docstr: Remove weight normalization module from all of the layers.
code: def remove_weight_norm(self):
      """Remove weight normalization module from all of the layers."""
  
      def _remove_weight_norm(m: torch.nn.Module):
          try:
              logging.debug(f"Weight norm is removed from {m}.")
              torch.nn.utils.remove_weight_norm(m)
          except ValueError:  # this module didn't have weight norm
              return
  
      self.apply(_remove_weight_norm)
  
imports: import logging
  import math
  from typing import Any, Dict, List, Optional
  import numpy
  import torch
  import torch.nn.functional
  from espnet2.gan_svs.visinger2.ddsp import remove_above_nyquist, scale_function, upsample
  from espnet2.gan_tts.hifigan import HiFiGANMultiPeriodDiscriminator, HiFiGANMultiScaleDiscriminator, HiFiGANMultiScaleMultiPeriodDiscriminator, HiFiGANPeriodDiscriminator, HiFiGANScaleDiscriminator
  from espnet2.gan_tts.hifigan.residual_block import ResidualBlock
label_desc: Use image node_id 4 for calling the VISinger2VocoderGenerator obj's underlying member method code with example usage: obj.remove_weight_norm() without return types