class_docstr: This module implements EoT of adding Gaussian noise with uniformly sampled standard deviation.
docstr: Transformation of an image with randomly sampled Gaussian noise.
  
  :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"]]]],
  ]:
      """
      Transformation of an image with randomly sampled Gaussian noise.
  
      :param x: Input samples.
      :param y: Label of the samples `x`.
      :return: Transformed samples and labels.
      """
      import torch
  
      std_i = np.random.uniform(
          low=self.std_range[0], high=self.std_range[1]
      )
      delta_i = torch.normal(
          mean=torch.zeros_like(x), std=torch.ones_like(x) * std_i
      )
      return (
          torch.clamp(
              x + delta_i,
              min=self.clip_values[0],
              max=self.clip_values[1],
          ),
          y,
      )
  
file_path: adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/gaussian_noise/pytorch.py
imports: import logging
  from typing import Dict, List, Tuple, Union, TYPE_CHECKING, Optional
  import numpy
  from art.preprocessing.expectation_over_transformation.pytorch import EoTPyTorch
label_desc: Use image node_id 2 for calling the EoTGaussianNoisePyTorch obj's underlying member method code with example usage: obj._transform(x, y) and returns: torch,  y