class_docstr: Implement the MP3 compression defense approach.
docstr: Create an instance of MP3 compression.
  
  :param sample_rate: Specifies the sampling rate of sample.
  :param channels_first: Set channels first or last.
  :param apply_fit: True if applied during fitting/training.
  :param apply_predict: True if applied during predicting.
  :param device_type: Type of device on which the classifier is run, either `gpu` or `cpu`.
  :param verbose: Show progress bars.
code: def __init__(
      self,
      sample_rate: int,
      channels_first: bool = False,
      apply_fit: bool = False,
      apply_predict: bool = True,
      device_type: str = "gpu",
      verbose: bool = False,
  ):
      """
      Create an instance of MP3 compression.
  
      :param sample_rate: Specifies the sampling rate of sample.
      :param channels_first: Set channels first or last.
      :param apply_fit: True if applied during fitting/training.
      :param apply_predict: True if applied during predicting.
      :param device_type: Type of device on which the classifier is run, either `gpu` or `cpu`.
      :param verbose: Show progress bars.
      """
      from torch.autograd import Function
  
      super().__init__(
          device_type=device_type,
          is_fitted=True,
          apply_fit=apply_fit,
          apply_predict=apply_predict,
      )
      self.channels_first = channels_first
      self.sample_rate = sample_rate
      self.verbose = verbose
      self._check_params()
  
      self.compression_numpy = Mp3Compression(
          sample_rate=sample_rate,
          channels_first=channels_first,
          apply_fit=apply_fit,
          apply_predict=apply_predict,
          verbose=verbose,
      )
  
      class CompressionPyTorchNumpy(Function):  # pylint: disable=W0223
          """
          Function running Preprocessor.
          """
  
          @staticmethod
          def forward(ctx, input):  # pylint: disable=W0622,W0221
              numpy_input = input.detach().cpu().numpy()
              result, _ = self.compression_numpy(numpy_input)
              return input.new(result)
  
          @staticmethod
          def backward(ctx, grad_output):  # pylint: disable=W0221
              numpy_go = grad_output.cpu().numpy()
              # np.expand_dims(input, axis=[0, 2])
              result = self.compression_numpy.estimate_gradient(
                  None, numpy_go
              )
              # result = result.squeeze()
              return grad_output.new(result)
  
      self._compression_pytorch_numpy = CompressionPyTorchNumpy
  
file_path: adversarial-robustness-toolbox/art/defences/preprocessor/mp3_compression_pytorch.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import TYPE_CHECKING, Optional, Tuple
  from art.defences.preprocessor.mp3_compression import Mp3Compression
  from art.defences.preprocessor.preprocessor import PreprocessorPyTorch
label_desc: Use image node_id 1 to create a new Mp3CompressionPyTorch object from inherited base classes: PreprocessorPyTorch with example: obj = Mp3CompressionPyTorch(sample_rate, channels_first, apply_fit, apply_predict, device_type, verbose)