code: def test_fit_np(self):
      # torch
      auto_lstm = get_auto_estimator(backend="torch")
      auto_lstm.fit(
          data=get_x_y(size=1000),
          epochs=1,
          batch_size=hp.choice([32, 64]),
          validation_data=get_x_y(size=400),
          n_sampling=1,
      )
      assert auto_lstm.get_best_model()
      best_config = auto_lstm.get_best_config()
      assert 0.1 <= best_config["dropout"] <= 0.2
      assert best_config["batch_size"] in (32, 64)
      assert 1 <= best_config["layer_num"] < 3
  
imports: from bigdl.chronos.utils import LazyImport
  import numpy
  from unittest import TestCase
  import pytest
  import tempfile
  from ...None import op_torch, op_tf2, op_distributed, op_inference
label_desc: Use image node_id 3 for calling the TestAutoLSTM obj's underlying member method code with example usage: obj.test_fit_np() without return types