code: def get_prediction(
      self,
      exog=None,
      transform=True,
      weights=None,
      row_labels=None,
      pred_kwds=None,
  ):
      """
      Compute prediction results.
  
      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.
      weights : array_like, optional
          Weights interpreted as in WLS, used for the variance of the predicted
          residual.
      row_labels : list
          A list of row labels to use.  If not provided, read `exog` is
          available.
      **kwargs
          Some models can take additional keyword arguments, see the predict
          method of the model for the details.
  
      Returns
      -------
      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
      if (
          transform
          and hasattr(self.model, "formula")
          and exog is not None
      ):
          from patsy import dmatrix
  
          if isinstance(exog, pd.Series):
              # GH-6509
              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:
              # Params informs whether a row or column vector
              if self.params.shape[0] > 1:
                  exog = exog[None, :]
              else:
                  exog = exog[:, None]
          exog = np.atleast_2d(exog)  # needed in count model shape[1]
      else:
          exog = self.model.exog
          if weights is None:
              weights = getattr(self.model, "weights", None)
  
          if row_labels is None:
              row_labels = getattr(self.model.data, "row_labels", None)
  
      # need to handle other arrays, TODO: is delegating to model possible ?
      if weights is not None:
          weights = np.asarray(weights)
          if weights.size > 1 and (
              weights.ndim != 1 or weights.shape[0] == exog.shape[1]
          ):
              raise ValueError("weights has wrong shape")
  
      if pred_kwds is None:
          pred_kwds = {}
      predicted_mean = self.model.predict(
          self.params, exog, **pred_kwds
      )
  
      covb = self.cov_params()
      var_pred_mean = (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"]
  
      if weights is not None:
          var_resid /= weights
  
      dist = ["norm", "t"][self.use_t]
      return PredictionResults(
          predicted_mean,
          var_pred_mean,
          var_resid,
          df=self.df_resid,
          dist=dist,
          row_labels=row_labels,
      )
  
file_path: statsmodels/statsmodels/regression/_prediction.py
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_prediction(exog, transform, weights, row_labels, pred_kwds) and returns: PredictionResults