code: def check_one_shot_input(data, target, backgrounds):
      if backgrounds is not None and not (
          isinstance(backgrounds, _tc.SArray)
      ):
          raise TypeError("'backgrounds' must be None or an SArray.")
      if isinstance(backgrounds, _tc.SArray) and len(backgrounds) == 0:
          raise _ToolkitError(
              "Unable to train with no background images"
          )
      if not isinstance(target, str):
          raise TypeError("'target' must be of type string.")
      if isinstance(data, _tc.SFrame):
          _tkutl._raise_error_if_column_exists(
              data, target, "data", target
          )
          image_column_name = _tkutl._find_only_image_column(data)
          target_column_name = target
          dataset_to_augment = data
      elif isinstance(data, _tc.Image):
          image_column_name = "image"
          target_column_name = "target"
          dataset_to_augment = _tc.SFrame(
              {image_column_name: [data], target_column_name: [target]}
          )
      else:
          raise TypeError("'data' must be of type SFrame or Image.")
      return dataset_to_augment, image_column_name, target_column_name
  
imports: import turicreate
  import turicreate.toolkits._internal_utils
  from turicreate.toolkits._main import ToolkitError
label_desc: Use image node_id 1 for calling a global function with example usage: check_one_shot_input(data, target, backgrounds) and returns: dataset_to_augment,  image_column_name,  target_column_name