open_targets_with_LLMs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query OpenTargets with LLMs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook was prepared for the pistoia alliance LLM evaluation effort<br>\n",
    "Author: Helena F. Deus, PhD<br>\n",
    "License: CC-BY-NC-SA (https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en)<br>\n",
    "Date: 2024-02-23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a new conda env and don't forget to run this the code below the first time you use this\n",
    "#python -m pip install -r requirement.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "graphQL_prompt = '''Your task is to create a GraphQL query for the user request given a schema. The instructions below will guide you systematically in creating a functional query. \n",
    "\n",
    "1. Determine the Operation Type: Start by defining the operation type of your GraphQL. For instance, in the provided examples, the operation type is `query`. \n",
    "\n",
    "2. Identify the Main Object: Scrutinize the request and identify the main subjects. These subjects correspond to the main objects in your GraphQL query. For example, in the first query, 'target' with a parameter 'ensemblId' is the key object.\n",
    "\n",
    "3. Specify the Parameters: The requests usually contain specific values that need to be passed as parameters in the query. Identify these in the parentheses following the object. For instance, in the second example 'efoId' is a parameter in 'disease' object. \n",
    "\n",
    "4. Set Return Fields: In GraphQL, you need to specify exactly what details you want returned by your queries. Identify the appropriate fields you need returned for your query from the request and list them within a pair of curly brackets after your object and parameters. \n",
    "\n",
    "5. Use Nested Fields: Sometimes, the information you need is nested within other fields. Make sure to check the schema and include these nested fields where necessary. For instance, the field 'associatedTargets' in the second example contains subfields 'count' and 'rows'.\n",
    "\n",
    "6. Reference Schema Documentation: Always remember to refer to the schema documentation when creating your query. The schema contains the structure and type of data that can be queried and will indicate what fields are available and how they are connected.\n",
    "\n",
    "7. Test Your Query: Once your query is prepared, test it to ensure it works effectively and returns the required data. If you encounter any issues or the data returned is not as expected, refer back to the schema and adjust your query accordingly.\n",
    "\n",
    "8. Return the query verbatim, do not add any explanation so that it can be parsed correctly\n",
    "\n",
    "9. Remember that the identifiers from EFO are as in example one meaning they require an underscore over a colon so if the user provides a colon, please use underscore instead, like so EFO:0000349 becomes EFO_0000349\n",
    "\n",
    "<Examples>\n",
    "\t\n",
    "\n",
    "\tQuestion: Find targets associated with a specific disease or phenotype (EFO_0000349)\n",
    "\tAnswer: query associatedTargets {{\n",
    "\t  disease(efoId: \"EFO_0000349\") {{\n",
    "\t    id\n",
    "\t    name\n",
    "\t    associatedTargets {{\n",
    "\t      count\n",
    "\t      rows {{\n",
    "\t        target {{\n",
    "\t          id\n",
    "\t          approvedSymbol\n",
    "\t        }}\n",
    "\t        score\n",
    "\t      }}\n",
    "\t    }}\n",
    "\t  }}\n",
    "\t}}\n",
    "    \n",
    "    Question: Return Genetic Constraint and Tractability data for AR (ENSG00000169083)\n",
    "\tAnswer: query targetInfo {{\n",
    "\ttarget(ensemblId: \"ENSG00000169083\") {{\n",
    "\t\tid\n",
    "\t\tapprovedSymbol\n",
    "\t\tbiotype\n",
    "\t\tgeneticConstraint {{\n",
    "\t\tconstraintType\n",
    "\t\texp\n",
    "\t\tobs\n",
    "\t\tscore\n",
    "\t\toe\n",
    "\t\toeLower\n",
    "\t\toeUpper\n",
    "\t\t}}\n",
    "\t\ttractability{{\n",
    "\t\tlabel\n",
    "\t\tmodality\n",
    "\t\tvalue\n",
    "\t\t}}\n",
    "\t}}\n",
    "\t}}\n",
    "    \n",
    "<Schema> \n",
    "{schema}\n",
    "\n",
    "<Question>\n",
    "{question}\n",
    "\n",
    "Remember that GraphQL is strongly typed and case sensitive. It is also worth noting that fields in GraphQL are only returned if specifically requested. Even errors in queries will not be returned unless the 'errors' keyword is used in your query. Lastly, when you feel stuck, always refer back to the schema or to the GraphQL documentation.'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## You need to replace this with your own key\n",
    "# Don't forget to add it to \".gitignore\" if you plan to commit to git\n",
    "OPENAI_API_KEY=open(\"openaikey-personal\", \"r\").read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define prompt, llm and output format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "prompt = ChatPromptTemplate.from_template(\n",
    "    graphQL_prompt\n",
    ")\n",
    "\n",
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "llm = ChatOpenAI(openai_api_key=OPENAI_API_KEY)\n",
    "\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "\n",
    "output_parser = StrOutputParser()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the chain with input from user "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "schema = open(\"./data/open-targets-schema.txt\", \"r\").read()\n",
    "question = \"Find the targets associated with lung adenocarcinoma (EFO:0000571)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "query associatedTargets {\n",
      "  disease(efoId: \"EFO_0000571\") {\n",
      "    id\n",
      "    name\n",
      "    associatedTargets {\n",
      "      count\n",
      "      rows {\n",
      "        target {\n",
      "          id\n",
      "          approvedSymbol\n",
      "        }\n",
      "        score\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "chain = prompt | llm | output_parser\n",
    "output = chain.invoke({\"question\": question, \"schema\":schema})\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query is valid!\n"
     ]
    }
   ],
   "source": [
    "#is the query valid? \n",
    "from graphql import graphql, build_schema, parse\n",
    "from graphql import parse\n",
    "\n",
    "try:\n",
    "    parsed = parse(output)\n",
    "    query_valid = True\n",
    "    print(\"Query is valid!\")\n",
    "except:\n",
    "    print(\"Query not valid\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now run this against OpenTargets API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data': {'disease': {'id': 'EFO_0000571', 'name': 'lung adenocarcinoma', 'associatedTargets': {'count': 7962, 'rows': [{'target': {'id': 'ENSG00000146648', 'approvedSymbol': 'EGFR'}, 'score': 0.8267159415027404}, {'target': {'id': 'ENSG00000141510', 'approvedSymbol': 'TP53'}, 'score': 0.7679343542735289}, {'target': {'id': 'ENSG00000133703', 'approvedSymbol': 'KRAS'}, 'score': 0.7564033322165448}, {'target': {'id': 'ENSG00000157764', 'approvedSymbol': 'BRAF'}, 'score': 0.7291460177657758}, {'target': {'id': 'ENSG00000118046', 'approvedSymbol': 'STK11'}, 'score': 0.7224909751877195}, {'target': {'id': 'ENSG00000171094', 'approvedSymbol': 'ALK'}, 'score': 0.6864287642178499}, {'target': {'id': 'ENSG00000141736', 'approvedSymbol': 'ERBB2'}, 'score': 0.6630123723575108}, {'target': {'id': 'ENSG00000182872', 'approvedSymbol': 'RBM10'}, 'score': 0.6510321647486}, {'target': {'id': 'ENSG00000121879', 'approvedSymbol': 'PIK3CA'}, 'score': 0.639439527044695}, {'target': {'id': 'ENSG00000047936', 'approvedSymbol': 'ROS1'}, 'score': 0.6378525119084185}, {'target': {'id': 'ENSG00000079999', 'approvedSymbol': 'KEAP1'}, 'score': 0.6363674974838249}, {'target': {'id': 'ENSG00000139687', 'approvedSymbol': 'RB1'}, 'score': 0.6259981889374922}, {'target': {'id': 'ENSG00000147889', 'approvedSymbol': 'CDKN2A'}, 'score': 0.6233195695317816}, {'target': {'id': 'ENSG00000105976', 'approvedSymbol': 'MET'}, 'score': 0.6044977925869884}, {'target': {'id': 'ENSG00000143924', 'approvedSymbol': 'EML4'}, 'score': 0.6019522954087198}, {'target': {'id': 'ENSG00000165731', 'approvedSymbol': 'RET'}, 'score': 0.5995493179047184}, {'target': {'id': 'ENSG00000168036', 'approvedSymbol': 'CTNNB1'}, 'score': 0.5942906146845932}, {'target': {'id': 'ENSG00000127616', 'approvedSymbol': 'SMARCA4'}, 'score': 0.5879197949687681}, {'target': {'id': 'ENSG00000073282', 'approvedSymbol': 'TP63'}, 'score': 0.5848340669264015}, {'target': {'id': 'ENSG00000149311', 'approvedSymbol': 'ATM'}, 'score': 0.5829633912373894}, {'target': {'id': 'ENSG00000157168', 'approvedSymbol': 'NRG1'}, 'score': 0.5816730738345159}, {'target': {'id': 'ENSG00000176890', 'approvedSymbol': 'TYMS'}, 'score': 0.5765425203329309}, {'target': {'id': 'ENSG00000160201', 'approvedSymbol': 'U2AF1'}, 'score': 0.5577691156756012}, {'target': {'id': 'ENSG00000164362', 'approvedSymbol': 'TERT'}, 'score': 0.5543757918205601}, {'target': {'id': 'ENSG00000101825', 'approvedSymbol': 'MXRA5'}, 'score': 0.5450865598211398}]}}}}\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "# Define the URL of the OpenTarget API endpoint\n",
    "url = 'https://api.platform.opentargets.org/api/v4/graphql'\n",
    "\n",
    "# Set up the request headers\n",
    "headers = {\n",
    "    'Content-Type': 'application/json',\n",
    "}\n",
    "\n",
    "# Set up the request payload\n",
    "data = {\n",
    "    'query': output\n",
    "}\n",
    "\n",
    "# Send the GraphQL query request\n",
    "response = requests.post(url, headers=headers, json=data)\n",
    "\n",
    "# Check if the request was successful\n",
    "if response.status_code == 200:\n",
    "    # Print the JSON response\n",
    "    json_data = response.json()\n",
    "    print(json_data)\n",
    "else:\n",
    "    # Print an error message if the request failed\n",
    "    print(f\"Error: {response.status_code}\")\n",
    "    print(response.text)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now load this into pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Disease Name</th>\n",
       "      <th>Target ID</th>\n",
       "      <th>Approved Symbol</th>\n",
       "      <th>Name</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000146648</td>\n",
       "      <td>EGFR</td>\n",
       "      <td></td>\n",
       "      <td>0.826716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000141510</td>\n",
       "      <td>TP53</td>\n",
       "      <td></td>\n",
       "      <td>0.767934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000133703</td>\n",
       "      <td>KRAS</td>\n",
       "      <td></td>\n",
       "      <td>0.756403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000157764</td>\n",
       "      <td>BRAF</td>\n",
       "      <td></td>\n",
       "      <td>0.729146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000118046</td>\n",
       "      <td>STK11</td>\n",
       "      <td></td>\n",
       "      <td>0.722491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000171094</td>\n",
       "      <td>ALK</td>\n",
       "      <td></td>\n",
       "      <td>0.686429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000141736</td>\n",
       "      <td>ERBB2</td>\n",
       "      <td></td>\n",
       "      <td>0.663012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000182872</td>\n",
       "      <td>RBM10</td>\n",
       "      <td></td>\n",
       "      <td>0.651032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000121879</td>\n",
       "      <td>PIK3CA</td>\n",
       "      <td></td>\n",
       "      <td>0.639440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000047936</td>\n",
       "      <td>ROS1</td>\n",
       "      <td></td>\n",
       "      <td>0.637853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000079999</td>\n",
       "      <td>KEAP1</td>\n",
       "      <td></td>\n",
       "      <td>0.636367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000139687</td>\n",
       "      <td>RB1</td>\n",
       "      <td></td>\n",
       "      <td>0.625998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000147889</td>\n",
       "      <td>CDKN2A</td>\n",
       "      <td></td>\n",
       "      <td>0.623320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000105976</td>\n",
       "      <td>MET</td>\n",
       "      <td></td>\n",
       "      <td>0.604498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000143924</td>\n",
       "      <td>EML4</td>\n",
       "      <td></td>\n",
       "      <td>0.601952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000165731</td>\n",
       "      <td>RET</td>\n",
       "      <td></td>\n",
       "      <td>0.599549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000168036</td>\n",
       "      <td>CTNNB1</td>\n",
       "      <td></td>\n",
       "      <td>0.594291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000127616</td>\n",
       "      <td>SMARCA4</td>\n",
       "      <td></td>\n",
       "      <td>0.587920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000073282</td>\n",
       "      <td>TP63</td>\n",
       "      <td></td>\n",
       "      <td>0.584834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000149311</td>\n",
       "      <td>ATM</td>\n",
       "      <td></td>\n",
       "      <td>0.582963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000157168</td>\n",
       "      <td>NRG1</td>\n",
       "      <td></td>\n",
       "      <td>0.581673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000176890</td>\n",
       "      <td>TYMS</td>\n",
       "      <td></td>\n",
       "      <td>0.576543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000160201</td>\n",
       "      <td>U2AF1</td>\n",
       "      <td></td>\n",
       "      <td>0.557769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000164362</td>\n",
       "      <td>TERT</td>\n",
       "      <td></td>\n",
       "      <td>0.554376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>lung adenocarcinoma</td>\n",
       "      <td>ENSG00000101825</td>\n",
       "      <td>MXRA5</td>\n",
       "      <td></td>\n",
       "      <td>0.545087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Disease Name        Target ID Approved Symbol Name     Score\n",
       "0   lung adenocarcinoma  ENSG00000146648            EGFR       0.826716\n",
       "1   lung adenocarcinoma  ENSG00000141510            TP53       0.767934\n",
       "2   lung adenocarcinoma  ENSG00000133703            KRAS       0.756403\n",
       "3   lung adenocarcinoma  ENSG00000157764            BRAF       0.729146\n",
       "4   lung adenocarcinoma  ENSG00000118046           STK11       0.722491\n",
       "5   lung adenocarcinoma  ENSG00000171094             ALK       0.686429\n",
       "6   lung adenocarcinoma  ENSG00000141736           ERBB2       0.663012\n",
       "7   lung adenocarcinoma  ENSG00000182872           RBM10       0.651032\n",
       "8   lung adenocarcinoma  ENSG00000121879          PIK3CA       0.639440\n",
       "9   lung adenocarcinoma  ENSG00000047936            ROS1       0.637853\n",
       "10  lung adenocarcinoma  ENSG00000079999           KEAP1       0.636367\n",
       "11  lung adenocarcinoma  ENSG00000139687             RB1       0.625998\n",
       "12  lung adenocarcinoma  ENSG00000147889          CDKN2A       0.623320\n",
       "13  lung adenocarcinoma  ENSG00000105976             MET       0.604498\n",
       "14  lung adenocarcinoma  ENSG00000143924            EML4       0.601952\n",
       "15  lung adenocarcinoma  ENSG00000165731             RET       0.599549\n",
       "16  lung adenocarcinoma  ENSG00000168036          CTNNB1       0.594291\n",
       "17  lung adenocarcinoma  ENSG00000127616         SMARCA4       0.587920\n",
       "18  lung adenocarcinoma  ENSG00000073282            TP63       0.584834\n",
       "19  lung adenocarcinoma  ENSG00000149311             ATM       0.582963\n",
       "20  lung adenocarcinoma  ENSG00000157168            NRG1       0.581673\n",
       "21  lung adenocarcinoma  ENSG00000176890            TYMS       0.576543\n",
       "22  lung adenocarcinoma  ENSG00000160201           U2AF1       0.557769\n",
       "23  lung adenocarcinoma  ENSG00000164362            TERT       0.554376\n",
       "24  lung adenocarcinoma  ENSG00000101825           MXRA5       0.545087"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "import pandas as pd\n",
    "# Extract the rows from the JSON data\n",
    "rows = json_data['data']['disease']['associatedTargets']['rows']\n",
    "\n",
    "# Extracting additional information from the JSON data and creating columns\n",
    "target_ids = [row['target'].get('id', '') for row in rows]\n",
    "approved_symbols = [row['target'].get('approvedSymbol', '') for row in rows]\n",
    "names = [row['target'].get('name', '') for row in rows]\n",
    "scores = [row.get('score', '') for row in rows]\n",
    "disease_name = json_data['data']['disease']['name']\n",
    "\n",
    "# Create a DataFrame with the extracted information\n",
    "df = pd.DataFrame({\n",
    "    'Disease Name': [disease_name] * len(rows),  # Repeat disease name for all rows\n",
    "    'Target ID': target_ids,\n",
    "    'Approved Symbol': approved_symbols,\n",
    "    'Name': names,\n",
    "    'Score': scores\n",
    "})\n",
    "\n",
    "df_sorted = df.sort_values(by='Score', ascending=False)\n",
    "\n",
    "# Display the DataFrame\n",
    "display(df_sorted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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