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","."]
docstr: Returns the forecast for the passed data.
docstr_tok: ["Returns","the","forecast","for","the","passed","data","."]
code: def forecast(self, X):
      """Returns the forecast for the passed data."""
      if self._model_package == _AZUREML:
          # AzureML forecasting models return a tuple of (forecast, data)
          # but we only want to return the actual forecast.
          return self._model.forecast(X)[0]
      if self._model_package == _SKTIME:
          return self._apply_sktime_method(self._model.predict, X)
  
      # default case
      return self._model.forecast(X)
  
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 3 for calling the _WrappedForecastingModel obj's underlying member method code with example usage: obj.forecast(X) and returns: self, self, self