docs: def training_base_config(estimator, inputs=None, job_name=None, mini_batch_size=None):
      """Export Airflow base training config from an estimator
  
      Args:
          estimator (sagemaker.estimator.EstimatorBase):
              The estimator to export training config from. Can be a BYO estimator,
              Framework estimator or Amazon algorithm estimator.
          inputs: Information about the training data. Please refer to the ``fit()`` method of
                  the associated estimator, as this can take any of the following forms:
  
              * (str) - The S3 location where training data is saved.
              * (dict[str, str] or dict[str, sagemaker.session.s3_input]) - If using multiple channels for
                  training data, you can specify a dict mapping channel names
                  to strings or :func:`~sagemaker.session.s3_input` objects.
              * (sagemaker.session.s3_input) - Channel configuration for S3 data sources that can provide
                  additional information about the training dataset. See :func:`sagemaker.session.s3_input`
                  for full details.
              * (sagemaker.amazon.amazon_estimator.RecordSet) - A collection of
                  Amazon :class:~`Record` objects serialized and stored in S3.
                  For use with an estimator for an Amazon algorithm.
              * (list[sagemaker.amazon.amazon_estimator.RecordSet]) - A list of
                  :class:~`sagemaker.amazon.amazon_estimator.RecordSet` objects, where each instance is
                  a different channel of training data.
  
          job_name (str): Specify a training job name if needed.
          mini_batch_size (int): Specify this argument only when estimator is a built-in estimator of an
              Amazon algorithm. For other estimators, batch size should be specified in the estimator.
  
      Returns:
          dict: Training config that can be directly used by SageMakerTrainingOperator in Airflow.
      """
      default_bucket = estimator.sagemaker_session.default_bucket()
      s3_operations = {}
  
      if job_name is not None:
          estimator._current_job_name = job_name
      else:
          base_name = estimator.base_job_name or utils.base_name_from_image(estimator.train_image())
          estimator._current_job_name = utils.name_from_base(base_name)
  
      if estimator.output_path is None:
          estimator.output_path = 's3://{}/'.format(default_bucket)
  
      if isinstance(estimator, sagemaker.estimator.Framework):
          prepare_framework(estimator, s3_operations)
  
      elif isinstance(estimator, amazon_estimator.AmazonAlgorithmEstimatorBase):
          prepare_amazon_algorithm_estimator(estimator, inputs, mini_batch_size)
      job_config = job._Job._load_config(inputs, estimator, expand_role=False, validate_uri=False)
  
      train_config = {
          'AlgorithmSpecification': {
              'TrainingImage': estimator.train_image(),
              'TrainingInputMode': estimator.input_mode
          },
          'OutputDataConfig': job_config['output_config'],
          'StoppingCondition': job_config['stop_condition'],
          'ResourceConfig': job_config['resource_config'],
          'RoleArn': job_config['role'],
      }
  
      if job_config['input_config'] is not None:
          train_config['InputDataConfig'] = job_config['input_config']
  
      if job_config['vpc_config'] is not None:
          train_config['VpcConfig'] = job_config['vpc_config']
  
      if estimator.hyperparameters() is not None:
          hyperparameters = {str(k): str(v) for (k, v) in estimator.hyperparameters().items()}
  
      if hyperparameters and len(hyperparameters) > 0:
          train_config['HyperParameters'] = hyperparameters
  
      if s3_operations:
          train_config['S3Operations'] = s3_operations
  
      return train_config