code: def test_validate(df, valid_configs, custom_tscv):
      bm = BenchmarkForecastConfig(
          df=df, configs=valid_configs, tscv=custom_tscv
      )
      bm.validate()
  
      for config_name in bm.result.keys():
          assert bm.result[config_name]["pipeline_params"] is not None
  
      def copy(obj):
          return pickle.loads(pickle.dumps(obj))
  
      # Error due to incompatible model components in config
      with pytest.raises(
          ValueError, match=rf"Unexpected key\(s\) found:"
      ):
          configs = copy(valid_configs)
          configs["valid_prophet"].model_components_param.regressors = {
              "regressor_cols": [
                  "regressor1",
                  "regressor2",
                  "regressor_categ",
              ]
          }
          bm = BenchmarkForecastConfig(
              df=df, configs=configs, tscv=custom_tscv
          )
          bm.validate()
  
      # Error due to different forecast horizons in configs
      with pytest.raises(
          ValueError,
          match=r"valid_silverkite's 'forecast_horizon' \(7\) does not "
          r"match that of 'tscv' \(14\)",
      ):
          configs = copy(valid_configs)
          configs["valid_silverkite"].forecast_horizon = 7
          bm = BenchmarkForecastConfig(
              df=df, configs=configs, tscv=custom_tscv
          )
          bm.validate()
  
      # Warning due to different periods_between_train_test
      with pytest.raises(
          ValueError,
          match=rf"valid_prophet's 'periods_between_train_test' \(7\) does not match "
          rf"that of 'tscv' \({custom_tscv.periods_between_train_test}\).",
      ):
          configs = copy(valid_configs)
          configs[
              "valid_prophet"
          ].evaluation_period_param.periods_between_train_test = 7
          bm = BenchmarkForecastConfig(
              df=df, configs=configs, tscv=custom_tscv
          )
          bm.validate()
  
      # Error due to different coverage
      with pytest.raises(
          ValueError,
          match="All forecast configs must have same coverage.",
      ):
          configs = copy(valid_configs)
          configs["valid_prophet"].coverage = 0.1
          bm = BenchmarkForecastConfig(
              df=df, configs=configs, tscv=custom_tscv
          )
          bm.validate()
  
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 7 for calling a global function with example usage: test_validate(df, valid_configs, custom_tscv) and returns: pickle