code: def summary(self, transformed=True):
      z_values = self.z.get_z_values(transformed=False)
  
      data = []
  
      for i in range(len(self.z.z_list) - int(self.z_hide)):
          data.append(
              {
                  "z_name": self.z.z_list[i].name,
                  "z_mean": np.round(
                      self.mean_est[i], self.rounding_points
                  ),
                  "z_median": np.round(
                      self.median_est[i], self.rounding_points
                  ),
                  "ci": "("
                  + str(
                      np.round(
                          self.lower_95_est[i], self.rounding_points
                      )
                  )
                  + " | "
                  + str(
                      np.round(
                          self.upper_95_est[i], self.rounding_points
                      )
                  )
                  + ")",
              }
          )
  
      fmt = [
          ("Latent Variable", "z_name", 40),
          ("Median", "z_median", 18),
          ("Mean", "z_mean", 18),
          ("95% Credibility Interval", "ci", 25),
      ]
  
      model_details = []
  
      model_fmt = [
          (self.model_name, "model_details", 55),
          ("", "model_results", 50),
      ]
  
      if self.method == "MLE":
          obj_desc = "Log Likelihood: " + str(
              np.round(-self.objective_object(z_values), 4)
          )
      else:
          obj_desc = "Unnormalized Log Posterior: " + str(
              np.round(-self.objective_object(z_values), 4)
          )
  
      model_details.append(
          {
              "model_details": "Dependent Variable: " + self.data_name,
              "model_results": "Method: " + str(self.method),
          }
      )
      model_details.append(
          {
              "model_details": "Start Date: "
              + str(self.index[self.max_lag]),
              "model_results": obj_desc,
          }
      )
      model_details.append(
          {
              "model_details": "End Date: " + str(self.index[-1]),
              "model_results": "AIC: " + str(self.aic),
          }
      )
      model_details.append(
          {
              "model_details": "Number of observations: "
              + str(self.data_length),
              "model_results": "BIC: " + str(self.bic),
          }
      )
  
      print(TablePrinter(model_fmt, ul="=")(model_details))
      print("=" * 106)
      print(TablePrinter(fmt, ul="=")(data))
      print("=" * 106)
  
imports: import numpy
  from .output import TablePrinter
  from .tests import find_p_value
  from .inference import norm_post_sim
label_desc: Use image node_id 3 for calling the MCMCResults obj's underlying member method code with example usage: obj.summary(transformed) without return types