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: Initialize a Transformer object.
  
  Algorithm-specific configuration parameters should be passed here.
docstr_tok: ["Initialize","a","Transformer","object",".","Algorithm-specific","configuration","parameters","should","be","passed","here","."]
code: def __init__(self, **kwargs):
      """Initialize a Transformer object.
  
      Algorithm-specific configuration parameters should be passed here.
      """
      self._params = kwargs
  
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 1 to create a new Transformer object from inherited base classes: BaseClass with example: obj = Transformer()