class_docstr: WaveNet with global conditioning.
class_docstr_tok: ["WaveNet","with","global","conditioning","."]
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.Conv2d
          ):
              torch.nn.utils.weight_norm(m)
              logging.debug(f"Weight norm is applied to {m}.")
  
      self.apply(_apply_weight_norm)
  
file_path: espnet/espnet2/gan_tts/wavenet/wavenet.py
imports: import logging
  import math
  from typing import Optional
  import torch
  from espnet2.gan_tts.wavenet.residual_block import Conv1d1x1, ResidualBlock
label_desc: Use image node_id 4 for calling the WaveNet obj's underlying member method code with example usage: obj.apply_weight_norm() without return types