def test_tcn_forecaster_save_load(self):
    (train_data, val_data, test_data) = create_data()
    forecaster = LSTMForecaster(past_seq_len=24, input_feature_num=2, output_feature_num=2, loss='mae', lr=0.01)
    train_mse = forecaster.fit(train_data, epochs=2)
    with tempfile.TemporaryDirectory() as tmp_dir_name:
        ckpt_name = os.path.join(tmp_dir_name, 'ckpt')
        test_pred_save = forecaster.predict(test_data[0])
        forecaster.save(ckpt_name)
        forecaster.load(ckpt_name)
        test_pred_load = forecaster.predict(test_data[0])
    np.testing.assert_almost_equal(test_pred_save, test_pred_load)
def test_tcn_forecaster_save_load(self):
    (train_data, val_data, test_data) = create_data()
    forecaster = LSTMForecaster(past_seq_len=24, input_feature_num=2, output_feature_num=2, loss='mae', lr=0.01)
    train_mse = forecaster.fit(train_data, epochs=2)
    with tempfile.TemporaryDirectory() as tmp_dir_name:
        ckpt_name = os.path.join(tmp_dir_name, 'ckpt')
        test_pred_save = forecaster.predict(test_data[0])
        forecaster.save(ckpt_name)
        forecaster.load(ckpt_name)
        test_pred_load = forecaster.predict(test_data[0])
    np.testing.assert_almost_equal(test_pred_save, test_pred_load)