docstr: Args:
      x (Tensor): Input tensor (B, hidden_channels, T).
      mask (Tensor): Mask tensor (B, 1, T).
  Returns:
      Tensor: Output tensor (B, 1, T * hop_size).
code: def forward(self, x, mask):
      """
      Args:
          x (Tensor): Input tensor (B, hidden_channels, T).
          mask (Tensor): Mask tensor (B, 1, T).
      Returns:
          Tensor: Output tensor (B, 1, T * hop_size).
      """
      istft_x = x
      istft_x = self.istft_pre(istft_x)
  
      istft_x = self.net(istft_x) * mask
  
      amp = self.istft_amplitude(istft_x).unsqueeze(-1)
      phase = (torch.rand(amp.shape) * 2 * 3.14 - 3.14).to(amp)
  
      real = amp * torch.cos(phase)
      imag = amp * torch.sin(phase)
      # spec = torch.cat([real, imag], 3)
      spec = torch.stack([real, imag], dim=-1)  # Change to stack
      spec_complex = torch.view_as_complex(
          spec.squeeze(-2)
      )  # Convert to complex tensor
      istft_x = torch.istft(
          spec_complex,
          self.fft_size,
          self.hop_size,
          self.win_size,
          self.window.to(amp),
          True,
          length=x.shape[2] * self.hop_size,
          return_complex=False,
      )
  
      return istft_x.unsqueeze(1)
  
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 Generator_Noise obj's underlying member method code with example usage: obj.forward(x, mask) and returns: istft_x