code: def _get_exog_predict(
      self, exog=None, transform=True, row_labels=None
  ):
      """Prepare or transform exog for 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.
  
      Returns
      -------
      exog : ndarray
          Prediction exog
      row_labels : list of str
          Labels or pandas index for rows of prediction
      """
  
      # prepare exog and row_labels, based on base Results.predict
      if (
          transform
          and hasattr(self.model, "formula")
          and exog is not None
      ):
          from patsy import dmatrix
  
          if isinstance(exog, pd.Series):
              exog = pd.DataFrame(exog)
          exog = dmatrix(self.model.data.design_info, exog)
  
      if exog is not None:
          if row_labels is None:
              row_labels = getattr(exog, "index", None)
              if callable(row_labels):
                  row_labels = None
  
          exog = np.asarray(exog)
          if exog.ndim == 1 and (
              self.model.exog.ndim == 1 or self.model.exog.shape[1] == 1
          ):
              exog = exog[:, None]
          exog = np.atleast_2d(exog)  # needed in count model shape[1]
      else:
          exog = self.model.exog
  
          if row_labels is None:
              row_labels = getattr(self.model.data, "row_labels", None)
      return exog, row_labels
  
imports: import numpy
  from scipy import stats
  import pandas
label_desc: Use image node_id 1 for calling a global function with example usage: _get_exog_predict(exog, transform, row_labels) and returns: exog,  row_labels