code: def remove_weight_norm_from_model(model):
      from functools import reduce
  
      layers_with_wn = []
      for param_name, _ in model.named_parameters():
          if param_name.endswith("_g"):
              # retrieve the module with this param_name
              module_names = param_name.split(".")[
                  :-1
              ]  # exclude the actual parameter name
              wn_module = reduce(getattr, module_names, model)
              layers_with_wn.append(wn_module)
      for wn_module in layers_with_wn:
          torch.nn.utils.remove_weight_norm(wn_module)
          logger.warning(
              f"Weight norm removed from module with {wn_module}\n"
          )
  
imports: import copy
  import logging
  from typing import Dict, List, Optional, Tuple
  import numpy
  import torch
  import torch.nn
  from torch import Tensor
  from fairseq import checkpoint_utils, utils
  from fairseq.data.data_utils import lengths_to_padding_mask
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqEncoderModel, FairseqLanguageModel, register_model, register_model_architecture
  from fairseq.models.speech_to_speech.modules.ctc_decoder import CTCDecoder
  from fairseq.models.speech_to_text.hub_interface import S2THubInterface
  from fairseq.models.transformer import Embedding, TransformerDecoder, TransformerModelBase
  from fairseq.models.wav2vec import Wav2VecEncoder
  from fairseq.modules.layer_norm import LayerNorm
label_desc: Use image node_id 5 for calling a global function with example usage: remove_weight_norm_from_model(model) without return types