class_docstr: Abstract base class for transformers.
  
  Transformers are an abstraction for any process which acts on a
  :obj:`Dataset` and returns a new, modified Dataset. This definition
  encompasses pre-processing, in-processing, and post-processing algorithms.
docstr: Return a new dataset generated by running this Transformer on the
  input.
  
  This function could return different `dataset.features`,
  `dataset.labels`, or both.
  
  Args:
      dataset (Dataset): Input dataset.
  
  Returns:
      Dataset: Output dataset. `metadata` should reflect the details of
      this transformation.
docstr_tok: ["Return","a","new","dataset","generated","by","running","this","Transformer","on","the","input",".","This","function","could","return","different","`","dataset.features","`",",","`","dataset.labels","`",",","or","both",".","Args",":","dataset","(","Dataset",")",":","Input","dataset",".","Returns",":","Dataset",":","Output","dataset",".","`","metadata","`","should","reflect","the","details","of","this","transformation","."]
code: def transform(self, dataset):
      """Return a new dataset generated by running this Transformer on the
      input.
  
      This function could return different `dataset.features`,
      `dataset.labels`, or both.
  
      Args:
          dataset (Dataset): Input dataset.
  
      Returns:
          Dataset: Output dataset. `metadata` should reflect the details of
          this transformation.
      """
      raise NotImplementedError(
          "'transform' is not supported for this class."
          " Perhaps you meant 'predict' or 'fit_transform' instead?"
      )
  
file_path: AIF360/aif360/algorithms/transformer.py
imports: from abc import abstractmethod
  from functools import wraps
  from aif360.datasets import Dataset
  from aif360.decorating_metaclass import ApplyDecorator
label_desc: Use image node_id 4 for calling the Transformer obj's underlying member method code with example usage: obj.transform(dataset) without return types