class_docstr: This module implements Expectation over Transformation preprocessing for image center crop in PyTorch.
docstr: Create an instance of EoTImageCenterCropPyTorch.
  
  :param nb_samples: Number of random samples per input sample.
  :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
                      for features.
  :param size: Maximal size of the crop on all sides of the image in pixels.
  :param label_type: String defining the type of labels. Currently supported: `classification`, `object_detection`
  :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],
      size: int = 5,
      label_type: str = "classification",
      apply_fit: bool = False,
      apply_predict: bool = True,
  ) -> None:
      """
      Create an instance of EoTImageCenterCropPyTorch.
  
      :param nb_samples: Number of random samples per input sample.
      :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
                          for features.
      :param size: Maximal size of the crop on all sides of the image in pixels.
      :param label_type: String defining the type of labels. Currently supported: `classification`, `object_detection`
      :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.size = size
      self.size_range = (0, size)
      self.label_type = label_type
      self._check_params()
  
file_path: adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/image_center_crop/pytorch.py
imports: import logging
  from typing import Dict, List, Optional, TYPE_CHECKING, Tuple, Union
  import numpy
  from art.preprocessing.expectation_over_transformation.pytorch import EoTPyTorch
label_desc: Use image node_id 1 to create a new EoTImageCenterCropPyTorch object from inherited base classes: EoTPyTorch with example: obj = EoTImageCenterCropPyTorch(nb_samples, clip_values, size, label_type, apply_fit, apply_predict)