class_docstr: This module defines a base class for EoT in PyTorch.
code: def _check_params(self) -> None:
      if not isinstance(self.nb_samples, int) or self.nb_samples < 1:
          raise ValueError(
              "The number of samples needs to be an integer greater than or equal to 1."
          )
  
      if not isinstance(self.clip_values, tuple) or (
          len(self.clip_values) != 2
          or not isinstance(self.clip_values[0], (int, float))
          or not isinstance(self.clip_values[1], (int, float))
          or self.clip_values[0] > self.clip_values[1]
      ):
          raise ValueError(
              "The argument `clip_Values` has to be a float or tuple of two float values as (min, max)."
          )
  
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 4 for calling the EoTPyTorch obj's underlying member method code with example usage: obj._check_params() without return types