class_docstr: Wrapper for forecasting models.
  
  This provides a unified API for the forecast method.
  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","forecasting","models",".","This","provides","a","unified","API","for","the","forecast","method",".","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(_WrappedForecastingModel, self).__init__(
          model,
          eval_function=None,
          examples=examples,
          model_task=ModelTask.FORECASTING,
      )
      self._model_package = _get_model_package(model)
      if self._model_package not in [_AZUREML, _SKTIME] and not hasattr(
          model, Forecasting.FORECAST
      ):
          raise ValueError(
              "The passed model does not have a 'forecast' method. "
              "'forecast' is required for the forecasting task_type. "
              "Alternatively, pass a sktime forecasting model."
          )
      self._validate_time_features(
          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 1 to create a new _WrappedForecastingModel object from inherited base classes: BaseWrappedModel with example: obj = _WrappedForecastingModel(model, examples, time_feature, time_series_id_features)