code: def get_prediction(
      self,
      exog=None,
      transform=True,
      which="mean",
      row_labels=None,
      average=False,
      agg_weights=None,
      pred_kwds=None,
  ):
      """
      Compute prediction results when endpoint transformation is valid.
  
      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.
      which : str
          Which statistic is to be predicted. Default is "mean".
          The available statistics and options depend on the model.
          see the model.predict docstring
      linear : bool
          Linear has been replaced by the `which` keyword and will be
          deprecated.
          If linear is True, then `which` is ignored and the linear
          prediction is returned.
      row_labels : list of str or None
          If row_lables are provided, then they will replace the generated
          labels.
      average : bool
          If average is True, then the mean prediction is computed, that is,
          predictions are computed for individual exog and then the average
          over observation is used.
          If average is False, then the results are the predictions for all
          observations, i.e. same length as ``exog``.
      agg_weights : ndarray, optional
          Aggregation weights, only used if average is True.
          The weights are not normalized.
      **kwargs :
          Some models can take additional keyword arguments, such as offset,
          exposure or additional exog in multi-part models like zero inflated
          models.
          See the predict method of the model for the details.
  
      Returns
      -------
      prediction_results : PredictionResults
          The prediction results instance contains prediction and prediction
          variance and can on demand calculate confidence intervals and
          summary dataframe for the prediction.
  
      Notes
      -----
      Status: new in 0.14, experimental
      """
      use_endpoint = getattr(self.model, "_use_endpoint", True)
  
      if which == "linear":
          res = get_prediction_linear(
              self,
              exog=exog,
              transform=transform,
              row_labels=row_labels,
              pred_kwds=pred_kwds,
          )
  
      elif (
          (which == "mean")
          and (use_endpoint is True)
          and (average is False)
      ):
          # endpoint transformation
          k1 = self.model.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
  
          pred_kwds["which"] = which
          # TODO: add link or ilink to all link based models (except zi
          link = getattr(self.model, "link", None)
          if link is None:
              # GLM
              if hasattr(self.model, "family"):
                  link = getattr(self.model.family, "link", None)
          if link is None:
              # defaulting to log link for count models
              import warnings
  
              warnings.warn("using default log-link in get_prediction")
              from statsmodels.genmod.families import links
  
              link = links.Log()
          res = get_prediction_monotonic(
              self,
              exog=exog,
              transform=transform,
              row_labels=row_labels,
              link=link,
              pred_kwds=pred_kwds,
              index=index,
          )
  
      else:
          # which is not mean or linear, or we need averaging
          res = get_prediction_delta(
              self,
              exog=exog,
              which=which,
              average=average,
              agg_weights=agg_weights,
              pred_kwds=pred_kwds,
          )
  
      return res
  
imports: import numpy
  from scipy import stats
  import pandas
label_desc: Use image node_id 6 for calling a global function with example usage: get_prediction(exog, transform, which, row_labels, average, agg_weights, pred_kwds) and returns: res