docs: def transform(self, data, data_type='S3Prefix', content_type=None, compression_type=None, split_type=None,
                    job_name=None):
          """Start a new transform job.
  
          Args:
              data (str): Input data location in S3.
              data_type (str): What the S3 location defines (default: 'S3Prefix'). Valid values:
  
                  * 'S3Prefix' - the S3 URI defines a key name prefix. All objects with this prefix will be used as
                      inputs for the transform job.
                  * 'ManifestFile' - the S3 URI points to a single manifest file listing each S3 object to use as
                      an input for the transform job.
  
              content_type (str): MIME type of the input data (default: None).
              compression_type (str): Compression type of the input data, if compressed (default: None).
                  Valid values: 'Gzip', None.
              split_type (str): The record delimiter for the input object (default: 'None').
                  Valid values: 'None', 'Line', 'RecordIO', and 'TFRecord'.
              job_name (str): job name (default: None). If not specified, one will be generated.
          """
          local_mode = self.sagemaker_session.local_mode
          if not local_mode and not data.startswith('s3://'):
              raise ValueError('Invalid S3 URI: {}'.format(data))
  
          if job_name is not None:
              self._current_job_name = job_name
          else:
              base_name = self.base_transform_job_name or base_name_from_image(self._retrieve_image_name())
              self._current_job_name = name_from_base(base_name)
  
          if self.output_path is None:
              self.output_path = 's3://{}/{}'.format(self.sagemaker_session.default_bucket(), self._current_job_name)
  
          self.latest_transform_job = _TransformJob.start_new(self, data, data_type, content_type, compression_type,
                                                              split_type)