class_docstr: Avocodo generator module.
class_docstr_tok: ["Avocodo","generator","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 logging
  from typing import Any, Dict, List, Optional
  import torch
  import torch.nn.functional
  from torch.nn import Conv1d
  from torch.nn.utils import spectral_norm, weight_norm
  from espnet2.gan_svs.visinger2.visinger2_vocoder import MultiFrequencyDiscriminator
  from espnet2.gan_tts.hifigan.residual_block import ResidualBlock
  from espnet2.gan_tts.melgan.pqmf import PQMF
label_desc: Use image node_id 5 for calling the AvocodoGenerator obj's underlying member method code with example usage: obj.apply_weight_norm() without return types