async def METHOD_NAME() -> None:
    import os
    from azure.core.credentials import AzureKeyCredential
    from azure.ai.textanalytics.aio import TextAnalyticsClient
    from azure.ai.textanalytics import (
        AnalyzeHealthcareEntitiesAction,
        RecognizePiiEntitiesAction,
    )
    endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
    key = os.environ["AZURE_LANGUAGE_KEY"]
    text_analytics_client = TextAnalyticsClient(
        endpoint=endpoint,
        credential=AzureKeyCredential(key),
    )
    documents = [
        """
        Patient needs to take 100 mg of ibuprofen, and 3 mg of potassium. Also needs to take
        10 mg of Zocor.
        """,
        """
        Patient needs to take 50 mg of ibuprofen, and 2 mg of Coumadin.
        """
    ]
    async with text_analytics_client:
        poller = await text_analytics_client.begin_analyze_actions(
            documents,
            display_name="Sample Text Analysis",
            actions=[
                AnalyzeHealthcareEntitiesAction(),
                RecognizePiiEntitiesAction(domain_filter="phi"),
            ],
        )
        pages = await poller.result()
        # To enumerate / zip for async, unless you install a third party library,
        # you have to read in all of the elements into memory first.
        # If you're not looking to enumerate / zip, we recommend you just asynchronously
        # loop over it immediately, without going through this step of reading them into memory
        document_results = []
        async for page in pages:
            document_results.append(page)
        for doc, action_results in zip(documents, document_results):
            print(f"\nDocument text: {doc}")
            for result in action_results:
                if result.kind == "Healthcare":
                    print("...Results of Analyze Healthcare Entities Action:")
                    for entity in result.entities:
                        print(f"Entity: {entity.text}")
                        print(f"...Normalized Text: {entity.normalized_text}")
                        print(f"...Category: {entity.category}")
                        print(f"...Subcategory: {entity.subcategory}")
                        print(f"...Offset: {entity.offset}")
                        print(f"...Confidence score: {entity.confidence_score}")
                        if entity.data_sources is not None:
                            print("...Data Sources:")
                            for data_source in entity.data_sources:
                                print(f"......Entity ID: {data_source.entity_id}")
                                print(f"......Name: {data_source.name}")
                        if entity.assertion is not None:
                            print("...Assertion:")
                            print(f"......Conditionality: {entity.assertion.conditionality}")
                            print(f"......Certainty: {entity.assertion.certainty}")
                            print(f"......Association: {entity.assertion.association}")
                    for relation in result.entity_relations:
                        print(f"Relation of type: {relation.relation_type} has the following roles")
                        for role in relation.roles:
                            print(f"...Role '{role.name}' with entity '{role.entity.text}'")
                elif result.kind == "PiiEntityRecognition":
                    print("Results of Recognize PII Entities action:")
                    for pii_entity in result.entities:
                        print(f"......Entity: {pii_entity.text}")
                        print(f".........Category: {pii_entity.category}")
                        print(f".........Confidence Score: {pii_entity.confidence_score}")
                elif result.is_error is True:
                    print(f"...Is an error with code '{result.error.code}' and message '{result.error.message}'")
                print("------------------------------------------")