code: def export_other_weights(ls_infer_model, state_dict, vocab_size):
      enc_norm_w = (
          state_dict["encoder.layer_norm.weight"].flatten().tolist()
      )
      enc_norm_b = (
          state_dict["encoder.layer_norm.bias"].flatten().tolist()
      )
      dec_norm_w = (
          state_dict["decoder.layer_norm.weight"].flatten().tolist()
      )
      dec_norm_b = (
          state_dict["decoder.layer_norm.bias"].flatten().tolist()
      )
      dec_shared_b = torch.zeros(vocab_size).flatten().tolist()
  
      ls_infer_model.src_embedding.norm_scale[:] = enc_norm_w
      ls_infer_model.src_embedding.norm_bias[:] = enc_norm_b
      ls_infer_model.trg_embedding.norm_scale[:] = dec_norm_w
      ls_infer_model.trg_embedding.norm_bias[:] = dec_norm_b
      ls_infer_model.trg_embedding.shared_bias[:] = dec_shared_b
  
file_path: lightseq/examples/inference/python/export/ls_transformer_export.py
imports: import argparse
  import time
  import numpy
  import torch
  from transformers import BertTokenizer
  from proto.transformer_pb2 import Transformer
  from lightseq.training import export_ls_config, export_ls_embedding, export_ls_encoder, export_ls_decoder, LSTransformer
  import lightseq.inference
label_desc: Use image node_id 2 for calling a global function with example usage: export_other_weights(ls_infer_model, state_dict, vocab_size) without return types