class_docstr: A class for creating and interacting with SageMaker AutoML jobs.
docstr: Validates the requested inference keys and updates response content.
  
  On validation, also updates the inference containers to emit appropriate response
  content in the inference response.
  
  Args:
      inference_containers (list): list of inference containers
      inference_response_keys (list): list of inference response keys
  
  Raises:
      ValueError: if one or more of inference_response_keys are unsupported by the model
docstr_tok: ["Validates","the","requested","inference","keys","and","updates","response","content",".","On","validation",",","also","updates","the","inference","containers","to","emit","appropriate","response","content","in","the","inference","response",".","Args",":","inference_containers","(","list",")",":","list","of","inference","containers","inference_response_keys","(","list",")",":","list","of","inference","response","keys","Raises",":","ValueError",":","if","one","or","more","of","inference_response_keys","are","unsupported","by","the","model"]
code: def validate_and_update_inference_response(
      cls, inference_containers, inference_response_keys
  ):
      """Validates the requested inference keys and updates response content.
  
      On validation, also updates the inference containers to emit appropriate response
      content in the inference response.
  
      Args:
          inference_containers (list): list of inference containers
          inference_response_keys (list): list of inference response keys
  
      Raises:
          ValueError: if one or more of inference_response_keys are unsupported by the model
      """
      if not inference_response_keys:
          return
  
      cls._check_inference_keys(
          inference_response_keys, inference_containers
      )
  
      previous_container_output = None
  
      for container in inference_containers:
          supported_inference_keys_container = (
              cls._get_supported_inference_keys(container, default=[])
          )
          if not supported_inference_keys_container:
              previous_container_output = None
              continue
          current_container_output = None
          for key in inference_response_keys:
              if key in supported_inference_keys_container:
                  current_container_output = (
                      current_container_output + "," + key
                      if current_container_output
                      else key
                  )
  
          if previous_container_output:
              container["Environment"].update(
                  {
                      "SAGEMAKER_INFERENCE_INPUT": previous_container_output
                  }
              )
          if current_container_output:
              container["Environment"].update(
                  {
                      "SAGEMAKER_INFERENCE_OUTPUT": current_container_output
                  }
              )
          previous_container_output = current_container_output
  
imports: from __future__ import absolute_import
  import logging
  from typing import Optional, List, Dict
  from six import string_types
  from sagemaker import Model, PipelineModel, s3
  from sagemaker.automl.candidate_estimator import CandidateEstimator
  from sagemaker.config import AUTO_ML_ROLE_ARN_PATH, AUTO_ML_KMS_KEY_ID_PATH, AUTO_ML_VPC_CONFIG_PATH, AUTO_ML_VOLUME_KMS_KEY_ID_PATH, AUTO_ML_INTER_CONTAINER_ENCRYPTION_PATH
  from sagemaker.job import _Job
  from sagemaker.session import Session
  from sagemaker.utils import name_from_base, resolve_value_from_config, format_tags, Tags
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.workflow.pipeline_context import runnable_by_pipeline
label_desc: Use image node_id 13 for calling the AutoML obj's underlying member method code with example usage: obj.validate_and_update_inference_response(cls, inference_containers, inference_response_keys) without return types