code: def test_non_is_residual_based_x_mat(forecast_result):
      """Tests predicting non-residual based interval with ``x_mat``."""
      model = QuantileRegressionUncertaintyModel(
          uncertainty_dict={
              "uncertainty_method": "quantile_regression",
              "params": {"value_col": "y", "is_residual_based": False},
          },
          coverage=0.9,
      )
      model.fit(
          train_df=forecast_result["fit_df"],
          x_mat=forecast_result["x_mat"],
      )
      pred = model.predict(
          fut_df=forecast_result["fit_df"],
          x_mat=forecast_result["x_mat"],
      )
      assert cst.PREDICTED_LOWER_COL in pred
      assert cst.PREDICTED_UPPER_COL in pred
      pred = model.predict(
          fut_df=forecast_result["predict_df"],
          x_mat=forecast_result["fut_x_mat"],
      )
      assert cst.PREDICTED_LOWER_COL in pred
      assert cst.PREDICTED_UPPER_COL in pred
  
file_path: greykite/greykite/tests/sklearn/uncertainty/test_quantile_regression_uncertainty_model.py
imports: import pandas
  import pytest
  from greykite.common import constants
  from greykite.common.data_loader import DataLoader
  from greykite.sklearn.estimator.simple_silverkite_estimator import SimpleSilverkiteEstimator
  from greykite.sklearn.uncertainty.exceptions import UncertaintyError
  from greykite.sklearn.uncertainty.quantile_regression_uncertainty_model import QuantileRegressionUncertaintyModel
label_desc: Use image node_id 6 for calling a global function with example usage: test_non_is_residual_based_x_mat(forecast_result) without return types