class_docstr: A class for creating and interacting with SageMaker AutoML jobs.
docstr: Checks if the pipeline supports the inference keys for the containers.
  
  Given inference response keys and list of containers, determines whether
  the keys are supported.
  
  Args:
      inference_response_keys (list): List of keys for inference response content.
      containers (list): list of inference container.
  
  Raises:
      ValueError, if one or more keys in inference_response_keys are not supported
      the inference pipeline.
docstr_tok: ["Checks","if","the","pipeline","supports","the","inference","keys","for","the","containers",".","Given","inference","response","keys","and","list","of","containers",",","determines","whether","the","keys","are","supported",".","Args",":","inference_response_keys","(","list",")",":","List","of","keys","for","inference","response","content",".","containers","(","list",")",":","list","of","inference","container",".","Raises",":","ValueError",",","if","one","or","more","keys","in","inference_response_keys","are","not","supported","the","inference","pipeline","."]
code: def _check_inference_keys(cls, inference_response_keys, containers):
      """Checks if the pipeline supports the inference keys for the containers.
  
      Given inference response keys and list of containers, determines whether
      the keys are supported.
  
      Args:
          inference_response_keys (list): List of keys for inference response content.
          containers (list): list of inference container.
  
      Raises:
          ValueError, if one or more keys in inference_response_keys are not supported
          the inference pipeline.
      """
      if not inference_response_keys:
          return
      try:
          supported_inference_keys = cls._get_supported_inference_keys(
              container=containers[-1]
          )
      except KeyError:
          raise ValueError(
              "The inference model does not support selection of inference content beyond "
              "it's default content. Please retry without setting "
              "inference_response_keys key word argument."
          )
      bad_keys = []
      for key in inference_response_keys:
          if key not in supported_inference_keys:
              bad_keys.append(key)
  
      if bad_keys:
          raise ValueError(
              "Requested inference output keys [{bad_keys_str}] are unsupported. "
              "The supported inference keys are [{allowed_keys_str}]".format(
                  bad_keys_str=", ".join(bad_keys),
                  allowed_keys_str=", ".join(supported_inference_keys),
              )
          )
  
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 12 for calling the AutoML obj's underlying member method code with example usage: obj._check_inference_keys(cls, inference_response_keys, containers) without return types