class_docstr: Wrapper for quantile forecasting models.
  
  This provides a unified API for the forecast_quantile method
  in addition to the forecast method provided by the base class.
  Additionally, this wrapper knows how to work with models from
  certain packages such as AzureML and sktime.
  Other models are expected to have a forecast method that takes
  a DataFrame with the time series ID features and time feature.
class_docstr_tok: ["Wrapper","for","quantile","forecasting","models",".","This","provides","a","unified","API","for","the","forecast_quantile","method","in","addition","to","the","forecast","method","provided","by","the","base","class",".","Additionally",",","this","wrapper","knows","how","to","work","with","models","from","certain","packages","such","as","AzureML","and","sktime",".","Other","models","are","expected","to","have","a","forecast","method","that","takes","a","DataFrame","with","the","time","series","ID","features","and","time","feature","."]
code: def __init__(
      self, model, examples, time_feature, time_series_id_features
  ):
      super(_WrappedQuantileForecastingModel, self).__init__(
          model,
          examples=examples,
          time_feature=time_feature,
          time_series_id_features=time_series_id_features,
      )
      if self._model_package not in [_AZUREML, _SKTIME] and not hasattr(
          model, Forecasting.FORECAST_QUANTILES
      ):
          raise ValueError(
              "The passed model does not have a 'forecast_quantiles' "
              "method. 'forecast_quantiles' is optional for the "
              "forecasting task_type, but required for "
              "WrappedQuantileForecastingModel. Alternatively, pass a "
              "sktime forecasting model that supports predict_quantiles."
          )
  
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 1 to create a new _WrappedQuantileForecastingModel object from inherited base classes: _WrappedForecastingModel with example: obj = _WrappedQuantileForecastingModel(model, examples, time_feature, time_series_id_features)