code: def test_enabling_data_capture_on_endpoint_shows_correct_data_capture_status(
      sagemaker_session, tensorflow_inference_latest_version
  ):
      endpoint_name = unique_name_from_base(
          "sagemaker-tensorflow-serving"
      )
      model_data = sagemaker_session.upload_data(
          path=os.path.join(
              tests.integ.DATA_DIR,
              "tensorflow-serving-test-model.tar.gz",
          ),
          key_prefix="tensorflow-serving/models",
      )
      with tests.integ.timeout.timeout_and_delete_endpoint_by_name(
          endpoint_name, sagemaker_session
      ):
          model = TensorFlowModel(
              model_data=model_data,
              role=ROLE,
              framework_version=tensorflow_inference_latest_version,
              sagemaker_session=sagemaker_session,
          )
          predictor = model.deploy(
              initial_instance_count=INSTANCE_COUNT,
              instance_type=INSTANCE_TYPE,
              endpoint_name=endpoint_name,
          )
  
          endpoint_desc = (
              sagemaker_session.sagemaker_client.describe_endpoint(
                  EndpointName=predictor.endpoint_name
              )
          )
  
          endpoint_config_desc = sagemaker_session.sagemaker_client.describe_endpoint_config(
              EndpointConfigName=endpoint_desc["EndpointConfigName"]
          )
  
          assert endpoint_config_desc.get("DataCaptureConfig") is None
  
          predictor.enable_data_capture()
  
          # Wait for endpoint to finish updating
          # Endpoint update takes ~7min. 25 retries * 60s sleeps = 25min timeout
          for _ in retries(
              max_retry_count=25,
              exception_message_prefix="Waiting for 'InService' endpoint status",
              seconds_to_sleep=60,
          ):
              new_endpoint = (
                  sagemaker_session.sagemaker_client.describe_endpoint(
                      EndpointName=predictor.endpoint_name
                  )
              )
              if new_endpoint["EndpointStatus"] == "InService":
                  break
  
          endpoint_desc = (
              sagemaker_session.sagemaker_client.describe_endpoint(
                  EndpointName=predictor.endpoint_name
              )
          )
  
          endpoint_config_desc = sagemaker_session.sagemaker_client.describe_endpoint_config(
              EndpointConfigName=endpoint_desc["EndpointConfigName"]
          )
  
          assert endpoint_config_desc["DataCaptureConfig"][
              "EnableCapture"
          ]
  
file_path: sagemaker-python-sdk/tests/integ/test_data_capture_config.py
imports: from __future__ import absolute_import
  import os
  import sagemaker
  import tests.integ
  import tests.integ.timeout
  from sagemaker.model_monitor import DataCaptureConfig, NetworkConfig
  from sagemaker.tensorflow.model import TensorFlowModel
  from sagemaker.utils import unique_name_from_base
  from tests.integ.retry import retries
label_desc: Use image node_id 1 for calling a global function with example usage: test_enabling_data_capture_on_endpoint_shows_correct_data_capture_status(sagemaker_session, tensorflow_inference_latest_version) without return types