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.
code: def apply_weight_norm(self):
      def _apply_weight_norm(m):
          if isinstance(m, (torch.nn.Conv1d, torch.nn.Conv2d)):
              torch.nn.utils.weight_norm(m)
  
      self.apply(_apply_weight_norm)
  
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 3 for calling the ParallelWaveganDiscriminator obj's underlying member method code with example usage: obj.apply_weight_norm() without return types