class_docstr: UHiFiGAN generator module.
class_docstr_tok: ["UHiFiGAN","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):
          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)
  
file_path: espnet/espnet2/gan_svs/uhifigan/uhifigan.py
imports: import logging
  from typing import List, Optional
  import numpy
  import torch
  import torch.nn.functional
label_desc: Use image node_id 5 for calling the UHiFiGANGenerator obj's underlying member method code with example usage: obj.apply_weight_norm() without return types