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: Train a model on the input.
  
  Args:
      dataset (Dataset): Input dataset.
  
  Returns:
      Transformer: Returns self.
docstr_tok: ["Train","a","model","on","the","input",".","Args",":","dataset","(","Dataset",")",":","Input","dataset",".","Returns",":","Transformer",":","Returns","self","."]
code: def fit(self, dataset):
      """Train a model on the input.
  
      Args:
          dataset (Dataset): Input dataset.
  
      Returns:
          Transformer: Returns self.
      """
      return self
  
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 2 for calling the Transformer obj's underlying member method code with example usage: obj.fit(dataset) and returns: self