code: def choose_norm(norm_type, channel_size):
      """This function returns the chosen normalization type.
  
      Arguments
      ---------
      norm_type : str
          One of ['gLN', 'cLN', 'batchnorm'].
      channel_size : int
          Number of channels.
  
      Example
      -------
      >>> choose_norm('gLN', 10)
      GlobalLayerNorm()
      """
  
      if norm_type == "gLN":
          return GlobalLayerNorm(channel_size)
      elif norm_type == "cLN":
          return ChannelwiseLayerNorm(channel_size)
      else:
          return nn.BatchNorm1d(channel_size)
  
imports: import torch
  import torch.nn
  import speechbrain
  import torch.nn.functional
  from speechbrain.processing.signal_processing import overlap_and_add
label_desc: Use image node_id 1 for calling a global function with example usage: choose_norm(norm_type, channel_size) and returns: GlobalLayerNorm, ChannelwiseLayerNorm, nn