class_docstr: PWGAN discriminator as in https://arxiv.org/abs/1910.11480.
  It classifies each audio window real/fake and returns a sequence
  of predictions.
      It is a stack of convolutional blocks with dilation.
docstr:     x : (B, 1, T).
  Returns:
      Tensor: (B, 1, T)
docstr_tok: ["x",":","(","B",",","1",",","T",")",".","Returns",":","Tensor",":","(","B",",","1",",","T",")"]
code: def forward(self, x):
      """
          x : (B, 1, T).
      Returns:
          Tensor: (B, 1, T)
      """
      for f in self.conv_layers:
          x = f(x)
      return x
  
file_path: TTS/TTS/vocoder/models/parallel_wavegan_discriminator.py
imports: import math
  import torch
  from torch import nn
  from TTS.vocoder.layers.parallel_wavegan import 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