content: from neuralforecast.auto import AutoRNN
  from neuralforecast.common._base_auto import MockTrial
  from neuralforecast.common._model_checks import check_model
  from neuralforecast.models import RNN
  
  from .test_helpers import check_args
  
  
  def test_rnn_model(suppress_warnings):
      """Test RNN model with the shared dataset fixture."""
      check_model(RNN, ["airpassengers"])
  
  def test_autornn_model(setup_dataset):
      dataset = setup_dataset
      # Unit test to test that Auto* model contains all required arguments from BaseAuto
      check_args(AutoRNN, exclude_args=['cls_model'])
  
      # Unit test for situation: Optuna with updated default config
      my_config = AutoRNN.get_default_config(h=12, backend='optuna')
      def my_config_new(trial):
          config = {**my_config(trial)}
          config.update({'max_steps': 1, 'val_check_steps': 1, 'input_size': -1, 'encoder_hidden_size': 8})
          return config
  
      model = AutoRNN(h=12, config=my_config_new, backend='optuna', num_samples=1, cpus=1)
      model.fit(dataset=dataset)
  
      assert model.config(MockTrial())['h'] == 12
      # Unit test for situation: Ray with updated default config
      my_config = AutoRNN.get_default_config(h=12, backend='ray')
      my_config['max_steps'] = 1
      my_config['val_check_steps'] = 1
      my_config['input_size'] = -1
      my_config['encoder_hidden_size'] = 8
      model = AutoRNN(h=12, config=my_config, backend='ray', num_samples=1, cpus=1)
      model.fit(dataset=dataset)