code: def transform(self, x):
      x_stft = torch.stft(
          x,
          self.fft_size,
          self.hop_size,
          self.win_size,
          self.window.type_as(x),
          normalized=self.normalized,
          return_complex=True,
      )
  
      real = torch.real(x_stft)
      imag = torch.imag(x_stft)
      mag = torch.clamp(real**2 + imag**2, min=1e-7)
      mag = torch.sqrt(mag)
      phase = torch.angle(x_stft)
  
      if self.mel_scale is not None:
          mag = self.mel_scale(mag)
  
      if self.domain == "log":
          mag = 20 * torch.log10(mag) - self.ref_level_db
          mag = torch.clamp(
              (mag - self.min_level_db) / -self.min_level_db, 0, 1
          )
          return mag, phase
      elif self.domain == "linear":
          return mag, phase
      elif self.domain == "double":
          log_mag = 20 * torch.log10(mag) - self.ref_level_db
          log_mag = torch.clamp(
              (log_mag - self.min_level_db) / -self.min_level_db, 0, 1
          )
          return torch.cat((mag, log_mag), dim=1), phase
  
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 2 for calling the TorchSTFT obj's underlying member method code with example usage: obj.transform(x) and returns: mag,  phase, mag,  phase, torch,  phase