class_docstr: Implementation of SmoothAdv training, as introduced in Salman et al. (2019)
  
  | Paper link: https://arxiv.org/abs/1906.04584
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docstr: Create a SmoothAdv classifier.
  
  :param model: PyTorch model. The output of the model can be logits, probabilities or anything else. Logits
         output should be preferred where possible to ensure attack efficiency.
  :param loss: The loss function for which to compute gradients for training. The target label must be raw
         categorical, i.e. not converted to one-hot encoding.
  :param input_shape: The shape of one input instance.
  :param nb_classes: The number of classes of the model.
  :param optimizer: The optimizer used to train the classifier.
  :param channels_first: Set channels first or last.
  :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
         maximum values allowed for features. If floats are provided, these will be used as the range of all
         features. If arrays are provided, each value will be considered the bound for a feature, thus
         the shape of clip values needs to match the total number of features.
  :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
  :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
  :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
         used for data preprocessing. The first value will be subtracted from the input. The input will then
         be divided by the second one.
  :param device_type: Type of device on which the classifier is run, either `gpu` or `cpu`.
  :param sample_size: Number of samples for smoothing.
  :param scale: Standard deviation of Gaussian noise added.
  :param alpha: The failure probability of smoothing.
  :param epsilon: The maximum perturbation that can be induced.
  :param num_noise_vec: The number of noise vectors.
  :param num_steps: The number of attack updates.
  :param warmup: The warm-up strategy that is gradually increased up to the original value.
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code: def __init__(
      self,
      model: "torch.nn.Module",
      loss: "torch.nn.modules.loss._Loss",
      input_shape: Tuple[int, ...],
      nb_classes: int,
      optimizer: Optional["torch.optim.Optimizer"] = None,
      channels_first: bool = True,
      clip_values: Optional["CLIP_VALUES_TYPE"] = None,
      preprocessing_defences: Union[
          "Preprocessor", List["Preprocessor"], None
      ] = None,
      postprocessing_defences: Union[
          "Postprocessor", List["Postprocessor"], None
      ] = None,
      preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
      device_type: str = "gpu",
      sample_size: int = 32,
      scale: float = 0.1,
      alpha: float = 0.001,
      epsilon: float = 1.0,
      num_noise_vec: int = 1,
      num_steps: int = 10,
      warmup: int = 1,
  ) -> None:
      """
      Create a SmoothAdv classifier.
  
      :param model: PyTorch model. The output of the model can be logits, probabilities or anything else. Logits
             output should be preferred where possible to ensure attack efficiency.
      :param loss: The loss function for which to compute gradients for training. The target label must be raw
             categorical, i.e. not converted to one-hot encoding.
      :param input_shape: The shape of one input instance.
      :param nb_classes: The number of classes of the model.
      :param optimizer: The optimizer used to train the classifier.
      :param channels_first: Set channels first or last.
      :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
             maximum values allowed for features. If floats are provided, these will be used as the range of all
             features. If arrays are provided, each value will be considered the bound for a feature, thus
             the shape of clip values needs to match the total number of features.
      :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
      :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
      :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
             used for data preprocessing. The first value will be subtracted from the input. The input will then
             be divided by the second one.
      :param device_type: Type of device on which the classifier is run, either `gpu` or `cpu`.
      :param sample_size: Number of samples for smoothing.
      :param scale: Standard deviation of Gaussian noise added.
      :param alpha: The failure probability of smoothing.
      :param epsilon: The maximum perturbation that can be induced.
      :param num_noise_vec: The number of noise vectors.
      :param num_steps: The number of attack updates.
      :param warmup: The warm-up strategy that is gradually increased up to the original value.
      """
      super().__init__(
          model=model,
          loss=loss,
          input_shape=input_shape,
          nb_classes=nb_classes,
          optimizer=optimizer,
          channels_first=channels_first,
          clip_values=clip_values,
          preprocessing_defences=preprocessing_defences,
          postprocessing_defences=postprocessing_defences,
          preprocessing=preprocessing,
          device_type=device_type,
          sample_size=sample_size,
          scale=scale,
          alpha=alpha,
      )
      self.epsilon = epsilon
      self.num_noise_vec = num_noise_vec
      self.num_steps = num_steps
      self.warmup = warmup
  
      classifier = PyTorchClassifier(
          model=model,
          loss=loss,
          input_shape=input_shape,
          nb_classes=nb_classes,
          optimizer=optimizer,
          channels_first=channels_first,
          clip_values=clip_values,
          preprocessing_defences=preprocessing_defences,
          postprocessing_defences=postprocessing_defences,
          preprocessing=preprocessing,
          device_type=device_type,
      )
      self.attack = ProjectedGradientDescent(
          classifier, eps=self.epsilon, max_iter=1, verbose=False
      )
  
file_path: adversarial-robustness-toolbox/art/estimators/certification/randomized_smoothing/smooth_adv/pytorch.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import List, Optional, Tuple, Union, TYPE_CHECKING
  from tqdm.auto import trange
  import numpy
  from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent import ProjectedGradientDescent
  from art.estimators.certification.randomized_smoothing.pytorch import PyTorchRandomizedSmoothing
  from art.estimators.classification.pytorch import PyTorchClassifier
  from art.utils import check_and_transform_label_format
label_desc: Use image node_id 1 to create a new PyTorchSmoothAdv object from inherited base classes: PyTorchRandomizedSmoothing with example: obj = PyTorchSmoothAdv(model, loss, input_shape, nb_classes, optimizer, channels_first, clip_values, preprocessing_defences, postprocessing_defences, preprocessing, device_type, sample_size, scale, alpha, epsilon, num_noise_vec, num_steps, warmup)