class_docstr: A class for creating and interacting with SageMaker AutoML jobs.
docstr: Validate if problem_type and job_objective are both None or are both provided.
  
  Args:
      problem_type (str): The type of problem of this AutoMLJob. Valid values are
          "Regression", "BinaryClassification", "MultiClassClassification".
      job_objective (dict): AutoMLJob objective, contains "AutoMLJobObjectiveType" (optional),
          "MetricName" and "Value".
  
  Raises (ValueError): raises ValueError if one of problem_type and job_objective is provided
      while the other is None.
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code: def _check_problem_type_and_job_objective(
      self, problem_type, job_objective
  ):
      """Validate if problem_type and job_objective are both None or are both provided.
  
      Args:
          problem_type (str): The type of problem of this AutoMLJob. Valid values are
              "Regression", "BinaryClassification", "MultiClassClassification".
          job_objective (dict): AutoMLJob objective, contains "AutoMLJobObjectiveType" (optional),
              "MetricName" and "Value".
  
      Raises (ValueError): raises ValueError if one of problem_type and job_objective is provided
          while the other is None.
      """
      if not (problem_type and job_objective) and (
          problem_type or job_objective
      ):
          raise ValueError(
              "One of problem type and objective metric provided. "
              "Either both of them should be provided or none of them should be provided."
          )
  
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 9 for calling the AutoML obj's underlying member method code with example usage: obj._check_problem_type_and_job_objective(problem_type, job_objective) without return types