docs: def prepare_amazon_algorithm_estimator(estimator, inputs, mini_batch_size=None):
      """ Set up amazon algorithm estimator, adding the required `feature_dim` hyperparameter from training data.
  
      Args:
          estimator (sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase):
              An estimator for a built-in Amazon algorithm to get information from and update.
          inputs: The training data.
              * (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.
      """
      if isinstance(inputs, list):
          for record in inputs:
              if isinstance(record, amazon_estimator.RecordSet) and record.channel == 'train':
                  estimator.feature_dim = record.feature_dim
                  break
      elif isinstance(inputs, amazon_estimator.RecordSet):
          estimator.feature_dim = inputs.feature_dim
      else:
          raise TypeError('Training data must be represented in RecordSet or list of RecordSets')
      estimator.mini_batch_size = mini_batch_size