code: def get_prediction_glm(
      self,
      exog=None,
      transform=True,
      row_labels=None,
      linpred=None,
      link=None,
      pred_kwds=None,
  ):
      """
      Compute prediction results for GLM compatible models.
  
      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.
      linpred : linear prediction instance
          Instance of linear prediction results used for confidence intervals
          based on endpoint transformation.
      link : instance of link function
          If no link function is provided, then the `model.family.link` is used.
      pred_kwds : dict
          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.
  
      Returns
      -------
      prediction_results : generalized_linear_model.PredictionResults
          The prediction results instance contains prediction and prediction
          variance and can on demand calculate confidence intervals and summary
          tables for the prediction of the mean and of new observations.
      """
  
      # 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 = {}
  
      predicted_mean = self.model.predict(
          self.params, exog, **pred_kwds
      )
  
      covb = self.cov_params()
  
      link_deriv = self.model.family.link.inverse_deriv(
          linpred.predicted_mean
      )
      var_pred_mean = link_deriv**2 * (
          exog * np.dot(covb, exog.T).T
      ).sum(1)
      var_resid = self.scale  # self.mse_resid / weights
  
      # TODO: check that we have correct scale, Refactor scale #???
      # special case for now:
      if self.cov_type == "fixed scale":
          var_resid = self.cov_kwds["scale"]
  
      dist = ["norm", "t"][self.use_t]
      return PredictionResultsMean(
          predicted_mean,
          var_pred_mean,
          var_resid,
          df=self.df_resid,
          dist=dist,
          row_labels=row_labels,
          linpred=linpred,
          link=link,
      )
  
imports: import numpy
  from scipy import stats
  import pandas
label_desc: Use image node_id 2 for calling a global function with example usage: get_prediction_glm(exog, transform, row_labels, linpred, link, pred_kwds) and returns: PredictionResultsMean