code: def create_config(vocab_size):
      transformer_config = LSTransformer.get_config(
          model="transformer-base",
          max_batch_tokens=2048,
          max_seq_len=512,
          vocab_size=vocab_size,
          padding_idx=0,
          num_encoder_layer=6,
          num_decoder_layer=6,
          fp16=True,
          local_rank=0,
      )
      return transformer_config
  
file_path: lightseq/examples/inference/python/export/ls_transformer_ptq_export.py
imports: import argparse
  import time
  import numpy
  import torch
  from transformers import BertTokenizer
  from proto.quant_transformer_pb2 import QuantTransformer
  from lightseq.training import export_ls_config, export_ls_embedding_ptq, export_ls_encoder_ptq, export_ls_decoder_ptq, LSTransformer
  import lightseq.inference
label_desc: Use image node_id 5 for calling a global function with example usage: create_config(vocab_size) and returns: transformer_config