code: def valid_configs():
      metadata = MetadataParam(
          time_col=TIME_COL, value_col=VALUE_COL, freq="D"
      )
      computation = ComputationParam(
          hyperparameter_budget=10, n_jobs=None, verbose=1
      )
      forecast_horizon = 2 * 7
      coverage = 0.90
      evaluation_metric = EvaluationMetricParam(
          cv_selection_metric=EvaluationMetricEnum.MeanAbsoluteError.name,
          cv_report_metrics=None,
          agg_periods=7,
          agg_func=np.mean,
          null_model_params=None,
      )
      evaluation_period = EvaluationPeriodParam(
          test_horizon=2 * 7,
          periods_between_train_test=2 * 7,
          cv_horizon=1 * 7,
          cv_min_train_periods=8 * 7,
          cv_expanding_window=True,
          cv_periods_between_splits=7,
          cv_periods_between_train_test=3 * 7,
          cv_max_splits=2,
      )
  
      silverkite_components = ModelComponentsParam(
          seasonality={
              "yearly_seasonality": False,
              "weekly_seasonality": True,
          },
          growth={"growth_term": "quadratic"},
          events={
              "holidays_to_model_separately": SilverkiteHoliday.ALL_HOLIDAYS_IN_COUNTRIES,
              "holiday_lookup_countries": ["UnitedStates"],
              "holiday_pre_num_days": 3,
          },
          changepoints={
              "changepoints_dict": {
                  "method": "uniform",
                  "n_changepoints": 20,
              }
          },
          regressors={
              "regressor_cols": [
                  "regressor1",
                  "regressor2",
                  "regressor3",
              ]
          },
          uncertainty={
              "uncertainty_dict": "auto",
          },
          hyperparameter_override={
              "input__response__null__max_frac": 0.1
          },
          custom={
              "fit_algorithm_dict": {
                  "fit_algorithm": "ridge",
                  "fit_algorithm_params": {"normalize": True},
              },
              "feature_sets_enabled": False,
          },
      )
  
      prophet_components = ModelComponentsParam(
          seasonality={
              "seasonality_mode": ["additive"],
              "yearly_seasonality": ["auto"],
              "weekly_seasonality": [True],
              "daily_seasonality": ["auto"],
          },
          growth={"growth_term": ["linear"]},
          events={
              "holiday_pre_num_days": [1],
              "holiday_post_num_days": [1],
              "holidays_prior_scale": [1.0],
          },
          changepoints={
              "changepoint_prior_scale": [0.05],
              "n_changepoints": [1],
              "changepoint_range": [0.5],
          },
          regressors={
              "add_regressor_dict": [
                  {
                      "regressor1": {
                          "prior_scale": 10,
                          "standardize": True,
                          "mode": "additive",
                      },
                      "regressor2": {
                          "prior_scale": 15,
                          "standardize": False,
                          "mode": "additive",
                      },
                      "regressor3": {},
                  }
              ]
          },
          uncertainty={"uncertainty_samples": [10]},
      )
  
      valid_prophet = ForecastConfig(
          model_template=ModelTemplateEnum.PROPHET.name,
          metadata_param=metadata,
          computation_param=computation,
          coverage=coverage,
          evaluation_metric_param=evaluation_metric,
          evaluation_period_param=evaluation_period,
          forecast_horizon=forecast_horizon,
          model_components_param=prophet_components,
      )
  
      valid_silverkite = ForecastConfig(
          model_template=ModelTemplateEnum.SILVERKITE.name,
          metadata_param=metadata,
          computation_param=computation,
          coverage=coverage,
          evaluation_metric_param=evaluation_metric,
          evaluation_period_param=evaluation_period,
          forecast_horizon=forecast_horizon,
          model_components_param=silverkite_components,
      )
  
      configs = {
          "valid_prophet": valid_prophet,
          "valid_silverkite": valid_silverkite,
      }
      return configs
  
imports: import pickle
  import sys
  import numpy
  import pandas
  import pytest
  from plotly.colors import DEFAULT_PLOTLY_COLORS
  from greykite.algo.forecast.silverkite.constants.silverkite_holiday import SilverkiteHoliday
  from greykite.common.constants import ACTUAL_COL
  from greykite.common.constants import PREDICTED_COL
  from greykite.common.constants import PREDICTED_LOWER_COL
  from greykite.common.constants import PREDICTED_UPPER_COL
  from greykite.common.constants import TIME_COL
  from greykite.common.constants import VALUE_COL
  from greykite.common.evaluation import EvaluationMetricEnum
  from greykite.common.evaluation import root_mean_squared_error
  from greykite.common.python_utils import assert_equal
  from greykite.common.testing_utils import generate_df_with_reg_for_tests
  from greykite.framework.benchmark.benchmark_class import BenchmarkForecastConfig
  from greykite.framework.constants import FORECAST_STEP_COL
  from greykite.framework.templates.autogen.forecast_config import ComputationParam
  from greykite.framework.templates.autogen.forecast_config import EvaluationMetricParam
  from greykite.framework.templates.autogen.forecast_config import EvaluationPeriodParam
  from greykite.framework.templates.autogen.forecast_config import ForecastConfig
  from greykite.framework.templates.autogen.forecast_config import MetadataParam
  from greykite.framework.templates.autogen.forecast_config import ModelComponentsParam
  from greykite.framework.templates.forecaster import Forecaster
  from greykite.framework.templates.model_templates import ModelTemplateEnum
  from greykite.sklearn.cross_validation import RollingTimeSeriesSplit
label_desc: Use image node_id 2 for calling a global function with example usage: valid_configs() and returns: configs