code: def training_config(estimator, inputs=None, job_name=None, mini_batch_size=None):
      
  
      train_config = training_base_config(estimator, inputs, job_name, mini_batch_size)
  
      train_config['TrainingJobName'] = estimator._current_job_name
  
      if estimator.tags is not None:
          train_config['Tags'] = estimator.tags
  
      return train_config
docs: Export Airflow 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.