class_docstr: This module defines a base class for EoT in PyTorch.
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.
code: def _transform(
      self,
      x: "torch.Tensor",
      y: Optional[
          Union["torch.Tensor", List[Dict[str, "torch.Tensor"]]]
      ],
      **kwargs,
  ) -> Tuple[
      "torch.Tensor",
      Optional[Union["torch.Tensor", List[Dict[str, "torch.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/pytorch.py
imports: from abc import abstractmethod
  import logging
  from typing import Dict, List, Optional, Tuple, TYPE_CHECKING, Union
  from art.preprocessing.preprocessing import PreprocessorPyTorch
label_desc: Use image node_id 2 for calling the EoTPyTorch obj's underlying member method code with example usage: obj._transform(x, y) without return types