvS09PR0BAAG7fvg1BEFBVVYXY2NiX\n+iANDQ2IjY3F2rVr1Qb5ff3111i0aBHy8/Pxl7/8BTt27GjT+RsbG5GamoqxY8fyfwUiate0tbUx\ne/ZsVWVMU5WUlODKlSu4f/8+Hj58iJSUFKxatQrXrl0DANTX12PkyJH4/PPPUVZWhhMnTsDLy0tt\nOfHWqqysxPTp0xEREYHr168jMjIS06dPb3H5cY0LdAAYMmTIMwcNPM+YMWMwYcIETJw4EStXrsTx\n48dVixrIZDJkZmbizp07qud///33cHV1/dU+RHl5ObS1tbFixQq1JqQHDx7A398f//73v7Fw4UIE\nBgbi9u3bbVrytbq6GvHx8a2ar09ERC/fqiASidClSxeMGDECY8aMQWFhoao119TUFFu3bsXBgwdx\n6tQpjB079qXGBty4cQNubm7w8vKCubk5XnnlFcjl8naxu50EeDR/raioCFlZWS1O+XqevLw81Z1N\nbGwsFi9erFpNrXv37vDz88OPP/6IAQMGoLS0FGFhYVi+fPlzBxk0NDS0uOSrRCJRayoyMTGBQqFA\nVlYWfHx81AJ9wIABKCoqQnp6OgwNDbFv3742zXG/ePEiZsyY0eolcomIqG10dHRU+3g0NDTgp59+\nwrVr1zBgwACUl5ernjdp0iS4urpi5cqVLz3GqXfv3oiPj0diYiKcnJxw/fp1ZGZmNpuCrbGBLpFI\nsGzZMly9erXVgd6zZ09s3rwZenp6EAQBN2/exOrVq1VBrKurCxcXF3z00Ud4/fXXkZ6ejtmzZ8PK\nygrJycnPPPe+ffvg7+/frDZ++fJlODk5qR2XSqXQ1dVVuwEoLi5GfHw8AgMDMXnyZPzjH/9AdHQ0\ndu3a1aq++sbGRsTFxWHJkiX8F0ZE9BsQi8VwdnZGYGAgmpqaIBKJ0Lt3b8TGxkIulyMlJQX9+vUD\n8KiV2dvbG56enpBIJNDV1UWPHj1UGfSsXNPW1sbMmTNVv1taWuLUqVPYuHEjEhISMG7cOPzwww/t\nY7qy8P9yc3OFSZMmCbW1tcKLqqqqEgAIubm5qmM5OTmCs7OzEB8fLyQlJQm2traCUqkUTE1NhYyM\nDGHr1q3C6dOnhaioKGHz5s1CY2OjcOHCBcHd3V1oampqdv6SkhK1n+LiYkGpVLb499y5c0cYMWKE\nUFZWJgiCIMTHxwsAhMrKSkEQBCEzM1MwMTERsrOzhdbIzs4WPDw8hLq6OoGIqCOoqakR5s6dy4J4\nCoVCIeTl5QkKhaJd/L3W1taC5MmatouLC65evdrq/u0na8UWFhbo3r07qqurVf3Z2traeP/99xER\nEYF//etfWLlyJS5evPjc8yYlJeHmzZvN3svT0/OFVjzq3r07TExMVM3/PXr0QHl5eYvN+M+44cG3\n336Lt99++6UGWhARUfshlUrb3ZojkiebN+bNm4dt27Zh9OjRrZradenSJWRlZaG+vh6XL19Geno6\nbG1tkZ+fr3qOq6sr7O3tsW7dOpiZmb3QeY2MjNCzZ89mAfuihWxpaQlHR0ecOHECLi4uiIyMhK+v\nL/r06fPCn62srAwHDhxoNleRiIhIIwMdAAYOHAgDAwOkpKTA0dHxuS/W0tLCnj17kJ2djXv37kEk\nEkEmk+GHH36AhYUFFAqFqt956NCh2LFjB8aNGwcA6NWrl2r93a5du7a4zq6Tk1OzvvJn0dPTw9Kl\nS1XzBfX19bFv3z4cPHgQ//znP+Hq6oo9e/a0au7l0aNHsX79ehgYGPBqISIijSUSfjG+/8aNG9i8\neTMOHz7cYbYJFQQBdXV1rW4+ycnJwaxZs/Cf//wH+vr6vFqIqMPgbmsdi1wuR7Oq6pAhQzBq1Cic\nOXOm49y1iERt6gvZvXs3du3axTAnIiKNJ24p/P70pz9h165dqqX2OqOLFy+iuroabm5uvEqIiKj9\nBToAmJubY9OmTdixYwcaGho6XaGUlJRgxYoV2LJlS7vYA5eIiOipo8M8PDygq6uL6OjoTlUgTU1N\n+PjjjxEQEIC+ffvyCiEiovYd6FpaWtiwYQN27tyJzMzMTlMgR48ehZ6eHqZOncqrg4iI2n+gA4/W\nSA8MDMTbb7+NgoKCDl8Yly5dwqFDh7B+/Xo2tRMRUccJdACwtbXF+vXrsXLlSrXF8Dua9PR0rFmz\nBkFBQTAxMeGVQUREHSvQAeDVV1+Fr68v1qxZg6qqqg5XCHfu3MHixYsRFhYGCwsLXhVERNQxAx0A\nZs+ejUmTJmH58uUoKirqMAWQlpaGBQsWICQkBHK5nFcEERF17EAHAB8fH/j4+OCPf/wjsrOz2/2H\nv3jxIpYuXYrQ0FDVMrRERETtkaS1L/D09IShoSHmzZuHwMDAF1rzXdM0NDQgMjISISEhOHLkiGpP\nXSIiok5RQ39s/Pjx2L9/P/z9/bF//34olcp284FLSkqwceNGJCYm4vDhwwxzIiLqvIEOADY2NggP\nD0deXh5WrVqFe/fuafyHTUxMhIeHB5ycnLB9+3aYm5vzCiAiog5B8jIvNjY2hr+/P7777jt4enpi\nzZo1WLBggcZtNZqbm4tPP/0Ut27dwtdff41BgwZxnjkREbGG/iQtLS1MnjwZCQkJqKurg4uLC86e\nPasRzfClpaU4cOAA3NzcMGHCBERGRmLw4MEMcyIiYqA/jUwmw+rVqxEZGYmzZ8/CxcUFp06dQm1t\n7W/+oQoLC7Fv3z7Y2dmhvr4eV69exYwZM6Ctrc1vnIiIOiTJr31CKysr7NixA9nZ2YiKisL48ePx\n1ltvwdHREYMGDYJYLP7VP4QgCKivr0dycjLOnTuHyMhIbNy4EampqTA2Nua3TEREHZ5IEAThf/kG\nFRUVSElJQWhoKHJycrBw4UJYW1tj2LBh6NKlCySS1t9TCIKAhoYGVFVVISUlBT/99BPCwsIwffp0\neHp6wtbWtk3nJSLqLBQKBRYtWoTw8HAWRgcgl8v/94H+pMLCQmRkZCA2NhbXrl1D79694ezsDGNj\nY3Tr1g09evSAVCqFnp4e9PT00NTUhJqaGtTW1qK6uhr3799HRUUFCgoKcP78eQDAuHHjMH78eMjl\nco0bjEdEpMmB7u3tjSNHjqChoYEF0o6JxWIMHz78tw30X8rNzUVNTQ1qamqQnp6Ou3fvoqysDNXV\n1airq4NYLIZUKoWhoSFMTU0xePBgWFtbo0uXLpDJZDAzM+M3SUTUBrW1tViyZAkEQeBA4Q7AyMjo\n9w10IiIi+pVq6iwCIiIiBjoREREx0ImIiIiBTkRERAx0IiIiBjoREREx0ImIiIiBTkRERAx0IiIi\nBjoREREx0ImIiIiBTkRERAx0IiIiBjoREREx0ImIiOh/Q8IiICKi9iwtLQ1hYWGQSJpHmpubGyZP\nnsxAJyIi0nSFhYXYuXNni49ZWlp2mnJgkzsREREDnYiIiBjoRERExEAnIiIiBjoREXVwgiB0ms8q\nEjrTpyUiog4nPz8fqampEIlEzR7r27cvBg8ezEAnIiKi9oFN7kRERAx0IljkBEAAAAaPSURBVCIi\nYqATERERA52IiIge4VruRETUriUmJuKDDz6Arq6u2nFBEODt7Y158+Yx0ImIiDRddXU1zpw50+Jj\nU6ZM6TTlwCZ3IiIiBjoREREx0ImIiIiBTkRERAx0IiLq4Lg5CxERUTtRUVGB4uLiFjdnMTQ0hJmZ\nGQOdiIiI2gc2uRMRETHQiYiIiIFOREREvwou/UpERO1aXV0dioqKWhwUZ2RkBCMjIwY6ERGRprt4\n8SLc3d1hYWHR7LEtW7bAz8+PgU5ERKTpmpqaAAC5ubnNHmtsbOw05cA+dCIiIgY6ERERMdCJiIiI\ngU5ERESPcFAcERG1azo6OrCxsYGBgUGzx/T09DpNOXAtdyIiateUSiWqq6tbfEwqlUIqlTLQiYiI\nqH1gHzoREREDnYiIiBjoRERExEAnIiIiBjoREVGHwXnoRESdUH19PaKjo6GlpcXC6ABEIhGnrRER\ndUYKhQITJ07E3r17O9WOZB2RWCyGl5cXa+hERJ1Vnz59YG9vz4LoAPT09NiHTkRE1CFq6iwCIiIi\nBjoREREx0ImIiIiBTkRERAx0IiIiBjoREREx0ImIiIiBTkRERAx0IiIiBjoREREx0ImIiIiBTkRE\nRAx0IiIiBjoREREx0ImIiIiBTkRERAx0IiIiBjoREREx0ImIiIiBTkRERAx0IiKijk7CIiAiIk1T\nX1+PH3/8EUql8lHtUyyGpaUlLCwsoKWlhdLSUqSmpsLKygp9+vRRvU6pVOLKlSswNDTEiBEjkJ+f\nj7y8PDg5OT31vR4+fAiFQgGZTKZ2rLGxERKJBF26dGENnYiIqC2qqqqwevVqBAcHIzw8HAcOHMCo\nUaPw2WefAQB+/vlnvPLKKwgJCVF7XUZGBlxdXREcHAwASEtLwwcffPDU9xEEAdu2bcObb76pOpae\nng57e3vY2Nhg2rRpuH37NgOdiIiorWpqavDxxx8jMDAQBw4cwPHjx/H3v/8dlZWVEIlEAIBTp06h\nuLhY9Zq4uDiYmppCS0sLACASiaCtrf3U94iJicFnn30GfX19AEBDQwP+/Oc/w9/fHykpKVi+fDnW\nrVuH2tpaBjoREVGbAkoshkKhgFKpRHV1NSorKyGTyVRN4JMmTYKzszNu3LgBAFAoFDh27Bg2bNjw\nQufPysrCRx99hC+//FJ1LDs7G7m5uZg8eTKMjY3h7u6OqKgoVFVVaXx5sQ+diIg0kkwmw9q1a2Fs\nbIz8/HxcuHABYWFh0NXVBQCYmZlh8uTJOHPmDMaPH4+7d+/C2NgYw4YNQ05OzjPPXVdXh82bN2P3\n7t0Qi8UQBAEAUFBQALlcDlNT00chKZHA0dERpaWlMDc3Z6ATERG1VmVlJf72t7+hf//+UCqVSE5O\nxvTp01UD3JRKJUaPHo25c+fi3XffxenTp+Hr6wsdHZ3nnvurr77CkCFDMHz4cFy9ehX19fVQKBQA\noGquf6y+vh5iseY3aDPQiYhIIzU0NEAmk6lqy7169YK7uzvu3r0LU1NTCIIAMzMzrF27FtHR0Th5\n8iTCwsKQmZn5zPMqFAocPXoUqampiI6ORkVFBf773/8iLy8PQUFBKCgoQFVVFQwNDdHQ0IDr16/D\nxMRE48uLfehERKSZNU6JBGVlZSgpKcGDBw+QmJiI7777Dr169VI1kUskEnh4eMDb2xu9e/dG9+7d\nn3teqVSKEydOICsrC3Fxcdi7dy/mzJmDqKgoWFlZISMjAwkJCQCAxMREzJkzB4aGhqyhExERtUVT\nUxOcnZ2hr6+P2tpa2NjY4Ntvv4WtrS2SkpKgUCjQ1NSEkSNHAgCmTZuGLl26oKmpSTV/vampCdXV\n1WrnFYlEkEqlqt91dHTQ2NgIPT09AMD58+fh6OgIV1dXZGRkICYmRu35mkokPL7NISKiTkOhUGDR\nokUIDw/X6EB/MqJEIpGqL1sQBAiCoPq9qakJIpEIIpFI7bFfPq8lLT1HoVAgKysLlpaWMDIy0vjv\nUy6Xs4ZORESa6Vkh/Di8W3ruk4/98nkvci4A0NPTg42NTfsqL14yREREHeAGiEVARETEQCciIiIG\nOhERETHQiYiIiIFORETEQCciIiIGOhERETHQiYiIiIFORETEQCciIiIGOhERETHQiYiIiIFORETE\nQCciIiIGOhERETHQiYiIiIFORETEQCciIiIGOhERETHQiYiIiIFORETNiUQiFkJH+j4FQRBYDERE\nnYtCoYC+vj7kcjkYA+3f7du38X/xkJi4JIUbbwAAAABJRU5ErkJggg==\n"
    }
   },
   "cell_type": "markdown",
   "id": "a58257e5",
   "metadata": {},
   "source": [
    "![graph.png](attachment:0faae7b6-9d83-4035-a009-9db4a05730ed.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dae08e90",
   "metadata": {},
   "source": [
    "<a id=\"create_graph_nodes\"></a>\n",
    "## Create Nodes\n",
    "\n",
    "Each node is represented as an integer so it is possible to determine the number of nodes that are needed. One node is required for each patient. For the age and BMI metrics, there is a single node for each integer value in the range of observed values in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e31a9549",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of patient nodes   : 442\n",
      "Number nodes for age :  61\n",
      "Number nodes for bmi :  26\n",
      "Total number of nodes: 529\n"
     ]
    }
   ],
   "source": [
    "n_patients = len(df)\n",
    "n_age = np.ptp(df['age']) + 1\n",
    "n_bmi = np.ptp(df['bmi']) + 1\n",
    "n_total = n_patients + n_age + n_bmi\n",
    "\n",
    "print('Number of patient nodes   : %3d' % n_patients)\n",
    "print('Number nodes for age : %3d' % n_age)\n",
    "print('Number nodes for bmi : %3d' % n_bmi)\n",
    "print('Total number of nodes: %3d' % n_total)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eda23dd",
   "metadata": {},
   "source": [
    "The data is encoded in the following order:\n",
    "\n",
    "- patient nodes\n",
    "- age nodes\n",
    "- followed by the BMI nodes\n",
    "\n",
    "Nodes zero to `n_patients - 1` represent each patient. Nodes `n_patients` to `n_patients + n_age - 1` represent each age in increasing order. Finally, nodes `n_patients + n_age` to `n_total - 1` represent each BMI value in increasing order. The offsets for the nodes are computed in the next cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c6549bea",
   "metadata": {},
   "outputs": [],
   "source": [
    "offset_age = n_patients\n",
    "offset_bmi = n_patients + n_age"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20c0c4c6",
   "metadata": {},
   "source": [
    "The next cell creates a dataframe where the index is the node number and the `Entity` column. The entities are things like the patient ID, a specific age, or a specific BMI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "953ee983",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Node ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>Patient ID 389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>age 49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>Patient ID 421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>Patient ID 308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>Patient ID 302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>Patient ID 381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>Patient ID 352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Patient ID 189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>age 42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>age 50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Entity\n",
       "Node ID                \n",
       "389      Patient ID 389\n",
       "472              age 49\n",
       "421      Patient ID 421\n",
       "308      Patient ID 308\n",
       "302      Patient ID 302\n",
       "381      Patient ID 381\n",
       "352      Patient ID 352\n",
       "189      Patient ID 189\n",
       "465              age 42\n",
       "473              age 50"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explanations = []\n",
    "\n",
    "for pid in range(n_patients):\n",
    "    explanations.append((pid, 'Patient ID %d' % pid))\n",
    "\n",
    "for i in range(n_age):\n",
    "    vid = offset_age + i\n",
    "    age = df['age'].min() + i\n",
    "    explanations.append((vid, 'age %d' % age))\n",
    "\n",
    "for i in range(n_bmi):\n",
    "    vid = offset_bmi + i\n",
    "    bmi = (i + df['bmi'].min())\n",
    "    explanations.append((vid, 'bmi %.d' % bmi))\n",
    "\n",
    "explanation_to_node = dict([(y, x) for x, y in explanations])\n",
    "\n",
    "node_explanations = pd.DataFrame(explanations).rename(columns = {0: 'Node ID', 1: 'Entity'}).set_index('Node ID')\n",
    "node_explanations.sample(10, random_state = 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b3132b4",
   "metadata": {},
   "source": [
    "<a id=\"create_graph_edge\"></a>\n",
    "## Create Edges\n",
    "\n",
    "As shown in the bipartite graph, the patient nodes are connected to the age and BMI nodes. However, a patient with a BMI of 16 does not have an edge between that patient and the BMI 16 node. It also has edges to the adjacent nodes, BMI 15 and BMI 17. This is done because the BMI metric was continuous in the dataset but it needs to be represented as discrete values in a graph.\n",
    "\n",
    "The next cell creates the `link_to_neighborhood()` function. It computes which edges need to be connected between nodes. It does some checking to ensure that edges are not connected to nodes that don't exist. For example, if the patient ages ranged from 20 to 80 years old, there would be nodes from 20 to 80. If you want to create the edges for a 20 year old patient, there would be no node for Age 19. The neighborhood for that patient would be Age 20 and Age 21, not Age 19, Age 20, and Age 21."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1a684f7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def link_to_neighborhood(pid, patient, feature):\n",
    "    feat_min = max(df[feature].min(), int(patient[feature]) - 1)\n",
    "    feat_max = min(df[feature].max(), int(patient[feature]) + 1)\n",
    "    for value in range(feat_min, feat_max + 1):\n",
    "        if feature == 'age':\n",
    "            age_id = explanation_to_node['age %d' % value]\n",
    "            adj[pid, age_id] = 1; adj[age_id, pid] = 1\n",
    "        elif feature == 'bmi':\n",
    "            bmi_id = explanation_to_node['bmi %d' % value]\n",
    "            adj[pid, bmi_id] = 1; adj[bmi_id, pid] = 1\n",
    "            \n",
    "adj = np.zeros((n_total, n_total))\n",
    "for pid, patient in df.iterrows():\n",
    "    link_to_neighborhood(pid, patient, 'age')\n",
    "    link_to_neighborhood(pid, patient, 'bmi')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5285b4e",
   "metadata": {},
   "source": [
    "<a id=\"create_graph_save\"></a>\n",
    "## Save Graph Data\n",
    "\n",
    "You can load graphs into PyPGX using configuration files, or in a [configuration free manner](https://docs.oracle.com/cd/F41824_01/latest/prog-guides/graph-management/configless-loading.html). The configuration file is demonstrated in this notebook, and requires a few files that describe the vertices and edges. The next few cells write describe the vertices and we call them vertex providers. Each file contains a column header and then each row contains a vertex id (`v_id`) and vertex properties (e.g., nodes of type patient have `age` and `bmi`). We start with the file that describes the patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c271a7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>v_id</th>\n",
       "      <th>age</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>72</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   v_id  age  bmi\n",
       "0     0   59   32\n",
       "1     1   48   21\n",
       "2     2   72   30\n",
       "3     3   24   25\n",
       "4     4   50   22"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA_DIR = tempfile.mkdtemp()\n",
    "\n",
    "# write the patient nodes to a file\n",
    "patients_path = os.path.join(DATA_DIR, 'patients.csv')\n",
    "with open(patients_path, 'w') as fw:\n",
    "    fw.write('v_id,age,bmi\\n')\n",
    "    for i in range(n_patients):\n",
    "        fw.write('%d,%d,%d\\n' % (i, df.loc[i]['age'], df.loc[i]['bmi']))\n",
    "pd.read_csv(patients_path)[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d93fe78",
   "metadata": {},
   "source": [
    "Next we create a file that describes nodes that correspond to different ages. Each row has a vertex id (`v_id`) and the actual age value as a property."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "00e12362",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>v_id</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>442</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>443</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>444</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>445</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>446</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   v_id  age\n",
       "0   442   19\n",
       "1   443   20\n",
       "2   444   21\n",
       "3   445   22\n",
       "4   446   23"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# write the age nodes to a file\n",
    "ages_path = os.path.join(DATA_DIR, 'ages.csv')\n",
    "with open(ages_path, 'w') as fw:\n",
    "    fw.write('v_id,age\\n')\n",
    "    for i in range(n_age):\n",
    "        fw.write('%d,%d\\n' % (offset_age + i, df['age'].min() + i))\n",
    "pd.read_csv(ages_path)[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73e88af6",
   "metadata": {},
   "source": [
    "Similarly we create another file that describes the BMI nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0efab75b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th></th>\n",
       "      <th>v_id</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>503</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
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       "      <td>19</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>506</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>507</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   v_id  bmi\n",
       "0   503   17\n",
       "1   504   18\n",
       "2   505   19\n",
       "3   506   20\n",
       "4   507   21"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# write the bmi nodes to a file\n",
    "bmis_path = os.path.join(DATA_DIR, 'bmis.csv')\n",
    "with open(bmis_path, 'w') as fw:\n",
    "    fw.write('v_id,bmi\\n')\n",
    "    for i in range(n_bmi):\n",
    "        fw.write('%d,%d\\n' % (offset_bmi + i, df['bmi'].min() + i))\n",
    "pd.read_csv(bmis_path)[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "881d74a9",
   "metadata": {},
   "source": [
    "An edge file is needed to define the links between two nodes. In our example, we have different types of nodes (patient nodes, age nodes and BMI nodes). It is a good practice to create edge files that are dedicated to certain type of links. In this notebook, we create one edge file that is dedicated to patient to age nodes links, and another edge file that is dedicated to patient to BMI node links.\n",
    "\n",
    "The next cell creates the patient-to-age edge file. Similarly to the vertex files, the edge file is also in CSV format and it starts with a header and then each row has edge id (`e_id`), source vertex id and destination vertex id. The source and destination vertex ids must match with the ones we used in the vertices files. In this example, the vertex ids, as defined in the vertices file, are integers. You could use more meaningful node ids of a different type (e.g., string)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d4b0e908",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>e_id</th>\n",
       "      <th>patient_vid</th>\n",
       "      <th>age_vid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   e_id  patient_vid  age_vid\n",
       "0     0            0      481\n",
       "1     1            0      482\n",
       "2     2            0      483\n",
       "3     3            1      470\n",
       "4     4            1      471"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# write the edge file for patient-to-age nodes to a file\n",
    "e_id = 0\n",
    "patient_to_age_path = os.path.join(DATA_DIR, 'patient_to_age.csv')\n",
    "with open(patient_to_age_path, 'w') as fw:\n",
    "    fw.write('e_id,patient_vid,age_vid\\n')\n",
    "    for i in range(n_patients):\n",
    "        patient_vid = i\n",
    "        for j in range(n_age):\n",
    "            age_vid = offset_age + j\n",
    "            if adj[patient_vid, age_vid] == 1:\n",
    "                fw.write('%d,%d,%d\\n' % (e_id, patient_vid, age_vid))\n",
    "                e_id += 1\n",
    "pd.read_csv(patient_to_age_path)[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c80fc34f",
   "metadata": {},
   "source": [
    "Similarly we create an edge file that is dedicated to links between patient and age nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d398f193",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>e_id</th>\n",
       "      <th>patient_vid</th>\n",
       "      <th>bmi_vid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   e_id  patient_vid  bmi_vid\n",
       "0     0            0      517\n",
       "1     1            0      518\n",
       "2     2            0      519\n",
       "3     3            1      506\n",
       "4     4            1      507"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# write the edge file for patient-to-age nodes to a file\n",
    "e_id = 0\n",
    "patient_to_bmi_path = os.path.join(DATA_DIR, 'patient_to_bmi.csv')\n",
    "with open(patient_to_bmi_path, 'w') as fw:\n",
    "    fw.write('e_id,patient_vid,bmi_vid\\n')\n",
    "    for i in range(n_patients):\n",
    "        patient_vid = i\n",
    "        for j in range(n_bmi):\n",
    "            bmi_vid = offset_bmi + j\n",
    "            if adj[patient_vid, bmi_vid] == 1:\n",
    "                fw.write('%d,%d,%d\\n' % (e_id, patient_vid, bmi_vid))\n",
    "                e_id += 1\n",
    "pd.read_csv(patient_to_bmi_path)[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7838ed51",
   "metadata": {},
   "source": [
    "Configuration files, in JSON format, are used to define how the graph is read. A typical configuration file defines the graph schema and paths to the vertices and edges files. The configuration file identifies the path to the vertices files with the `vertex_providers` parameter. It is a list whose elements describe each vertex provider. Each element of the list needs to have certain properties such as `name`, `format`, `uris` and `key_column` parameter identifies what column in the file contains the information to uniquely identify the vertex. Additionally, it can have properties such as `header` indicating whether the file has a header and `props` describing the node properties. Simiarly, we also have `edge_providers` parameter to define how to process the edge files. The most notable parameters of each edge provider are `source_vertex_provider` and `destination_vertex_provider` which correspond to the names of the vertex providers, `key_column` that is used to uniquely identify the edge and `source_column` and `destination_column` identify which columns denote the source and destination vertices for the edges. Since we want a bidirectional graph, we create two more edge providers where the source and destination columns are swapped. The next cell defines the configuration and writes to the `config.json` configuration file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5dac5172",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the graph configuration\n",
    "config = {\n",
    "    \"name\": \"graph\",\n",
    "    \"vertex_providers\": [{\n",
    "        \"name\": \"patientsProvider\",\n",
    "        \"format\": \"csv\",\n",
    "        \"uris\": [\"patients.csv\"],\n",
    "        \"key_column\": \"v_id\",\n",
    "        \"header\": True,\n",
    "        \"props\": [{\n",
    "            \"name\": \"age\",\n",
    "            \"type\": \"double\",\n",
    "            \"column\": \"age\"\n",
    "        },{\n",
    "            \"name\": \"bmi\",\n",
    "            \"type\": \"double\",\n",
    "            \"column\": \"bmi\"\n",
    "        }]\n",
    "    },{\n",
    "        \"name\": \"agesProvider\",\n",
    "        \"format\": \"csv\",\n",
    "        \"uris\": [\"ages.csv\"],\n",
    "        \"key_column\": \"v_id\",\n",
    "        \"header\": True,\n",
    "        \"props\": [{\n",
    "            \"name\": \"age\",\n",
    "            \"type\": \"double\",\n",
    "            \"column\": \"age\"\n",
    "        }]\n",
    "    },{\n",
    "        \"name\": \"bmisProvider\",\n",
    "        \"format\": \"csv\",\n",
    "        \"uris\": [\"bmis.csv\"],\n",
    "        \"key_column\": \"v_id\",\n",
    "        \"header\": True,\n",
    "        \"props\": [{\n",
    "            \"name\": \"bmi\",\n",
    "            \"type\": \"double\",\n",
    "            \"column\": \"bmi\"\n",
    "        }]\n",
    "    }],\n",
    "    \"edge_providers\":[{\n",
    "        \"name\": \"patientsToAgesProvider\",\n",
    "        \"format\": \"csv\",\n",
    "        \"uris\": [\"patient_to_age.csv\"],\n",
    "        \"source_vertex_provider\":\"patientsProvider\",\n",
    "        \"destination_vertex_provider\":\"agesProvider\",\n",
    "        \"key_column\": \"e_id\",\n",
    "        \"source_column\": \"patient_vid\",\n",
    "        \"destination_column\": \"age_vid\",\n",
    "        \"header\": True\n",
    "    },{\n",
    "        \"name\": \"agesToPatientsProvider\",\n",
    "        \"format\": \"csv\",\n",
    "        \"uris\": [\"patient_to_age.csv\"],\n",
    "        \"source_vertex_provider\":\"agesProvider\",\n",
    "        \"destination_vertex_provider\":\"patientsProvider\",\n",
    "        \"key_column\": \"e_id\",\n",
    "        \"source_column\": \"age_vid\",\n",
    "        \"destination_column\": \"patient_vid\",\n",
    "        \"header\": True\n",
    "    },{\n",
    "        \"name\": \"patientsToBmisProvider\",\n",
    "        \"format\": \"csv\",\n",
    "        \"uris\": [\"patient_to_bmi.csv\"],\n",
    "        \"source_vertex_provider\":\"patientsProvider\",\n",
    "        \"destination_vertex_provider\":\"bmisProvider\",\n",
    "        \"key_column\": \"e_id\",\n",
    "        \"source_column\": \"patient_vid\",\n",
    "        \"destination_column\": \"bmi_vid\",\n",
    "        \"header\": True\n",
    "    },{\n",
    "        \"name\": \"bmisToPatientsProvider\",\n",
    "        \"format\": \"csv\",\n",
    "        \"uris\": [\"patient_to_bmi.csv\"],\n",
    "        \"source_vertex_provider\":\"bmisProvider\",\n",
    "        \"destination_vertex_provider\":\"patientsProvider\",\n",
    "        \"key_column\": \"e_id\",\n",
    "        \"source_column\": \"bmi_vid\",\n",
    "        \"destination_column\": \"patient_vid\",\n",
    "        \"header\": True\n",
    "    }]\n",
    "}\n",
    "\n",
    "# write the graph configuration to a file\n",
    "config_path = os.path.join(DATA_DIR, 'config.json')\n",
    "with open(config_path, 'w') as fw:\n",
    "    json.dump(config, fw, indent = 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfd9564d",
   "metadata": {},
   "source": [
    "<a id=\"create_graph_load\"></a>\n",
    "## Load Graph Data\n",
    "\n",
    "You use the [`.read_graph_with_properties()`](https://docs.oracle.com/cd/F41824_01/latest/api/api.html#pypgx.api.PgxSession.read_graph_with_properties) method to read in the configuration file. The method returns a [PgxGraph](https://docs.oracle.com/cd/F41824_01/latest/api/api.html#pypgx.api.PgxGraph) object. The graph configuration file is used to determine the location of the nodes and edges files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3aec857b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PgxGraph(name: graph, v: 529, e: 5290, directed: True, memory(Mb): 0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph = session.read_graph_with_properties(config_path)\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "425d9b63",
   "metadata": {},
   "source": [
    "<a id='built-in'></a>\n",
    "# PyPGX Built-in Algorithms\n",
    "\n",
    "Graph machine learning techniques open the door for the discovery of complex relationships in your data. Often, standard data analytical techniques don't have the flexibility to discover these relationships. The [Parallel Graph AnalytiX (PGX)](https://www.oracle.com/middleware/technologies/parallel-graph-analytix.html) library provides access to powerful graph analytics functionality. It supports a broad spectrum of analytics capabilities such as Graph Machine Learning (for example, DeepWalk, [GraphSAGE](http://snap.stanford.edu/graphsage/), Deep Graph Infomax, and many more). PGX has more than 60 built-in graph algorithms such as [PageRank](https://en.wikipedia.org/wiki/PageRank), InfoMap, Centrality, and others. Computational performance is baked into PGX as its algorithms are optimized and parallelized. It is written in Java and has Python bindings in the PyPGX product.\n",
    "\n",
    "This notebook would be very long if all of the algorithms were discussed in detail and demonstrated. Instead, this notebook demonstrates how to call several of the popular graph techniques including [Personnalized PageRank](https://docs.oracle.com/cd/F41824_01/latest//api/api.html#pypgx.api.Analyst.personalized_pagerank), [Eigenvector Centrality](https://docs.oracle.com/cd/F41824_01/latest/api/api.html#pypgx.api.Analyst.eigenvector_centrality), [Diameter](https://docs.oracle.com/cd/F41824_01/latest/api/api.html#pypgx.api.Analyst.diameter) / [Radius](https://docs.oracle.com/cd/F41824_01/latest/api/api.html#pypgx.api.Analyst.radius), [Local Clustering Coefficient](https://docs.oracle.com/cd/F41824_01/latest/api/api.html#pypgx.api.Analyst.local_clustering_coefficient), and [Communities Label Propagation](https://docs.oracle.com/cd/F41824_01/latest/api/api.html#pypgx.api.Analyst.communities_label_propagation). Calling these algorithms is straightforward using the [analyst](https://docs.oracle.com/cd/F41824_01/21.3.0/api/api.html?highlight=analyst#pypgx.api.Analyst) class."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a0bc3c",
   "metadata": {},
   "source": [
    "<a id='built-in_pagerank'></a>\n",
    "## Personnalized PageRank\n",
    "\n",
    "The Classical PageRank algorithm computes the ranking scores for the nodes using the network created by the incoming edges in the graph. It is intended for directed graphs, although undirected graphs can be treated by converting them into directed graphs with reciprocated edges. For example, keeping the original edge and creating a second one going in the opposite direction. The edges on the graph define the relevance of each node in the graph reflecting the relevance on the scores. Meaning that greater scores correspond to nodes with greater relevance.\n",
    "\n",
    "The Personalized Pagerank algorithm allows you to select a set of nodes and give them greater importance when computing the ranking score. This results in a personalized Pagerank score and can discover similar nodes. That is, it computes the important vertices relevant to the vertex of interest based on connectivity.\n",
    "\n",
    "\n",
    "Time Complexity $O(E * k)$ with $E$ = number of edges and $k$ is the number of iterations.\n",
    "\n",
    "Space Requirement $O(N)$ with $N$ = number of nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e49b8002",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 15.7 s, sys: 3.76 s, total: 19.5 s\n",
      "Wall time: 5.29 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "prs = []\n",
    "for patient in range(n_patients):\n",
    "    prs.append(analyst.personalized_pagerank(graph,\n",
    "                                             graph.get_vertex(patient),\n",
    "                                             damping = 0.9,\n",
    "                                             max_iter = 100,\n",
    "                                             tol = 1e-06))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82af0fac",
   "metadata": {},
   "source": [
    "<a id='built-in_eigenvector'></a>\n",
    "## Eigenvector Centrality\n",
    "\n",
    "The Eigenvector Centrality (also called eigencentrality) determines the centrality of a node by adding and weighting the centrality of its neighbors. It's a measure of the influence of a node in a network. You do this by assigning scores to all nodes based on the quality of the connection. So a connection to a high-scoring node contributes more to the score than a similar connection to a low-scoring node. Using outgoing or incoming edges when computing the eigenvector centrality is equivalent to using the normal or the transposed adjacency matrix respectively, leading to the \"right\" and \"left\" eigenvectors.\n",
    "\n",
    "For the graph, $G:=(V,E)$, with |V| number of vertices where $V$ is a set of vertices in $G$ and $E$ is the set of edges that connect the vertices in $V$. Let $A=(a_{i,j})$ be the adjacency matrix so that $a_{i,j}=1$ is the vertex, $i$, and is connected to the vertex $j$. Otherwise, the vertex is $a_{i,j}=0$.\n",
    "\n",
    "The relative centrality score for a vertex $i$ is defined as:\n",
    "$$x_i=\\frac{1}{\\lambda}\\sum_{j \\in G} a_{i,j}x_j$$\n",
    "\n",
    "The score can be written as an eigenvector equation:\n",
    "\n",
    "$$Ax = \\lambda x$$\n",
    "\n",
    "Eigenvector Centrality is a measure of the influence a node has on a network. If a node is pointed to by many nodes, which also have high eigenvector centrality, then that node has high eigenvector centrality.\n",
    "\n",
    "Time Complexity $O(N * k)$ with $N$ = number of nodes, $k$ <= maximum number of iterations\n",
    "\n",
    "Space Requirement $O(2 * N)$ with $N$ = number of nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3e69c1bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 317 ms, sys: 11.2 ms, total: 328 ms\n",
      "Wall time: 77 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "eigenvector_centrality = analyst.eigenvector_centrality(graph, \n",
    "                                                      tol=0.001, max_iter=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e78eb0b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(PgxVertex[provider=patientsProvider,ID=220], 0.0025747190948555115)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigenvector_centrality.get_values()[220]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eb63093",
   "metadata": {},
   "source": [
    "<a id='built-in_diameter'></a>\n",
    "## Diameter / Radius\n",
    "\n",
    "The diameter of a graph is the maximal value of eccentricity of all the vertices in the graph, while the radius is the minimum graph eccentricity. The eccentricity of a vertex is the maximum distance using the shortest paths to any other vertex in the graph. The family of eccentricity algorithms compute the distance between nodes. These algorithms compute the eccentricity of all the vertices and return the diameter or radius value depending on the request.\n",
    "\n",
    "\n",
    "Time Complexity $O(N * E)$ with $N$ = number of nodes, $E$ = number of edges\n",
    "\n",
    "Space Requirement $O(N)$ with $N$ = number of nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c1fac70",
   "metadata": {},
   "source": [
    "**Diameter**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7957b267",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 84 ms, sys: 8.68 ms, total: 92.7 ms\n",
      "Wall time: 39.5 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "diameter = analyst.diameter(graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dedc710f",
   "metadata": {},
   "source": [
    "**Radius**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0829ddc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 19 ms, sys: 0 ns, total: 19 ms\n",
      "Wall time: 5.87 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "radius = analyst.radius(graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d84d65e4",
   "metadata": {},
   "source": [
    "<a id='built-in_lcc'></a>\n",
    "## Local Clustering Coefficient\n",
    "\n",
    "A clustering coefficient is a measure of the tendency of nodes to cluster. While global metrics give an indication of the overall clustering, a local metric measures the degree to which a single node is clustered.\n",
    "\n",
    "The Local Clustering Coefficient of a vertex, $v$, is the fraction of connections between each pair of neighbors of $v$. Which is the fraction of existing triangles from all the possible triangles involving $v$, and every other pair of neighbor vertices of $v$. \n",
    "\n",
    "This is defined as a $G:=(V,E)$ graph with |V| number of vertices where $V$ is a set of vertices in $G$, and $E$ is the set of edges that connect the vertices in $V$. Let the edge $e_{i,j}$ connect vertices $v_i$ and $v_j$. The neighborhood $N_i$ for a vertex $v_i$ is defined as:\n",
    "\n",
    "$$N_i= \\left \\{ v_j : e_{i,j}\\in E \\vee e_{j,i} \\in E \\right \\}$$\n",
    "\n",
    "\n",
    "This implementation is intended for undirected graphs so is $e_{i,j} = e_{j,i}$. Which means that a vertex $v_i$ has $k_i$ neighbors, and a maximum of $\\frac{k_i(k_i-1)}{2}$ edges.\n",
    "\n",
    "The Local Clustering Coefficient for undirected graphs, $C_i$, is given with:\n",
    "\n",
    "$$C_i = \\frac{2|\\left \\{ e_{j,k} : v_j , v_k \\in N_i, e_{j,k} \\in E \\right \\} |}{k_i(k_i-1)}$$\n",
    "\n",
    "\n",
    "Nodes with a degree smaller than 2 are assigned a value of 0.\n",
    "\n",
    "Time Complexity $O(N ^ 2)$ with $N$ = number of nodes\n",
    "\n",
    "Space Requirement $O(N)$ with $N$ = number of nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8dca31e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 84.5 ms, sys: 685 µs, total: 85.2 ms\n",
      "Wall time: 18.6 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "local_clustering_coefficient = analyst.local_clustering_coefficient(graph, str(patient))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b77fce00",
   "metadata": {},
   "source": [
    "<a id='built-in_label'></a>\n",
    "## Communities Label Propagation\n",
    "\n",
    "Label Propagation is a semi-supervised machine learning algorithm designed to find community structures in a graph. When the algorithm starts, there tends to be a small set of nodes that are labeled. It assigns labels to unlabeled nodes during each iteration. The algorithm assigns a unique community label to each vertex in the graph. The vertex is updated on each iteration by looking and choosing the most frequent label from its neighbors. The small number of originally labeled data points have their labels propagated throughout the graph.\n",
    "\n",
    "Some though not all, of the nodes have a label at the start. The label identifies the data point as belonging to a community. As the algorithm progresses, the membership of each community can change. Densely connected communities develop a common label. The algorithm is given as:\n",
    "\n",
    "1. For a node x, label it as $C_x(0)=x$ for all nodes. Which initializes the labels on all nodes.\n",
    "1. Let $t=1$.\n",
    "1. Randomly sort the nodes and set it to $X$.\n",
    "1. For all $x \\in X$, determine which label occurs with the highest frequency among neighbours. If there is a tie, choose one at random.\n",
    "1. If every node has the same label as the maximum number of their neighbors have, then stop. Otherwise, increment it, and go to step 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "51aef006",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 279 ms, sys: 10.1 ms, total: 289 ms\n",
      "Wall time: 51.3 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "communities_label_propagation = analyst.communities_label_propagation(graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "593f9530",
   "metadata": {},
   "source": [
    "<a id=\"example\"></a>\n",
    "# Graph Machine Learning Examples\n",
    "\n",
    "<a id='example_pagerank'></a>\n",
    "\n",
    "## Example: Personalize PageRank\n",
    "\n",
    "The Classical PageRank algorithm computes ranking scores for the nodes using the network created by the incoming edges in the graph. The algorithm intended for directed graphs, although undirected graphs can be treated as well by converting them into directed graphs with reciprocated edges. For example, keeping the original edge and creating a second one going in the opposite direction. The edges on the graph define the relevance of each node in the graph, reflecting this on the scores. Meaning that greater scores correspond to nodes with greater relevance.\n",
    "\n",
    "The Personalized Pagerank algorithm allows you to select a set of nodes, and give them greater importance when computing the ranking score. It creates a personalized pagerank score, and can discover similar nodes. It computes the important vertices relevant to the vertex of interest based on connectivity.\n",
    "\n",
    "This example determines the relevance between BMI and patients using the Personalized PageRank algorithm. A target feature is created that labels a patient as risky or non-risky based on their target value. A patient is deemed risky when the target value is 250 or higher."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "03c2dfa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "loop1:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = DatasetFactory.from_dataframe(df)\n",
    "ds = ds.set_target('target')\n",
    "raw_data = ds.to_pandas_dataframe().copy()\n",
    "raw_data.loc[raw_data['target'] < 250, 'Y'] = 0\n",
    "raw_data.loc[raw_data['target'] >= 250, 'Y'] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f26b6042",
   "metadata": {},
   "source": [
    "The goal of the model is to determine which BMI values are associated with risky and non-risky patients using the Personalized PageRank algorithm. The algorithm computes ranking scores for the vertices using the network created by the incoming edges in the graph. It is intended for directed graphs, although undirected graphs can be treated as well by converting them into directed graphs with reciprocated edges. For example, keeping the original edge and creating a second one going in the opposite direction. The edges on the graph define the relevance of each vertex in the graph, reflecting this on the scores, meaning that greater scores will correspond to vertices with greater relevance.\n",
    "\n",
    "The next cell builds Personalized Pagerank models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9acde628",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.15 s, sys: 416 ms, total: 4.57 s\n",
      "Wall time: 1.67 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "prs = []\n",
    "for patient in range(n_patients):\n",
    "    prs.append(analyst.personalized_pagerank(graph,\n",
    "                                             graph.get_vertex(patient),\n",
    "                                             damping = 0.9,\n",
    "                                             max_iter = 10,\n",
    "                                             tol = 1e-06))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93e917f3",
   "metadata": {},
   "source": [
    "For all patients in a group, the relevance metric is normalized so that the area under the curve is 1. The normalized relevance metric is a probability density function (PDF). The next cell performs the normalization and plots the PDFs. The green line represents the distribution of relevance of the non-risky group, and the red line is for the risky group. This plot demonstrates that patients that aren't at risk associated with lower BMI values though patients that are at risk for diabetes are associated with higher BMI values. Thus, there is a correlation between diabetes and BMI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "75e1bfb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "risky_ranks = np.zeros(n_total)\n",
    "nonrisky_ranks = np.zeros(n_total)\n",
    "risky_patients = set(raw_data[raw_data['Y'] == 1].index)\n",
    "\n",
    "for pid, pr in enumerate(prs):\n",
    "    values = np.zeros(n_total)\n",
    "    for vertex, score in pr:\n",
    "        values[vertex.id] += score\n",
    "    \n",
    "    ranks = risky_ranks if pid in risky_patients else nonrisky_ranks\n",
    "    ranks += values\n",
    "    \n",
    "risky_relevance = pd.DataFrame(risky_ranks).reset_index().rename(\n",
    "    columns = {'index': 'Node ID', 0: 'Risky Relevance'}).set_index('Node ID')\n",
    "nonrisky_relevance = pd.DataFrame(nonrisky_ranks).reset_index().rename(\n",
    "    columns = {'index': 'Node ID', 0: 'Non-Risky Relevance'}).set_index('Node ID')\n",
    "importance = node_explanations.join(risky_relevance).join(nonrisky_relevance)\n",
    "\n",
    "bmi_data = importance[importance['Entity'].apply(lambda x: x.startswith('bmi'))].copy()\n",
    "bmi_data['Entity'] = bmi_data['Entity'].apply(lambda x: x.split()[1]).astype(float)\n",
    "bmi_data['Risky Relevance'] = bmi_data['Risky Relevance'] / bmi_data.sum()['Risky Relevance']\n",
    "bmi_data['Non-Risky Relevance'] = bmi_data['Non-Risky Relevance'] / bmi_data.sum()['Non-Risky Relevance']\n",
    "sns.lineplot(x=bmi_data['Entity'], y=bmi_data['Non-Risky Relevance'], color='green');\n",
    "sns.lineplot(x=bmi_data['Entity'], y=bmi_data['Risky Relevance'], color='red').set(xlabel = 'BMI', ylabel = 'Relevance');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b858cbb9",
   "metadata": {},
   "source": [
    "<a id='example_deepwalk'></a>\n",
    "\n",
    "## Example: DeepWalk\n",
    "\n",
    "The next cell creates a DeepWalk model using the [`.deepwalk_builder()`](https://docs.oracle.com/en/database/oracle/property-graph/21.1/pypgx/api/api.html#pypgx.api.Analyst.deepwalk_builder) method in the [`Analyst`](https://docs.oracle.com/en/database/oracle/property-graph/21.1/pypgx/api/api.html#pypgx.api.Analyst) class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8a1654bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 176845.953125\n"
     ]
    }
   ],
   "source": [
    "model = analyst.deepwalk_builder(window_size = 5,\n",
    "                                 walks_per_vertex = 100,\n",
    "                                 walk_length = 50,\n",
    "                                 layer_size = 25,\n",
    "                                 num_epochs = 5,\n",
    "                                 learning_rate = 0.1,\n",
    "                                 min_learning_rate = 0.001,\n",
    "                                 seed = 0)\n",
    "model.fit(graph)\n",
    "print('Loss: %.6f' % model.loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bde1ddd",
   "metadata": {},
   "source": [
    "The embeddings are available in the models [`trained_vectors`](https://docs.oracle.com/en/database/oracle/property-graph/21.1/pypgx/api/mllib.html#pypgx.api.mllib.DeepWalkModel.trained_vectors) property. These are converted from a [`PgxFrame`](https://docs.oracle.com/cd/E56133_01/latest/prog-guides/frames.html) into a Pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e5015cfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert from pypgx frame to Pandas dataframe\n",
    "embeddings = model.trained_vectors.flatten_all().to_pandas()\n",
    "embeddings['vertexId'] = embeddings['vertexId'].apply(lambda x: int(x))\n",
    "embeddings.sort_values(by=['vertexId'], inplace=True)\n",
    "embeddings.drop('vertexId', axis=1, inplace=True)\n",
    "embeddings.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8601994a",
   "metadata": {},
   "source": [
    "To demonstrate the power of the DeepWalk algorithm, a comparison between two approaches is made.  The first approach uses the standardized features (the unaugmented features). The second approach uses the same data plus the augmented values from the DeepWalk embeddings. A random forest model is created for each approach, and the results are compared.\n",
    "\n",
    "A random forest model is a good choice for comparison as the model class is generally insensitive to an increase in the number of features. In many model classes like logistic regression, an increase in the number of features results in a model that is better at predicting the results of the training data. This isn't the case with a random forest.\n",
    "\n",
    "The `rf_evaluator` function splits the data into training and test sets. Then the function builds a random forest model based on the training data. The test data is used to make predictions, and [`ADSEvaluator`](https://docs.oracle.com/en-us/iaas/tools/ads-sdk/latest/ads.evaluations.html#ads.evaluations.evaluator.ADSEvaluator) computes the several binary classification metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b1af54ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "loop1:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "loop1:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_cols = list(raw_data.columns[:-2]) # remove cols related to targets\n",
    "raw_data[training_cols] = StandardScaler().fit_transform(raw_data[training_cols])\n",
    "\n",
    "augmented_data = raw_data.join(embeddings)\n",
    "augmented_data = DatasetFactory.from_dataframe(augmented_data).set_target('Y')\n",
    "augmented_data = augmented_data.drop('target', axis=1)\n",
    "\n",
    "unaugmented_data = DatasetFactory.from_dataframe(raw_data).set_target('Y')\n",
    "unaugmented_data = unaugmented_data.drop('target', axis=1)\n",
    "\n",
    "def rf_evaluator(data):\n",
    "    train_data, test_data = data.train_test_split(test_size = .3, random_state = 0)\n",
    "    rf_classifier = RandomForestClassifier(random_state=0)\n",
    "    rf_classifier.fit(train_data.X, train_data.y)\n",
    "    rf_model_ads = ADSModel.from_estimator(rf_classifier)\n",
    "\n",
    "    ads_evaluator = ADSEvaluator(test_data, models = [rf_model_ads], positive_class = 1)\n",
    "    ads_evaluator.del_metrics(['hamming_loss'])\n",
    "    return ads_evaluator.show_in_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a476117a",
   "metadata": {},
   "source": [
    "The unaugmented data used the following features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f5a0fe02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'age sex bmi bp s1 s2 s3 s4 s5 s6 Y'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "' '.join(unaugmented_data.columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4d13071",
   "metadata": {},
   "source": [
    "The augmented data used the following features. It has 24 additional features that are identified by the name, `embedding_#`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "92e66c5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'age sex bmi bp s1 s2 s3 s4 s5 s6 Y embedding_0 embedding_1 embedding_2 embedding_3 embedding_4 embedding_5 embedding_6 embedding_7 embedding_8 embedding_9 embedding_10 embedding_11 embedding_12 embedding_13 embedding_14 embedding_15 embedding_16 embedding_17 embedding_18 embedding_19 embedding_20 embedding_21 embedding_22 embedding_23 embedding_24'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "' '.join(augmented_data.columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cb4f3e4",
   "metadata": {},
   "source": [
    "The binary classification evaluation metrics on the test data for the unaugmented are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d5337931",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:ads.common:ADSModel is being deprecated. Users should instead use GenericModel or one of its subclasses. More information here: https://accelerated-data-science.readthedocs.io/en/latest/user_guide/model_registration/introduction.html#register\n"
     ]
    },
    {
     "data": {
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rV6+u0G7FihV69NFHK8Q++ugjw00Y1atXN9w48v7775t+nvPnz9e4cePK3/v5+enFF1+Un5+fnn766Qp14+Pj9dJLL+mWW25x8EoBAAAA4PrEk/oAAAAA/vDS09Pl7e1d/qpTp466dOliera39FvC69LkUlW0a9dOixcv1oYNG1SzZs0q92eWJL3Iz8+vyv1fKzNmzKiQ0DcTEBBgesPFped/Hz16VKmpqYZ6lz5NnJ2dra+//tpQb+TIkRUS+hdVq1bNdBv2rVu3qri42O7c3YG3t7f69u2rr776SmPHjrVZ7/Tp06ZxX19fp8azVf/3/dv6PV+L37I7zcVVwsLCKiT0bTHbBSM5OVkZGRkVYmY3y/Tt21d169atEDM7yqBhw4aGmwQuGjFihCF2/vx5bd682e68AQAAAOBGQFIfAAAAAJzg7e2thIQENW7c2GV9enl5qXHjxvLwcM0/0fz9/W2WmZ2bfr2wdQb7pcySj1u3btWvv/5a/j4+Pt5wjnfTpk3Vq1evCrH9+/ebnvcdFxcni8Vi+nrmmWcM9c+dO2dz63Z3YrFYVL169UqfuK9du7ZpvLCw0KnxbNX/ff+2fs/X4rfsTnNxlf79+ztUr3fv3oYn4q1WqxISEsrfnzhxQomJiYa2ZmvS7PiRrKwseXl5ma4rW39zd+3a5dD8AQAAAOB6RlIfAAAAABzUvn17JSUl6d5773Vpv2lpabr33nu1ePFil/Tn6+tr84n/w4cPu2SMq6127doVtgO3p1evXmratGmFWFlZmT788MPy92ZPE48cOdJwY4Wt7dYvx+9vKnBXJSUlSkhIUOfOnZWenm6zXv369U3jJ06ccHiswsJCm0/8/77/Bg0amNbJz89XTk6Ow+O5gq25XK/rSvptpxBHeHh4aOTIkYb473fB+PDDD3XhwoUK5c2aNdNdd91laOeqtXU9rCsAAAAAqCqS+gAAAABgonr16rrpppsUERGhCRMmaMuWLUpLS1NERITTfb399tsqLS1Vbm6uEhMT1b17d0Mdq9WqCRMmuOxp7g4dOpjGzZ6idZVLk3m/d/bs2Sr1bW/3gUtVlnw8cOCA4XO2WCwaPXp0leZYGXvHIlxNpaWlKikp0dGjR7Vo0SI1bNjQUOf48eMaPHiwze/UViJ43759Ki0tdWgee/fuNd0FwcPDQ8HBweXv27RpI29vb9M+tm3b5tBYrhIWFmYa3759u86fP3/FxrX1PVR1XUnOrS2zNfLf//5XP/zwgyTjMReSNGrUKJftQmLGXdYVAAAAAFxJJPUBAAAA/OEFBgbKarVWeJ09e1aZmZnasWOHXn/9dfXo0UMWi+Wy+vfw8JCXl5fq1q2rmJgYbdq0Sd26dTPUKykp0YQJE6p4Nb+JjY01jS9btszpLdLNmH0W9s6MP3LkSJXGczYpOGrUKMMcd+3apZ9//tk08RgTE6MWLVoY4raezL4c9m56uJq8vLzk7e2tpk2basyYMdqyZYtq1KhhqLdz50698847pn1069ZNnp6ehnhRUZE2bNjg0DxWrVplGg8PD69w/rqfn5+ioqJM6/773/92aCxXsbWucnNztXLlyir3b+tvjK21VdV1JTm3tm699VbdeeedhviKFSv0008/affu3RXi9m6WcdXacpd1BQAAAABXEkl9AAAAALjKvLy8tGTJEtOnj1NSUrRu3boqjzF48GDTpOupU6f0xBNPONWX2RPIvr6+pn2b1d27d6+ysrKcGrOqWrZsqejoaEM8Pj6+whngF5md+S1JoaGhponWuLg4lZaWOvUy2z3AHYSGhmr69OmmZf/4xz9UUlJiiNeuXVu9evUybfPCCy9Ummg9duyYlixZYlrWr18/Q2zYsGGmdTdv3mz6fdpTlSfqmzVrZprUlqSpU6fq5MmTDvdltVpVVlZWIWa2riTZXD8bN250eDxXMVsr8fHxpkdadO/eXUFBQab9tG/f3hALCwtzel1t2bKlytcEAAAAAO6OpD4AAAAAXAMtW7a0+QTrCy+8UOX+W7dubTNRvWjRIk2dOtWQULxUSUmJ3njjDQ0aNMhQZrZl+/nz55WamlohZrVaNWPGDCdm7jpm1x8XF6dDhw5ViNWuXVv9+/c37aNBgwYKDw83xBcsWKD8/Hx5eXlV+tq3b5/Wrl1renNAUFCQLBZLhZfZLg5X2uTJkxUQEGCIZ2Rk6N133zVt8/e//900vnv3bk2aNMl0a31JOn36tPr372+6Y0T9+vU1fvx4Q3zkyJFq06aNaX+jR4926Cn5nJwcTZ48Wf/6178qrWvPSy+9ZBo/fvy4evbsqWPHjlXax65duxQZGWmoa7auJCkpKckQW7lypcuO63DGgAED5OfnVyF26NAhvfLKK4a6tv4GSea7HqSlpWnNmjUOrau8vDy9+eabpjcvAQAAAMCNhqQ+AAAAAFwjTz/9tLy8vAzxnTt36osvvqhy/y+++KLNp2Tnzp2r0NBQLVy4UAcOHFB+fr4KCgqUnp6uNWvW6Mknn1STJk00fvx45eTkGNp36tTJtN8hQ4YoOTlZRUVF+u677zR48GCtXbu2ytdyOfr3729IPp46dcpQb/Dgwabbz1/05JNPGmKHDx9Wx44dNWfOHH377bc6efKkiouLlZWVpT179mjZsmUaN26cWrVqpfDwcH3yySdVv6ArqFatWnr88cdNy2bPnm36dHvXrl1t7j6wcOFCRUVF6eOPP1ZmZqaKior0888/680331RoaKi++uor03Zz585VrVq1DHFPT08tWrRIPj4+hrLi4mINGjRIvXv3VkJCgtLT01VUVKS8vDwdPHhQy5cv1/Dhw9WkSRO9+uqrKi0ttfNJVC4yMlLjxo0zLUtLS1NISIiefPJJJSUlKSsrS8XFxfr111+1a9cuzZs3T126dFFERIR27NhhaB8WFma6Hf7SpUs1e/Zs5eXlKSsrSwsWLLB5U9CV5uvrq4EDBxril64tf39//fnPf7bZz4gRI0y34B8wYIAeeeQRbd26VceOHVNxcbFOnTqlH3/8UWvWrNHzzz+vmJgYNWrUSJMmTar6BQEAAADAdcBitXXrPAAAAADcYI4cOaLmzZsb4oGBgS45m3rUqFF67733DPF3331Xo0aNcqpNdHS0tm3bVuU5HTx4UJGRkaaJeUfFxMQoMTGxQiwjI0OBgYFVOs/a1uferVs3w7Vf7nc0ZswYm9u8X7Rz50516dLFZrnValWfPn20adMmp8e/aOTIkVq6dKkhHhQUpPT09Aoxs8/7ctg6n93eE/SBgYE6ffq0oeydd94xTSKfO3dO3bt3N01QO+uJJ55QXFyc3TorV67UkCFDqvS7mzlzpp577rnLbi9JZWVl6tevnz777LMq9XP48GHDjTd/+tOftHnz5ir1u3XrVsOOD0uXLjX9Ds3qVmb79u2KioqyW2fs2LF688037dZZtmyZhg8f7tTYl+K/tQAAAAD8EfCkPgAAAABcQ88884zpk7lJSUmmW247Kzg4WF9//bUiIiKq3NfvNWnSRGPGjHGobtOmTXXrrbe6dHxH2dv+W5LatWtnN6Ev/ZYcX716tbp27erKqbkdf39/TZgwwbRs1qxZpsc1+Pj4aNOmTaZHNDjK09NTs2fP1ssvv1xp3YEDB2rz5s1q3LjxZY/nCp6enlq9erWmT59uun6rYubMmQ71abFYFBMT49KxHRUZGang4GC7dSpbe5I0bNgwzZkzx+YNKAAAAACA35DUBwAAAIBrqHXr1qZbWUvSCy+84JIxgoKClJycrAULFqhly5ZOtb355pttnjcfFxennj172m3fpUsXpaSk6JZbbnFqXFfp2rWrzbPYJTm8hbmfn5+SkpL0f//3f6bbw9tisVgUGRmpAQMGONXmWpk8ebLp9R06dEgJCQmmbWrVqqWEhAQtX75coaGhTo3Xp08fbd++XdOmTXP4unv06KG0tDRNnTpVderUcWq8Dh06uCwR7uXlpVmzZik5OVn33HOPU9+bt7e3BgwYoHr16hnKIiMjtWDBAruJ/bp16yo+Pt7mDiBXg721Exoaqttvv92hfqZMmaIvvvhCISEhTo3fpEkTTZkyxak2AAAAAHC9Yvt9AAAAAH8Y7rj9viTt379fHTp0MN1GeseOHS59yv7ChQvaunWrEhMTlZqaqvT0dOXm5io/P181atRQ3bp11apVK3Xs2FF9+vRRTEyMvL29bfZXVlamDz74QB988IHS0tKUn5+v+vXrq1OnTho6dKiGDBkii8Xi1Jb6rtx+X/rtnPapU6ca4t7e3srIyFDDhg2d6u/06dOKj49XUlKS9uzZo+zsbOXn56tatWry9/dX8+bN1aZNG915553q2bOnzRsaioqKVKdOHcMZ72+99ZYeeeQRp+Zkxtnt9y+aOnWq5s6da4i3adNG+/fvt5tstlqt2rZtm7Zs2aLk5GQdPXpUubm5KiwslL+/v+rVq6fg4GBFR0crNjZW7dq1c+6iLlFYWKh169YpOTlZu3btUmZmpnJzc3X27FnVqlVLDRs2VEhIiLp06aK7775bHTt2rNJ49hw6dEgbNmxQSkqKvv32W+Xm5pafM+/n56fAwEC1a9dOPXr0UGxsbKW/u71792revHlKTExUZmamatasqRYtWui+++7T+PHj1aBBA6e21Hfl9vuSdOLECTVt2tR0B4dXXnlFkydPdqo/q9WqzZs3a+3atdq5c6eOHj2qvLw8lZWVyc/PT40bN1ZwcLA6d+6su+66S+Hh4S7fJQEAAAAA3BVJfQAAAAAAroGNGzeqT58+FWLR0dFKTExkO3IAAAAAAFCOW5oBAAAAALgGvvzyywrvfXx8tGjRIhL6AAAAAACgApL6AAAAAABcA5cm9WfMmKHg4OBrNBsAAAAAAOCu2H4fAAAAAICr7PTp06pXr175eeShoaHas2ePvL29r/HMAAAAAACAu+FJfQAAAAAArrLExMTyhL6Hh4cWL15MQh8AAAAAAJjiSX0AAAAAAAA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      "text/plain": [
       "<Figure size 2592x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2592x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1728x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2592x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_evaluator(unaugmented_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc0ec851",
   "metadata": {},
   "source": [
    "The binary classification evaluation metrics on the test data for the augmented are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c8a86b4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:ads.common:ADSModel is being deprecated. Users should instead use GenericModel or one of its subclasses. More information here: https://accelerated-data-science.readthedocs.io/en/latest/user_guide/model_registration/introduction.html#register\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2592x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2592x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1728x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2592x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_evaluator(augmented_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92099b87",
   "metadata": {},
   "source": [
    "These results demonstrate that there is a substantial improvement in the precision metric when the DeepWalk augment features are included in the model. Meaning that the model  able to decrease its false detection rate. While there is no guarantee that this effect can exist in all models for all datasets, it does demonstrate that the DeepWalk graph machine learning algorithm can be a powerful preprocessing step."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daf5df3f",
   "metadata": {},
   "source": [
    "<a id='pgql'></a>\n",
    "# PGQL Queries\n",
    "\n",
    "The [Parallel Graph AnalytiX (PGX)](https://www.oracle.com/middleware/technologies/parallel-graph-analytix.html) library provides access to powerful graph pattern-matching query language for the property graph data model called PGQL. PGQL is an SQL-like language used to query the property graph data model. It allows you to define graph patterns that are matched against vertices and edges. PGQL supports grouping, aggregation, sorting, and many other SQL constructs. Furthermore, PGQL has powerful regular expression constructs for graph reachability (transitive closure), shortest path finding, and cheapest path finding.\n",
    "\n",
    "<a id=\"intro_struture\"></a>\n",
    "## Main Query Structure\n",
    "\n",
    "This the syntax of the main query structure:\n",
    "```\n",
    "PgqlStatement ::= CreatePropertyGraph\n",
    "| DropPropertyGraph\n",
    "| Query\n",
    "\n",
    "Query ::= SelectQuery\n",
    "| ModifyQuery\n",
    "\n",
    "SelectQuery ::= PathPatternMacros?\n",
    "SelectClause\n",
    "FromClause\n",
    "WhereClause?\n",
    "GroupByClause?\n",
    "HavingClause?\n",
    "OrderByClause?\n",
    "LimitOffsetClauses?\n",
    "```\n",
    "\n",
    "Details of the different clauses of a query are found in the following sections:\n",
    "\n",
    "* The [path pattern macros](https://pgql-lang.org/spec/latest/#path-pattern-macros) allow for specifying complex reachability queries.\n",
    "* The [SELECT](https://pgql-lang.org/spec/latest/#select) clause specifies what should be returned.\n",
    "* The [FROM](https://pgql-lang.org/spec/latest/#from) clause defines the graph pattern that is to be matched.\n",
    "* The [WHERE](https://pgql-lang.org/spec/latest/#where) clause specifies filters.\n",
    "* The [GROUP BY](https://pgql-lang.org/spec/latest/#group-by) clause allows for creating groups of results.\n",
    "* The [HAVING](https://pgql-lang.org/spec/latest/#having) clause allows for filtering entire groups of results.\n",
    "* The [ORDER BY](https://pgql-lang.org/spec/latest/#order-by) clause allows for sorting of results.\n",
    "* The [LIMIT and OFFSET](https://pgql-lang.org/spec/latest/#limit-and-offset) clauses allow for pagination of results.\n",
    "\n",
    "<a id=\"intro_pg\"></a>\n",
    "## Property Graph\n",
    "\n",
    "A property graph has a name, which is a (character) string, and contains:\n",
    "* A set of vertices (or nodes).\n",
    "* Each vertex has zero or more labels.\n",
    "* Each vertex has zero or more properties (or attributes), which are arbitrary key-value pairs.\n",
    "* A set of edges (or relationships).\n",
    "* Each edge is directed.\n",
    "* Each edge has zero or more labels.\n",
    "* Each edge has zero or more properties (or attributes), which are arbitrary key-value pairs.\n",
    "Labels, as well as names of properties, are strings. Property values are scalars such as numerics, strings or booleans.\n",
    "\n",
    "For Example:"
   ]
  },
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RXXZA0x8ZLxAXjaNOAQ8XgltNWnVgO6LwHcl+ABtPVmz1WLAgxn3akCmQ7drTD+4kehCw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    }
   },
   "cell_type": "markdown",
   "id": "4869eb66",
   "metadata": {},
   "source": [
    "![pg.png](attachment:9a116e4b-fad4-4a8f-97e1-d836a473547c.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3874ad99",
   "metadata": {},
   "source": [
    "In this example, `student_network` is the name of the graph. The graph has three vertices labeled `Person`, and one vertex labeled `University`. Six directed edges connect the vertices. Three of them go from person to person vertices, and they have the `knows` label. Three others go from person to university vertices, and are labeled `studentOf`. The person vertices have two properties, `name` for encoding the name of the person, and `dob` for encoding the date of birth of the person. The university vertex has only a single property `name` for encoding the name of the university. The edges have no properties."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff9a9b28",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "[PGQL](https://pgql-lang.org/) is a graph query language that is built on top of SQL. It provides graph pattern matching capabilities into the PyPGX conda environment. PGQL allows you to construct pattern matching queries using the familiar SQL-like commands such as `SELECT`, `FROM`, `WHERE`, `GROUP BY`, and `ORDER BY`. You can use fixed-length graph patterns to match a fixed number of vertices and edges per solution. In the query, you can define arbitrary label expressions like `friend_of|sibling_of`. The expression matches edges that have the label `friend_of` or `sibling_of`. You can define the nature of the relationship that you want to return. Effectively, the edge patterns are higher-level join operations that relate entities. You can also use PGQL to find variable-length graph patterns. These patterns contain quantifiers such as, `*`, `+`, `?` or `{X,Y}`, that match vertices and edges recursively.\n",
    "\n",
    "You can use to create subqueries that allow vertices and edges that are matched in a query to be passed to the parent query for more processing. The interconnection between PGQL and SQL allows you to benefit from existing SQL features and more capabilities that are added to the SQL standard."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b809b09d",
   "metadata": {},
   "source": [
    "<a id='pgql_patients'></a>\n",
    "## Example: Filter Patients Based On Age\n",
    "\n",
    "The [`.query_pgql()`](https://docs.oracle.com/en/database/oracle/property-graph/21.2/pypgx/api/api.html#pypgx.api.PgxGraph.query_pgql) method of a graph object is used to perform a PGQL query on the data. It returns a [`PgqlResultSet`](https://docs.oracle.com/en/database/oracle/property-graph/21.1/pypgx/api/api.html#pypgx.api.PgqlResultSet) object, which is the query result. Generally, a call to `.to_pandas()` is used to materialize the results in a Pandas dataframe.\n",
    "\n",
    "In a traditional relational database management system, the `FROM` clause contains a table name of an inner select statement. However, in a PGQL query the `FROM` clause defines the graph pattern that is to be matched. Syntactically, a `FROM` clause is composed of the keyword `FROM` followed by a comma-separated sequence of `MATCH` clauses, each defining a path pattern:\n",
    "```\n",
    "FromClause ::= 'FROM' MatchClause ( ',' MatchClause )*\n",
    "```\n",
    "\n",
    "The [`MATCH`](https://pgql-lang.org/spec/latest/#match) is defined by the following pattern:\n",
    "\n",
    "```\n",
    "MatchClause ::= 'MATCH' ( PathPattern | GraphPattern ) OnClause?\n",
    "\n",
    "GraphPattern ::= '(' PathPattern ( ',' PathPattern )* ')'\n",
    "\n",
    "PathPattern ::= SimplePathPattern\n",
    "| AnyPathPattern\n",
    "| AnyShortestPathPattern\n",
    "| AllShortestPathPattern\n",
    "| TopKShortestPathPattern\n",
    "| AnyCheapestPathPattern\n",
    "| TopKCheapestPathPattern\n",
    "| AllPathPattern\n",
    "\n",
    "\n",
    "SimplePathPattern ::= VertexPattern ( PathPrimary VertexPattern )*\n",
    "\n",
    "VertexPattern ::= '(' VariableSpecification ')'\n",
    "\n",
    "PathPrimary ::= EdgePattern\n",
    "| ReachabilityPathExpression\n",
    "\n",
    "EdgePattern ::= OutgoingEdgePattern\n",
    "| IncomingEdgePattern\n",
    "| AnyDirectedEdgePattern\n",
    "\n",
    "OutgoingEdgePattern ::= '->'\n",
    "| '-[' VariableSpecification ']->'\n",
    "\n",
    "IncomingEdgePattern ::= '<-'\n",
    "| '<-[' VariableSpecification ']-'\n",
    "\n",
    "AnyDirectedEdgePattern::= '-'\n",
    "| '-[' VariableSpecification ']-'\n",
    "\n",
    "VariableSpecification ::= VariableName? LabelPredicate?\n",
    "\n",
    "VariableName ::= Identifier\n",
    "```\n",
    "\n",
    "A path pattern that describes a partial topology of the subgraph pattern. In other words, a topology constraint describes some connectivity relationships between vertices and edges in the pattern, whereas the whole topology of the pattern is described with one or multiple topology constraints.\n",
    "\n",
    "A topology constraint is composed of one or more vertices and relations, where a relation is either an edge or a path. In a query, each vertex or edge is (optionally) associated with a variable, which is a symbolic name to reference the vertex or edge in other clauses. For example, consider the following topology constraint:\n",
    "\n",
    "```\n",
    "(n) -[e]-> (m)\n",
    "```\n",
    "\n",
    "The preceding example defines two vertices (with variable names `n` and `m`), and an edge (with the variable name `e`) between them. The edge is directed so that the `e` edge is an outgoing edge from vertex `n`.\n",
    "\n",
    "More specifically, a vertex term is written as a variable name inside a pair of parenthesis `()`. An edge term is written as a variable name inside a square bracket `[]` with two dashes and an inequality symbol attached to it – which makes it look like an arrow drawn in ASCII art. An edge term is always connected with two vertex terms as for the source and destination vertex of the edge. The source vertex is located at the tail of the ASCII arrow, and the destination at the head of the ASCII arrow.\n",
    "\n",
    "There can be multiple path patterns in the `FROM` clause of a PGQL query. Semantically, all constraints are conjunctive, each matched result satisfies every constraint in the `FROM` clause.\n",
    "\n",
    "\n",
    "In the next cell,  you run a a PGQL query to match on any vertex where the age value in that vertex is greater than seventy. It returns the vertex ID and age. The [`ID()`](https://pgql-lang.org/spec/latest/#id) function is used. It returns a system-generated identifier for the vertex (or edge).\n",
    "\n",
    "These results are sorted in descending order by age. Notice that the column names can be renamed using the `AS`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ca4393d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>502</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>204</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>402</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>501</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>499</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>321</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>498</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>311</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>211</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID   Age\n",
       "0  502  79.0\n",
       "1  204  79.0\n",
       "2  402  79.0\n",
       "3  501  78.0\n",
       "4  500  77.0\n",
       "5  499  76.0\n",
       "6  321  75.0\n",
       "7  498  75.0\n",
       "8  311  75.0\n",
       "9  211  74.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_set = graph.query_pgql(\"\"\"\n",
    "    SELECT id(x) AS ID, x.age AS Age\n",
    "    FROM MATCH (x)\n",
    "    WHERE x.age > 70\n",
    "    ORDER BY x.age DESC\n",
    "\"\"\")\n",
    "result_set.to_pandas().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5f1d0b0",
   "metadata": {},
   "source": [
    "<a id='pgql_bmi'></a>\n",
    "## Example: Patients with a High BMI\n",
    "\n",
    "In this example, the `MATCH` expression uses `(x)-[e]->(y)`. The vertices and edges related to the BMI vertice identifiers are returned if the BMI identifier is greater than 450. This is not the BMI value, but the identifier for the BMI node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0a543a8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------------+\n",
      "| id(x) | Age  | BMI_id |\n",
      "+-----------------------+\n",
      "| 10    | 22.0 | 503    |\n",
      "| 136   | 23.0 | 503    |\n",
      "| 173   | 31.0 | 503    |\n",
      "| 247   | 26.0 | 503    |\n",
      "| 281   | 23.0 | 503    |\n",
      "| 358   | 43.0 | 503    |\n",
      "| 381   | 29.0 | 503    |\n",
      "| 406   | 33.0 | 503    |\n",
      "| 10    | 22.0 | 504    |\n",
      "| 26    | 19.0 | 504    |\n",
      "+-----------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "result_set_bmi = graph.query_pgql(\"\"\"\n",
    "    SELECT id(x), x.age AS Age, id(y) AS BMI_id\n",
    "    FROM MATCH (x)-[e]->(y)\n",
    "    WHERE id(y) > 450\n",
    "    LIMIT 10\n",
    "\"\"\")\n",
    "result_set_bmi.print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a15a61a",
   "metadata": {},
   "source": [
    "Clean up the graph files that have been created:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "860f6c8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "shutil.rmtree(DATA_DIR, ignore_errors=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf2f2ffe",
   "metadata": {},
   "source": [
    "<a id=\"ref\"></a>\n",
    "# References\n",
    "\n",
    "- [ADS Library Documentation](https://docs.cloud.oracle.com/en-us/iaas/tools/ads-sdk/latest/index.html)\n",
    "- [Data Science YouTube Videos](https://www.youtube.com/playlist?list=PLKCk3OyNwIzv6CWMhvqSB_8MLJIZdO80L)\n",
    "- [OCI Data Science Documentation](https://docs.cloud.oracle.com/en-us/iaas/data-science/using/data-science.htm)\n",
    "- [Oracle Data & AI Blog](https://blogs.oracle.com/datascience/)\n",
    "- [Property Graph Query Language (PGQL)](https://pgql-lang.org/)\n",
    "- [PyPGX](https://docs.oracle.com/cd/F41824_01/latest/index.html)\n",
    "- [Oracle Property Graph](https://docs.oracle.com/en/database/oracle/property-graph/)"
   ]
  }
 ],
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