将A2I（Amazon Augmented AI）与异步textract调用集成的方法如下所示：

1. 从AWS SDK导入boto3和botocore模块
2. 创建一个AsyncAWSClient类，该类继承了textract.AsyncAnalysisClient并覆盖了start_document_analysis方法
3. 在覆盖的start_document_analysis方法中，添加A2I集成的代码，以便在在textract调用成功完成后，在A2I人工智能工作任务流程中创建一个人工智能审核任务
4. 将AsyncAWSClient作为textract.AsyncAnalysisClient的参数传递，并使用textract.start_document_analysis方法调用textract

代码示例：

import boto3
import botocore
import json

s3 = boto3.resource('s3')
textract = boto3.client('textract')
a2i = boto3.client('sagemaker-a2i-runtime')

def start_async_textract_job(bucketName, objectName):
    response = textract.start_document_analysis(
        DocumentLocation={
            'S3Object': {
                'Bucket': bucketName,
                'Name': objectName
            }
        },
        FeatureTypes=['FORMS']
    )
    
    async_client = AsyncAWSClient()
    async_client.start_document_analysis(
        document_location={
            'S3Object': {
                'Bucket': bucketName,
                'Name': objectName
            },
        },
        feature_types=['FORMS'],
        client_request_token=response['JobId']
    )
    
    return response['JobId']

class AsyncAWSClient(textract.AsyncAnalysisClient):
    def start_document_analysis(self, document_location, feature_types, client_request_token=None):
        response = super(AsyncAWSClient, self).start_document_analysis(
            DocumentLocation=document_location,
            FeatureTypes=feature_types,
            ClientRequestToken=client_request_token
        )
        
        human_loop_name = 'textract-job-' + response['JobId']
        flow_definition_arn = ''
        human_loop_input_content = {