code: def main(args):
      argsdict = vars(args)
      print(pprint.pformat(argsdict))
  
      # Save command to file
      with open(os.path.join(args.save_dir, "command.txt"), "w") as f:
          f.write(pprint.pformat(argsdict))
  
      # Load and tokenize data using the tokenizer from `args.load`. If the data is already cached, load it from there.
      # You can customize this function to load your own data for any Seq2Seq LM tasks.
      train_data = load_tokenize_data(args)
  
      if args.data_num != -1:
          train_data = train_data.select(
              [i for i in range(args.data_num)]
          )
  
      # Load model from `args.load`
      model = AutoModelForSeq2SeqLM.from_pretrained(
          args.load,
          torch_dtype=torch.float16,
          low_cpu_mem_usage=True,
          trust_remote_code=True,
      )
  
      print(
          f"  ==> Loaded model from {args.load}, model size {model.num_parameters()}"
      )
      freeze_decoder_except_xattn_codegen(model)
  
      run_training(args, model, train_data)
  
file_path: codet5/CodeT5+/instruct_tune_codet5p.py
imports: import os
  import pprint
  import argparse
  import numpy
  import copy
  import torch
  from datasets import load_dataset, load_from_disk
  from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, TrainingArguments, Trainer
label_desc: Use image node_id 5 for calling a global function with example usage: main(args) without return types