code: def get_prediction_linear(
      self,
      exog=None,
      transform=True,
      row_labels=None,
      pred_kwds=None,
      index=None,
  ):
      """
      Compute prediction results for linear prediction.
  
      Parameters
      ----------
      exog : array_like, optional
          The values for which you want to predict.
      transform : bool, optional
          If the model was fit via a formula, do you want to pass
          exog through the formula. Default is True. E.g., if you fit
          a model y ~ log(x1) + log(x2), and transform is True, then
          you can pass a data structure that contains x1 and x2 in
          their original form. Otherwise, you'd need to log the data
          first.
      row_labels : list of str or None
          If row_lables are provided, then they will replace the generated
          labels.
      pred_kwargs :
          Some models can take additional keyword arguments, such as offset or
          additional exog in multi-part models.
          See the predict method of the model for the details.
      index : slice or array-index
          Is used to select rows and columns of cov_params, if the prediction
          function only depends on a subset of parameters.
  
      Returns
      -------
      prediction_results : PredictionResults
          The prediction results instance contains prediction and prediction
          variance and can on demand calculate confidence intervals and summary
          tables for the prediction.
      """
  
      # prepare exog and row_labels, based on base Results.predict
      exog, row_labels = _get_exog_predict(
          self,
          exog=exog,
          transform=transform,
          row_labels=row_labels,
      )
  
      if pred_kwds is None:
          pred_kwds = {}
  
      k1 = exog.shape[1]
      if len(self.params > k1):
          # TODO: we allow endpoint transformation only for the first link
          index = np.arange(k1)
      else:
          index = None
      # get linear prediction and standard errors
      covb = self.cov_params(column=index)
      var_pred = (exog * np.dot(covb, exog.T).T).sum(1)
      pred_kwds_linear = pred_kwds.copy()
      pred_kwds_linear["which"] = "linear"
      predicted = self.model.predict(
          self.params, exog, **pred_kwds_linear
      )
  
      dist = ["norm", "t"][self.use_t]
      res = PredictionResultsBase(
          predicted,
          var_pred,
          df=self.df_resid,
          dist=dist,
          row_labels=row_labels,
      )
      return res
  
imports: import numpy
  from scipy import stats
  import pandas
label_desc: Use image node_id 3 for calling a global function with example usage: get_prediction_linear(exog, transform, row_labels, pred_kwds, index) and returns: res