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Add management sample (#13)
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‎README.md

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@@ -15,6 +15,7 @@ Azure AI Content Understanding is a new Generative AI-based [Azure AI service](h
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| [content_extraction.ipynb](notebooks/content_extraction.ipynb) | In this sample we will show content understanding API can help you get semantic information from your file. For example OCR with table in document, audio transcription, and face analysis in video. |
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| [field_extraction.ipynb](notebooks/field_extraction.ipynb) | In this sample we will show how to create an analyzer to extract fields in your file. For example invoice amount in the document, how many people in an image, names mentioned in an audio, or summary of a video. You can customize the fields by creating your own analyzer template. |
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| [analyzer_training.ipynb](notebooks/analyzer_training.ipynb) | If you want to futher boost the performance for field extraction, we can do training when you provide few labeled samples to the API. Note: This feature is available to document scenario now. |
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| [management.ipynb](notebooks/management.ipynb) | This sample will demo how to create a minimal analyzer, list all the analyzers in your resource, and delete the analyzer you don't need. |
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## Getting started

‎notebooks/management.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Manage Analyzers in Your Resource"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This notebook demo how to create a simple analyzer and manage its lifecycle."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Prerequisites\n",
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"1. Ensure Azure AI service is configured following [steps](../README.md#configure-azure-ai-service-resource)\n",
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"2. Install the required packages to run the sample."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -r ../requirements.txt"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Create Azure AI Content Understanding Client\n",
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"\n",
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"> The [AzureContentUnderstandingClient](../python/content_understanding_client.py) is a utility class containing functions to interact with the Content Understanding API. Before the official release of the Content Understanding SDK, it can be regarded as a lightweight SDK.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import logging\n",
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"import json\n",
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"import os\n",
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"import sys\n",
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"from pathlib import Path\n",
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"from dotenv import find_dotenv, load_dotenv\n",
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"from azure.identity import DefaultAzureCredential, get_bearer_token_provider\n",
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"\n",
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"load_dotenv(find_dotenv())\n",
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"logging.basicConfig(level=logging.INFO)\n",
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"\n",
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"AZURE_AI_ENDPOINT = os.getenv(\"AZURE_AI_ENDPOINT\")\n",
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"AZURE_AI_API_VERSION = os.getenv(\"AZURE_AI_API_VERSION\", \"2024-12-01-preview\")\n",
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"\n",
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"# Add the parent directory to the path to use shared modules\n",
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"parent_dir = Path(Path.cwd()).parent\n",
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"sys.path.append(str(parent_dir))\n",
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"from python.content_understanding_client import AzureContentUnderstandingClient\n",
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"\n",
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"credential = DefaultAzureCredential()\n",
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"token_provider = get_bearer_token_provider(credential, \"https://cognitiveservices.azure.com/.default\")\n",
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"\n",
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"client = AzureContentUnderstandingClient(\n",
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" endpoint=AZURE_AI_ENDPOINT,\n",
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" api_version=AZURE_AI_API_VERSION,\n",
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" token_provider=token_provider,\n",
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" x_ms_useragent=\"azure-ai-content-understanding-python/field_extraction\",\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Create a simple analyzer\n",
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"We first create an analyzer from a template to extract invoice fields."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import uuid\n",
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"\n",
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"ANALYZER_TEMPLATE = \"../analyzer_templates/invoice.json\"\n",
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"ANALYZER_ID = \"analyzer-management-sample-\" + str(uuid.uuid4())\n",
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"\n",
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"response = client.begin_create_analyzer(ANALYZER_ID, analyzer_template_path=ANALYZER_TEMPLATE)\n",
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"result = client.poll_result(response)\n",
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"\n",
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"print(json.dumps(result, indent=2))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## List all analyzers created in your resource"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"After the analyzer is successfully created, we can use it to analyze our input files."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"response = client.get_all_analyzers()\n",
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"print(f\"Number of analyzers in your resource: {len(response['value'])}\")\n",
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"print(f\"First 3 analyzer details: {json.dumps(response['value'][:3], indent=2)}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Get analyzer details with id\n",
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"\n",
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"Remember the analyzer id when you create it. You can use the id to look up detail analyzer definitions afterwards."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"result = client.get_analyzer_detail_by_id(ANALYZER_ID)\n",
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"print(json.dumps(result, indent=2))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Delete Analyzer\n",
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"If you don't need an analyzer anymore, delete it with its id."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"client.delete_analyzer(ANALYZER_ID)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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