code: def predict(self, dataset, missing_value_action='auto'):
          
  
          return super(LinearRegression, self).predict(dataset, missing_value_action=missing_value_action)
docs: Return target value predictions for ``dataset``, using the trained
          linear regression model. This method can be used to get fitted values
          for the model by inputting the training dataset.
  
          Parameters
          ----------
          dataset : SFrame | pandas.Dataframe
              Dataset of new observations. Must include columns with the same
              names as the features used for model training, but does not require
              a target column. Additional columns are ignored.
  
          missing_value_action : str, optional
              Action to perform when missing values are encountered. This can be
              one of:
  
              - 'auto': Default to 'impute'
              - 'impute': Proceed with evaluation by filling in the missing
                values with the mean of the training data. Missing
                values are also imputed if an entire column of data is
                missing during evaluation.
              - 'error': Do not proceed with prediction and terminate with
                an error message.
  
  
          Returns
          -------
          out : SArray
              Predicted target value for each example (i.e. row) in the dataset.
  
          See Also
          ----------
          create, evaluate
  
          Examples
          ----------
          >>> data =  turicreate.SFrame('https://static.turi.com/datasets/regression/houses.csv')
  
          >>> model = turicreate.linear_regression.create(data,
                                               target='price',
                                               features=['bath', 'bedroom', 'size'])
          >>> results = model.predict(data)