source/operators/rekognition/check_label_detection_status.py [20:62]:
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patch_all()

operator_name = os.environ['OPERATOR_NAME']
output_object = OutputHelper(operator_name)

mie_config = json.loads(os.environ['botoConfig'])
config = config.Config(**mie_config)
rek = boto3.client('rekognition', config=config)

def lambda_handler(event, context):
    print("We got the following event:\n", event)
    try:
        status = event["Status"]
        asset_id = event['MetaData']['AssetId']
    except KeyError as e:
        output_object.update_workflow_status("Error")
        output_object.add_workflow_metadata(LabelDetectionError="Missing key {e}".format(e=e))
        raise MasExecutionError(output_object.return_output_object())
    # Images will have already been processed, so return if job status is already set.
    if status == "Complete":
        output_object.update_workflow_status("Complete")
        return output_object.return_output_object()
    try:
        job_id = event["MetaData"]["JobId"]
        workflow_id = event["MetaData"]["WorkflowExecutionId"]
    except KeyError as e:
        output_object.update_workflow_status("Error")
        output_object.add_workflow_metadata(LabelDetectionError="Missing a required metadata key {e}".format(e=e))
        raise MasExecutionError(output_object.return_output_object())
    # Check rekognition job status:
    dataplane = DataPlane()
    pagination_token = ''
    is_paginated = False
    # If pagination token is in event["MetaData"] then use that to start
    # reading reko results from where this Lambda's previous invocation left off.
    if ("PageToken" in event["MetaData"]):
        pagination_token = event["MetaData"]["PageToken"]
        is_paginated = True
    # Read and persist 10 reko pages per invocation of this Lambda
    for page_number in range(11):
        # Get reko results
        print("job id: " + job_id + " page token: " + pagination_token)
        try:
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source/operators/rekognition/check_shot_detection_status.py [20:62]:
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patch_all()

operator_name = os.environ['OPERATOR_NAME']
output_object = OutputHelper(operator_name)

mie_config = json.loads(os.environ['botoConfig'])
config = config.Config(**mie_config)
rek = boto3.client('rekognition', config=config)

def lambda_handler(event, context):
    print("We got the following event:\n", event)
    try:
        status = event["Status"]
        asset_id = event['MetaData']['AssetId']
    except KeyError as e:
        output_object.update_workflow_status("Error")
        output_object.add_workflow_metadata(LabelDetectionError="Missing key {e}".format(e=e))
        raise MasExecutionError(output_object.return_output_object())
    # Images will have already been processed, so return if job status is already set.
    if status == "Complete":
        output_object.update_workflow_status("Complete")
        return output_object.return_output_object()
    try:
        job_id = event["MetaData"]["JobId"]
        workflow_id = event["MetaData"]["WorkflowExecutionId"]
    except KeyError as e:
        output_object.update_workflow_status("Error")
        output_object.add_workflow_metadata(LabelDetectionError="Missing a required metadata key {e}".format(e=e))
        raise MasExecutionError(output_object.return_output_object())
    # Check rekognition job status:
    dataplane = DataPlane()
    pagination_token = ''
    is_paginated = False
    # If pagination token is in event["MetaData"] then use that to start
    # reading reko results from where this Lambda's previous invocation left off.
    if ("PageToken" in event["MetaData"]):
        pagination_token = event["MetaData"]["PageToken"]
        is_paginated = True
    # Read and persist 10 reko pages per invocation of this Lambda
    for page_number in range(11):
        # Get reko results
        print("job id: " + job_id + " page token: " + pagination_token)
        try:
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