code: def __init__(
      self,
      sample_rate,
      fft_size,
      hop_size,
      win_size,
      normalized=False,
      domain="linear",
      mel_scale=False,
      ref_level_db=20,
      min_level_db=-100,
  ):
      super().__init__()
      self.fft_size = fft_size
      self.hop_size = hop_size
      self.win_size = win_size
      self.ref_level_db = ref_level_db
      self.min_level_db = min_level_db
      self.window = torch.hann_window(win_size)
      self.normalized = normalized
      self.domain = domain
      self.mel_scale = (
          MelScale(
              sample_rate=sample_rate,
              n_mels=(fft_size // 2 + 1),
              n_stft=(fft_size // 2 + 1),
          )
          if mel_scale
          else None
      )
  
imports: import logging
  import math
  from typing import Any, Dict, List, Optional
  import numpy
  import torch
  import torch.nn.functional
  from espnet2.gan_svs.visinger2.ddsp import remove_above_nyquist, scale_function, upsample
  from espnet2.gan_tts.hifigan import HiFiGANMultiPeriodDiscriminator, HiFiGANMultiScaleDiscriminator, HiFiGANMultiScaleMultiPeriodDiscriminator, HiFiGANPeriodDiscriminator, HiFiGANScaleDiscriminator
  from espnet2.gan_tts.hifigan.residual_block import ResidualBlock
label_desc: Use image node_id 1 to create a new TorchSTFT object from inherited base classes: torch.nn with example: obj = TorchSTFT(sample_rate, fft_size, hop_size, win_size, normalized, domain, mel_scale, ref_level_db, min_level_db)