code: def test_fit_data_creator(self):
      auto_seq2seq = get_auto_estimator()
      auto_seq2seq.fit(
          data=train_dataloader_creator,
          epochs=1,
          batch_size=hp.choice([32, 64]),
          validation_data=valid_dataloader_creator,
          n_sampling=1,
      )
      assert auto_seq2seq.get_best_model()
      best_config = auto_seq2seq.get_best_config()
      assert 0.1 <= best_config["dropout"] <= 0.3
      assert best_config["batch_size"] in (32, 64)
      assert best_config["lstm_hidden_dim"] in (32, 64, 128)
      assert best_config["lstm_layer_num"] in (1, 2, 3, 4)
  
imports: from torch.utils.data import Dataset, DataLoader
  import torch
  import numpy
  from unittest import TestCase
  import pytest
  import tempfile
  from zoo.chronos.autots.model.auto_seq2seq import AutoSeq2Seq
  from zoo.orca.automl import hp
label_desc: Use image node_id 4 for calling the TestAutoSeq2Seq obj's underlying member method code with example usage: obj.test_fit_data_creator() without return types