code: def remove_weightnorm(self):
      if self.removed_weightnorm:
          return
      for wavenet in self.wavenet:
          wavenet.start = torch.nn.utils.remove_weight_norm(
              wavenet.start
          )
          wavenet.in_layers = remove(wavenet.in_layers)
          wavenet.cond_layer = torch.nn.utils.remove_weight_norm(
              wavenet.cond_layer
          )
          wavenet.res_skip_layers = remove(wavenet.res_skip_layers)
      self.removed_weightnorm = True
  
file_path: NeMo/nemo/collections/tts/modules/waveglow.py
imports: from typing import Tuple
  import torch
  from nemo.collections.tts.modules.submodules import Invertible1x1Conv, WaveNet
  from nemo.collections.tts.parts.utils.helpers import OperationMode, remove, split_view
  from nemo.core.classes import Exportable, NeuralModule, typecheck
  from nemo.core.neural_types.elements import AudioSignal, IntType, MelSpectrogramType, NormalDistributionSamplesType, VoidType
  from nemo.core.neural_types.neural_type import NeuralType
label_desc: Use image node_id 9 for calling the WaveGlowModule obj's underlying member method code with example usage: obj.remove_weightnorm() without return types