code: def main():
      happy_tt = HappyTextToText("T5", "t5-base")
  
      train_csv_path = "train.csv"
      eval_csv_path = "eval.csv"
  
      # There's no training split. Just eval and test. So, we'll use eval for train and test for eval.
      # 755 cases, but each case has 4 corrections so there are really 3020
      train_dataset = load_dataset("jfleg", split="validation[:]")
  
      # 748 cases, but again, each case has 4 correction so there are really
      eval_dataset = load_dataset("jfleg", split="test[:]")
  
      generate_csv("train.csv", train_dataset)
      generate_csv("eval.csv", eval_dataset)
  
      train_args = TTTrainArgs(
          num_train_epochs=1,
          learning_rate=1e-5,
          # fp16=True,
          # report_to = ('wandb'),
          # project_name = "happy-transformer-examples",
          # run_name = "grammar-correction",
          # deepspeed="ZERO-2"
      )
  
      happy_tt.train(
          train_csv_path, args=train_args, eval_filepath=eval_csv_path
      )
  
      happy_tt.save("finetuned-model/")
  
imports: import csv
  from happytransformer.happy_text_to_text import HappyTextToText, TTTrainArgs
  from datasets import load_dataset
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types