class_docstr: This module defines a base class for EoT in TensorFlow v2.
class_docstr_tok: ["This","module","defines","a","base","class","for","EoT","in","TensorFlow","v2","."]
docstr: Internal method implementing the transformation per input sample.
  
  :param x: Input samples.
  :param y: Label of the samples `x`.
  :return: Transformed samples and labels.
docstr_tok: ["Internal","method","implementing","the","transformation","per","input","sample",".",":","param","x",":","Input","samples",".",":","param","y",":","Label","of","the","samples","`","x","`",".",":","return",":","Transformed","samples","and","labels","."]
code: def _transform(
      self, x: "tf.Tensor", y: Optional["tf.Tensor"], **kwargs
  ) -> Tuple["tf.Tensor", Optional["tf.Tensor"]]:
      """
      Internal method implementing the transformation per input sample.
  
      :param x: Input samples.
      :param y: Label of the samples `x`.
      :return: Transformed samples and labels.
      """
      raise NotImplementedError
  
file_path: adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/tensorflow.py
imports: from abc import abstractmethod
  import logging
  from typing import Dict, List, Optional, Tuple, TYPE_CHECKING, Union
  import numpy
  from art.preprocessing.preprocessing import PreprocessorTensorFlowV2
label_desc: Use image node_id 2 for calling the EoTTensorFlowV2 obj's underlying member method code with example usage: obj._transform(x, y) without return types