code: def _wrap_model(
      model, examples, time_feature, time_series_id_features
  ):
      """Choose the appropriate wrapper for the model.
  
      If the model has a forecast_quantiles method
      (or in the sktime case: predict_quantiles) then choose the wrapped
      quantile forecasting model, otherwise the basic wrapper without
      quantile forecasting capabilities.
      """
      model_package = _get_model_package(model)
      if (
          model_package == _SKTIME
          and hasattr(model, "predict_quantiles")
      ) or hasattr(model, Forecasting.FORECAST_QUANTILES):
          return _WrappedQuantileForecastingModel(
              model, examples, time_feature, time_series_id_features
          )
      else:
          return _WrappedForecastingModel(
              model, examples, time_feature, time_series_id_features
          )
  
file_path: responsible-ai-toolbox/responsibleai/responsibleai/_internal/_forecasting_wrappers.py
imports: import inspect
  import sys
  import pandas
  from ml_wrappers.model.base_wrapped_model import BaseWrappedModel
  from raiutils.models import Forecasting, ModelTask
label_desc: Use image node_id 2 for calling a global function with example usage: _wrap_model(model, examples, time_feature, time_series_id_features) and returns: _WrappedQuantileForecastingModel, _WrappedForecastingModel