class_docstr: Multi resolution STFT loss module.
class_docstr_tok: ["Multi","resolution","STFT","loss","module","."]
docstr: Initialize Multi resolution STFT loss module.
  Args:
      fft_sizes (list): List of FFT sizes.
      hop_sizes (list): List of hop sizes.
      win_lengths (list): List of window lengths.
      window (str): Window function type.
docstr_tok: ["Initialize","Multi","resolution","STFT","loss","module",".","Args",":","fft_sizes","(","list",")",":","List","of","FFT","sizes",".","hop_sizes","(","list",")",":","List","of","hop","sizes",".","win_lengths","(","list",")",":","List","of","window","lengths",".","window","(","str",")",":","Window","function","type","."]
code: def __init__(
      self, fft_sizes, hop_sizes, win_lengths, window="hann_window"
  ):
      """Initialize Multi resolution STFT loss module.
      Args:
          fft_sizes (list): List of FFT sizes.
          hop_sizes (list): List of hop sizes.
          win_lengths (list): List of window lengths.
          window (str): Window function type.
      """
      super(MultiResolutionSTFTLoss, self).__init__()
      assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
      self.stft_losses = torch.nn.ModuleList()
      for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
          self.stft_losses += [STFTLoss(fs, ss, wl, window)]
  
imports: import torch
  import torch.nn.functional
  from nemo.core.classes import Loss, typecheck
  from nemo.core.neural_types.elements import AudioSignal, LengthsType, LossType, SpectrogramType
  from nemo.core.neural_types.neural_type import NeuralType
label_desc: Use image node_id 1 to create a new MultiResolutionSTFTLoss object from inherited base classes: Loss with example: obj = MultiResolutionSTFTLoss(fft_sizes, hop_sizes, win_lengths, window)