class_docstr: Make async prediction requests to an Amazon SageMaker endpoint.
class_docstr_tok: ["Make","async","prediction","requests","to","an","Amazon","SageMaker","endpoint","."]
docstr: Updates the DataCaptureConfig for the Predictor's associated Amazon SageMaker Endpoint.
  
  Update is done using the provided DataCaptureConfig.
  
  Args:
      data_capture_config (sagemaker.model_monitor.DataCaptureConfig): The
          DataCaptureConfig to update the predictor's endpoint to use.
docstr_tok: ["Updates","the","DataCaptureConfig","for","the","Predictor","'s","associated","Amazon","SageMaker","Endpoint",".","Update","is","done","using","the","provided","DataCaptureConfig",".","Args",":","data_capture_config","(","sagemaker.model_monitor.DataCaptureConfig",")",":","The","DataCaptureConfig","to","update","the","predictor","'s","endpoint","to","use","."]
code: def update_data_capture_config(self, data_capture_config):
      """Updates the DataCaptureConfig for the Predictor's associated Amazon SageMaker Endpoint.
  
      Update is done using the provided DataCaptureConfig.
  
      Args:
          data_capture_config (sagemaker.model_monitor.DataCaptureConfig): The
              DataCaptureConfig to update the predictor's endpoint to use.
      """
      self.predictor.update_data_capture_config(data_capture_config)
  
imports: from __future__ import absolute_import
  import threading
  import time
  import uuid
  from botocore.exceptions import WaiterError
  from sagemaker import s3
  from sagemaker.exceptions import PollingTimeoutError, AsyncInferenceModelError
  from sagemaker.async_inference import WaiterConfig, AsyncInferenceResponse
  from sagemaker.s3 import parse_s3_url
  from sagemaker.session import Session
  from sagemaker.utils import name_from_base, sagemaker_timestamp, format_tags
label_desc: Use image node_id 15 for calling the AsyncPredictor obj's underlying member method code with example usage: obj.update_data_capture_config(data_capture_config) without return types