class_docstr: Implement the MP3 compression defense approach.
docstr: Apply MP3 compression to sample `x`.
  
  :param x: Sample to compress with shape `(length, channel)` or an array of sample arrays with shape
            (length,) or (length, channel).
  :param y: Labels of the sample `x`. This function does not affect them in any way.
  :return: Compressed sample.
code: def forward(
      self, x: "torch.Tensor", y: Optional["torch.Tensor"] = None
  ) -> Tuple["torch.Tensor", Optional["torch.Tensor"]]:
      """
      Apply MP3 compression to sample `x`.
  
      :param x: Sample to compress with shape `(length, channel)` or an array of sample arrays with shape
                (length,) or (length, channel).
      :param y: Labels of the sample `x`. This function does not affect them in any way.
      :return: Compressed sample.
      """
      import torch
  
      ndim = x.ndim
  
      if ndim == 1:
          x = torch.unsqueeze(x, dim=0)
          if self.channels_first:
              dim = 1
          else:
              dim = 2
          x = torch.unsqueeze(x, dim=dim)
  
      x_compressed = self._compression_pytorch_numpy.apply(x)
  
      if ndim == 1:
          x_compressed = torch.squeeze(x_compressed)
  
      return x_compressed, y
  
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 2 for calling the Mp3CompressionPyTorch obj's underlying member method code with example usage: obj.forward(x, y) and returns: x_compressed,  y