class_docstr: Model for enhancement based on Wide ResNet.
  
  Full model description at: https://arxiv.org/pdf/2112.06068.pdf
  
  Arguments
  ---------
  n_fft : int
      Number of points in the fourier transform, see ``speechbrain.processing.features.STFT``
  win_length : int
      Length of stft window in ms, see ``speechbrain.processing.features.STFT``
  hop_length : int
      Time between windows in ms, see ``speechbrain.processing.features.STFT``
  sample_rate : int
      Number of samples per second of input audio.
  channel_counts : list of ints
      Number of output channels in each CNN block. Determines number of blocks.
  dense_count : int
      Number of dense layers.
  dense_nodes : int
      Number of nodes in the dense layers.
  activation : function
      Function to apply before convolution layers.
  normalization : class
      Name of class to use for constructing norm layers.
  dropout : float
      Portion of layer outputs to drop during training (between 0 and 1).
  mask_weight : float
      Amount of weight to give mask. 0 - no masking, 1 - full masking.
  
  Example
  -------
  >>> inputs = torch.rand([10, 16000])
  >>> model = EnhanceResnet()
  >>> outputs, feats = model(inputs)
  >>> outputs.shape
  torch.Size([10, 15872])
  >>> feats.shape
  torch.Size([10, 63, 257])
docstr: Takes the stft output and produces features for computation.
docstr_tok: ["Takes","the","stft","output","and","produces","features","for","computation","."]
code: def extract_feats(self, x):
      """Takes the stft output and produces features for computation."""
      return torch.log1p(spectral_magnitude(x, power=0.5))
  
file_path: speechbrain/speechbrain/lobes/models/EnhanceResnet.py
imports: import torch
  import speechbrain
  from speechbrain.processing.features import STFT, ISTFT, spectral_magnitude
label_desc: Use image node_id 3 for calling the EnhanceResnet obj's underlying member method code with example usage: obj.extract_feats(x) and returns: torch