class_docstr: Parallel WaveGAN Discriminator module.
docstr: Calculate forward propagation.
  
  Args:
      x (Tensor): Input noise signal (B, 1, T).
  
  Returns:
      Tensor: Output tensor (B, 1, T).
docstr_tok: ["Calculate","forward","propagation",".","Args",":","x","(","Tensor",")",":","Input","noise","signal","(","B",",","1",",","T",")",".","Returns",":","Tensor",":","Output","tensor","(","B",",","1",",","T",")","."]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """Calculate forward propagation.
  
      Args:
          x (Tensor): Input noise signal (B, 1, T).
  
      Returns:
          Tensor: Output tensor (B, 1, T).
  
      """
      for f in self.conv_layers:
          x = f(x)
      return x
  
imports: import logging
  import math
  from typing import Any, Dict, Optional
  import numpy
  import torch
  from espnet2.gan_tts.parallel_wavegan import upsample
  from espnet2.gan_tts.wavenet.residual_block import Conv1d, Conv1d1x1, ResidualBlock
label_desc: Use image node_id 2 for calling the ParallelWaveGANDiscriminator obj's underlying member method code with example usage: obj.forward(x) and returns: x