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: Create an instance of EoTTensorFlowV2.
  
  :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 apply_fit: True if applied during fitting/training.
  :param apply_predict: True if applied during predicting.
docstr_tok: ["Create","an","instance","of","EoTTensorFlowV2",".",":","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","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],
      apply_fit: bool = False,
      apply_predict: bool = True,
  ) -> None:
      """
      Create an instance of EoTTensorFlowV2.
  
      :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 apply_fit: True if applied during fitting/training.
      :param apply_predict: True if applied during predicting.
      """
      super().__init__(
          is_fitted=True,
          apply_fit=apply_fit,
          apply_predict=apply_predict,
      )
  
      self.nb_samples = nb_samples
      self.clip_values = clip_values
      EoTTensorFlowV2._check_params(self)
  
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 1 to create a new EoTTensorFlowV2 object from inherited base classes: PreprocessorTensorFlowV2 with example: obj = EoTTensorFlowV2(nb_samples, clip_values, apply_fit, apply_predict)