code: def chose_norm(norm_type, channel_size):
      """The input of normalization will be (M, C, K), where M is batch size.
  
      C is channel size and K is sequence length.
      """
      if norm_type == "gLN":
          return GlobalLayerNorm(channel_size)
      elif norm_type == "cLN":
          return ChannelwiseLayerNorm(channel_size)
      elif norm_type == "BN":
          # Given input (M, C, K), nn.BatchNorm1d(C) will accumulate statics
          # along M and K, so this BN usage is right.
          return nn.BatchNorm1d(channel_size)
      else:
          raise ValueError("Unsupported normalization type")
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling a global function with example usage: chose_norm(norm_type, channel_size) and returns: GlobalLayerNorm, ChannelwiseLayerNorm, nn