class_docstr: Exhaustive search over specified parameter values for an estimator.
  
  TODO: Add detailed docstring similar to the one available here:
  https://github.com/scikit-learn/scikit-learn/blob/c6512929fbee7232949c0f18cfb28cf3b5959df9/sklearn/model_selection/_search.py#L972
class_docstr_tok: ["Exhaustive","search","over","specified","parameter","values","for","an","estimator",".","TODO",":","Add","detailed","docstring","similar","to","the","one","available","here",":","https",":","\/\/github.com\/scikit-learn\/scikit-learn\/blob\/c6512929fbee7232949c0f18cfb28cf3b5959df9\/sklearn\/model_selection\/_search.py","#","L972"]
code: def __init__(
      self,
      forecaster,
      cv,
      alpha: Optional[float],
      coverage: Union[float, List[float]],
      param_grid,
      scoring=None,
      n_jobs=None,
      refit=True,
      refit_metric: str = "smape",
      verbose=0,
      pre_dispatch="2*n_jobs",
      error_score=np.nan,
      return_train_score=False,
  ):
      super(ForecastingGridSearchCV, self).__init__(
          pipeline=forecaster,
          cv=cv,
          alpha=alpha,
          coverage=coverage,
          n_jobs=n_jobs,
          pre_dispatch=pre_dispatch,
          refit=refit,
          refit_metric=refit_metric,
          scoring=scoring,
          verbose=verbose,
          error_score=error_score,
          return_train_score=return_train_score,
      )
      self.param_grid = param_grid
  
imports: import time
  import warnings
  from collections import defaultdict
  from functools import partial
  from typing import Any, Dict, Generator, List, Optional, Tuple, Union
  import numpy
  import pandas
  from joblib import Parallel, delayed
  from scipy.stats import rankdata
  from sklearn.base import clone
  from sklearn.metrics._scorer import _PredictScorer
  from sklearn.model_selection import ParameterGrid, ParameterSampler, check_cv
  from sklearn.model_selection._validation import _aggregate_score_dicts
  from sktime.forecasting.compose import ForecastingPipeline
  from sktime.forecasting.model_selection import ExpandingWindowSplitter, SlidingWindowSplitter
  from sktime.utils.validation.forecasting import check_y_X
  from pycaret.internal.logging import get_logger
  from pycaret.utils.generic import _get_metrics_dict, get_function_params
  from pycaret.utils.time_series.forecasting import get_predictions_with_intervals, update_additional_scorer_kwargs
  from pycaret.utils.time_series.forecasting.pipeline import _get_imputed_data
label_desc: Use image node_id 1 to create a new ForecastingGridSearchCV object from inherited base classes: BaseGridSearch with example: obj = ForecastingGridSearchCV(forecaster, cv, alpha, coverage, param_grid, scoring, n_jobs, refit, refit_metric, verbose, pre_dispatch, error_score, return_train_score)