code: def result(df):
      forecaster = Forecaster()
      # Run the forecast
      result = forecaster.run_forecast_config(
          df=df,  # includes the regressor
          config=ForecastConfig(
              model_template=ModelTemplateEnum.SILVERKITE.name,
              forecast_horizon=7,
              coverage=0.8,
              metadata_param=MetadataParam(
                  time_col="ts", value_col="y", freq="D"
              ),
              evaluation_period_param=EvaluationPeriodParam(
                  cv_max_splits=1,
                  cv_horizon=7,
                  test_horizon=7,
                  cv_min_train_periods=80,
              ),
              model_components_param=ModelComponentsParam(
                  custom={
                      "fit_algorithm_dict": {"fit_algorithm": "linear"}
                  },
                  autoregression={"autoreg_dict": "auto"},
              ),
              computation_param=ComputationParam(n_jobs=-1),
          ),
      )
      return result
  
imports: import inspect
  import os
  import sys
  from collections import OrderedDict
  import pytest
  from greykite.common.python_utils import assert_equal
  from greykite.common.testing_utils import generate_df_for_tests
  from greykite.framework.templates.autogen.forecast_config import ComputationParam
  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.framework.templates.pickle_utils import dump_obj
  from greykite.framework.templates.pickle_utils import load_obj
  from greykite.framework.templates.pickle_utils import recursive_rm_dir
  from greykite.framework.utils.result_summary import summarize_grid_search_results
label_desc: Use image node_id 2 for calling a global function with example usage: result(df) and returns: result