code: def test_x_mat_in_predict(daily_data):
      """Tests to check if prediction phase design matrix is returned."""
      train_df = daily_data["train_df"]
      test_df = daily_data["test_df"][:20]
  
      uncertainty_dict = {
          "uncertainty_method": "simple_conditional_residuals",
          "params": {
              "conditional_cols": ["dow_hr"],
              "quantiles": [0.025, 0.975],
              "quantile_estimation_method": "normal_fit",
              "sample_size_thresh": 10,
              "small_sample_size_method": "std_quantiles",
              "small_sample_size_quantile": 0.98,
          },
      }
  
      model = SimpleSilverkiteEstimator(
          fit_algorithm_dict={"fit_algorithm": "linear"},
          yearly_seasonality=False,
          quarterly_seasonality=False,
          monthly_seasonality=False,
          weekly_seasonality=False,
          daily_seasonality=False,
          holiday_lookup_countries=None,
          extra_pred_cols=["C(dow == 1)", "ct1"],
          uncertainty_dict=uncertainty_dict,
      )
  
      model.fit(
          train_df, time_col=cst.TIME_COL, value_col=cst.VALUE_COL
      )
  
      pred_df = model.predict(test_df)
      cols = [
          "ts",
          cst.QUANTILE_SUMMARY_COL,
          "err_std",
          "forecast_lower",
          "forecast_upper",
      ]
      assert_equal(model.forecast[cols], pred_df[cols])
      assert (
          model.forecast["y"].values == pred_df["forecast"].values
      ).all()
  
      forecast_x_mat = model.forecast_x_mat
      assert list(forecast_x_mat.columns) == [
          "Intercept",
          "C(dow == 1)[T.True]",
          "ct1",
      ]
      assert len(forecast_x_mat) == len(pred_df)
  
imports: import datetime
  import numpy
  import pandas
  import pytest
  from pandas.testing import assert_frame_equal
  from sklearn.linear_model import SGDRegressor
  from sklearn.metrics import mean_squared_error
  import greykite.common.constants
  from greykite.algo.forecast.silverkite.forecast_simple_silverkite import SimpleSilverkiteForecast
  from greykite.common.constants import EVENT_DF_DATE_COL
  from greykite.common.constants import EVENT_DF_LABEL_COL
  from greykite.common.data_loader import DataLoader
  from greykite.common.features.timeseries_features import convert_date_to_continuous_time
  from greykite.common.python_utils import assert_equal
  from greykite.common.testing_utils import daily_data_reg
  from greykite.common.testing_utils import generate_df_for_tests
  from greykite.common.testing_utils import generate_df_with_reg_for_tests
  from greykite.sklearn.estimator.simple_silverkite_estimator import SimpleSilverkiteEstimator
  from greykite.sklearn.estimator.testing_utils import params_component_breakdowns
label_desc: Use image node_id 16 for calling a global function with example usage: test_x_mat_in_predict(daily_data) without return types