class_docstr: Base adapter class for StatsForecast models.
class_docstr_tok: ["Base","adapter","class","for","StatsForecast","models","."]
docstr: Check if prediction intervals will work with forecaster.
  
  Check if `level` argument is available in `predict_in_sample` and
  `predict` methods from the `statsforecast` forecaster.
  A tuple of booleans (`support_pred_int_in_sample`, `support_pred_int`)
  is returned where the user is informed which of the two, if any,
  support interval predictions.
  
   Furthermore, will throw a warning to let the user know that he should consider
   upgrading statsforecast version as both or one of the two methods might
   not be able to produce confidence intervals.
  
  Returns
  -------
  Dict of bool
      A dict containing two boolean values:
      - `support_pred_int_in_sample`: True if prediction intervals are supported
        in `predict_in_sample`, False otherwise.
      - `support_pred_int`: True if prediction intervals are supported
        in `predict`, False otherwise.
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code: def _check_supports_pred_int(self) -> Dict[str, bool]:
      """
      Check if prediction intervals will work with forecaster.
  
      Check if `level` argument is available in `predict_in_sample` and
      `predict` methods from the `statsforecast` forecaster.
      A tuple of booleans (`support_pred_int_in_sample`, `support_pred_int`)
      is returned where the user is informed which of the two, if any,
      support interval predictions.
  
       Furthermore, will throw a warning to let the user know that he should consider
       upgrading statsforecast version as both or one of the two methods might
       not be able to produce confidence intervals.
  
      Returns
      -------
      Dict of bool
          A dict containing two boolean values:
          - `support_pred_int_in_sample`: True if prediction intervals are supported
            in `predict_in_sample`, False otherwise.
          - `support_pred_int`: True if prediction intervals are supported
            in `predict`, False otherwise.
      """
      statsforecast_class = self._get_statsforecast_class()
      if (
          "level"
          not in signature(
              statsforecast_class.predict_in_sample
          ).parameters.keys()
      ):
          support_pred_int_in_sample = False
          import statsforecast
  
          warn(
              f" {statsforecast_class.__name__} from "
              f"statsforecast v{statsforecast.__version__} "
              f"does not support prediction of intervals in `predict_in_sample`. "
              f"Consider upgrading to a newer version."
          )
      else:
          support_pred_int_in_sample = True
  
      if (
          "level"
          not in signature(
              statsforecast_class.predict
          ).parameters.keys()
      ):
          support_pred_int = False
          import statsforecast
  
          warn(
              f" {statsforecast_class.__name__} from "
              f"statsforecast v{statsforecast.__version__} "
              f"does not support prediction of intervals in `predict`. "
              f"Consider upgrading to a newer version."
          )
      else:
          support_pred_int = True
  
      return {
          "int_in_sample": support_pred_int_in_sample,
          "int": support_pred_int,
      }
  
imports: from inspect import signature
  from typing import Dict
  from warnings import warn
  import pandas
  from sktime.forecasting.base import BaseForecaster
label_desc: Use image node_id 13 for calling the _GeneralisedStatsForecastAdapter obj's underlying member method code with example usage: obj._check_supports_pred_int() and returns: dict