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: "tf.Tensor", y: Optional["tf.Tensor"], **kwargs
  ) -> Tuple["tf.Tensor", Optional["tf.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 tensorflow as tf
  
      std_i = np.random.uniform(
          low=self.std_range[0], high=self.std_range[1]
      )
      delta_i = tf.random.normal(
          shape=x.shape, mean=0.0, stddev=std_i, seed=None
      )
      return (
          tf.clip_by_value(
              x + delta_i,
              clip_value_min=self.clip_values[0],
              clip_value_max=self.clip_values[1],
          ),
          y,
      )
  
file_path: adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/gaussian_noise/tensorflow.py
imports: import logging
  from typing import Tuple, Union, TYPE_CHECKING, Optional
  import numpy
  from art.preprocessing.expectation_over_transformation.tensorflow import EoTTensorFlowV2
label_desc: Use image node_id 2 for calling the EoTGaussianNoiseTensorFlow obj's underlying member method code with example usage: obj._transform(x, y) and returns: tf,  y