class_docstr: Style MelGAN disciminator module.
docstr: Apply weight normalization module from all of the layers.
docstr_tok: ["Apply","weight","normalization","module","from","all","of","the","layers","."]
code: def apply_weight_norm(self):
      """Apply weight normalization module from all of the layers."""
  
      def _apply_weight_norm(m: torch.nn.Module):
          if isinstance(m, torch.nn.Conv1d) or isinstance(
              m, torch.nn.ConvTranspose1d
          ):
              torch.nn.utils.weight_norm(m)
              logging.debug(f"Weight norm is applied to {m}.")
  
      self.apply(_apply_weight_norm)
  
imports: import copy
  import logging
  import math
  from typing import Any, Dict, List, Optional
  import numpy
  import torch
  import torch.nn.functional
  from espnet2.gan_tts.melgan import MelGANDiscriminator
  from espnet2.gan_tts.melgan.pqmf import PQMF
  from espnet2.gan_tts.style_melgan.tade_res_block import TADEResBlock
label_desc: Use image node_id 4 for calling the StyleMelGANDiscriminator obj's underlying member method code with example usage: obj.apply_weight_norm() without return types