class_docstr: This module implements EoT of adding shot noise (Poisson) with uniformly sampled rate parameter.
docstr: Create an instance of EoTShotNoiseTensorFlow.
  
  :param nb_samples: Number of random samples per input sample.
  :param clip_values: Tuple of float representing minimum and maximum values of input `(min, max)`.
  :param lam: Range to sample the rate of the Poisson distribution. A single float
              is translated to range [0.0, lam] or a tuple of floats is used to create sampling range
              [lam[0], lam[1]]. The applied delta is sampled uniformly from this range for each image.
  :param apply_fit: True if applied during fitting/training.
  :param apply_predict: True if applied during predicting.
code: def __init__(
      self,
      nb_samples: int,
      clip_values: Tuple[float, float],
      lam: Union[float, Tuple[float, float]],
      apply_fit: bool = False,
      apply_predict: bool = True,
  ) -> None:
      """
      Create an instance of EoTShotNoiseTensorFlow.
  
      :param nb_samples: Number of random samples per input sample.
      :param clip_values: Tuple of float representing minimum and maximum values of input `(min, max)`.
      :param lam: Range to sample the rate of the Poisson distribution. A single float
                  is translated to range [0.0, lam] or a tuple of floats is used to create sampling range
                  [lam[0], lam[1]]. The applied delta is sampled uniformly from this range for each image.
      :param apply_fit: True if applied during fitting/training.
      :param apply_predict: True if applied during predicting.
      """
      super().__init__(
          apply_fit=apply_fit,
          apply_predict=apply_predict,
          nb_samples=nb_samples,
          clip_values=clip_values,
      )
  
      self.lam = lam
      self.lam_range = (
          (0.0, lam) if isinstance(lam, (int, float)) else lam
      )
      self._check_params()
  
file_path: adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/shot_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 1 to create a new EoTShotNoiseTensorFlow object from inherited base classes: EoTTensorFlowV2 with example: obj = EoTShotNoiseTensorFlow(nb_samples, clip_values, lam, apply_fit, apply_predict)