code: def main():
      args = get_args()
      set_seed(args.seed)
  
      dataset = load_dataset("codeparrot/codecomplex", split="train")
      train_test = dataset.train_test_split(test_size=0.2)
      test_validation = train_test["test"].train_test_split(
          test_size=0.5
      )
      train_test_validation = DatasetDict(
          {
              "train": train_test["train"],
              "test": test_validation["train"],
              "valid": test_validation["test"],
          }
      )
  
      print("Loading tokenizer and model")
      tokenizer = AutoTokenizer.from_pretrained(args.model_ckpt)
      tokenizer.pad_token = tokenizer.eos_token
      model = AutoModelForSequenceClassification.from_pretrained(
          args.model_ckpt, num_labels=7
      )
      model.config.pad_token_id = model.config.eos_token_id
  
      if args.freeze:
          for param in model.roberta.parameters():
              param.requires_grad = False
  
      labels = ClassLabel(
          num_classes=7,
          names=list(set(train_test_validation["train"]["complexity"])),
      )
  
      def tokenize(example):
          inputs = tokenizer(
              example["src"], truncation=True, max_length=1024
          )
          label = labels.str2int(example["complexity"])
          return {
              "input_ids": inputs["input_ids"],
              "attention_mask": inputs["attention_mask"],
              "label": label,
          }
  
      tokenized_datasets = train_test_validation.map(
          tokenize,
          batched=True,
          remove_columns=train_test_validation["train"].column_names,
      )
      data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
  
      training_args = TrainingArguments(
          output_dir=args.output_dir,
          learning_rate=args.learning_rate,
          lr_scheduler_type=args.lr_scheduler_type,
          evaluation_strategy="epoch",
          save_strategy="epoch",
          logging_strategy="epoch",
          per_device_train_batch_size=args.batch_size,
          per_device_eval_batch_size=args.batch_size,
          num_train_epochs=args.num_epochs,
          gradient_accumulation_steps=args.gradient_accumulation_steps,
          weight_decay=0.01,
          metric_for_best_model="accuracy",
          run_name="complexity-java",
          report_to="wandb",
      )
  
      trainer = Trainer(
          model=model,
          args=training_args,
          train_dataset=tokenized_datasets["train"],
          eval_dataset=tokenized_datasets["valid"],
          tokenizer=tokenizer,
          data_collator=data_collator,
          compute_metrics=compute_metrics,
      )
  
      print("Training...")
      trainer.add_callback(CustomCallback(trainer))
      trainer.train()
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/codeparrot/examples/train_complexity_predictor.py
imports: import argparse
  from copy import deepcopy
  import numpy
  from datasets import ClassLabel, DatasetDict, load_dataset
  from evaluate import load
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, Trainer, TrainerCallback, TrainingArguments, set_seed
label_desc: Use image node_id 3 for calling a global function with example usage: main() and returns: dict