code: def test_len_padding_max():
      train_data = "../data/gen/train-eval.csv"
      save_path = "data/gen/decode-test/"
  
      args = GENTrainArgs(
          num_train_epochs=1,
          max_length=None,  # default
          save_path=save_path,
      )
  
      happy_gen.train(train_data, args=args)
  
      tok_data = load_from_disk(save_path)
  
      for input_ids, labels in zip(
          tok_data["train"]["input_ids"], tok_data["train"]["input_ids"]
      ):
          assert len(input_ids) == len(labels)
          assert happy_gen.tokenizer.model_max_length == len(input_ids)
  
imports: import csv
  from happytransformer import HappyWordPrediction, GENTrainArgs, WPTrainArgs
  from datasets import load_from_disk
  from tests import happy_gen, happy_wp
label_desc: Use image node_id 1 for calling a global function with example usage: test_len_padding_max() without return types