code: def summary_without_hessian(self):
      t_z = self.z.get_z_values(transformed=True)
  
      print(
          "Hessian not invertible! Consider a different model specification."
      )
      print("")
  
      data = []
  
      for i in range(len(self.z.z_list)):
          data.append(
              {
                  "z_name": self.z.z_list[i].name,
                  "z_value": np.round(
                      self.z.z_list[i].prior.transform(
                          self.results.x[i]
                      ),
                      4,
                  ),
              }
          )
  
      fmt = [
          ("Latent Variable", "z_name", 40),
          ("Estimate", "z_value", 10),
      ]
  
      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(self.results.x), 4)
          )
      else:
          obj_desc = "Unnormalized Log Posterior: " + str(
              np.round(-self.objective_object(self.results.x), 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)
      if "Skewt" in self.model_name:
          print(
              "WARNING: Skew t distribution is not well-suited for MLE or MAP inference"
          )
          print(
              "Workaround 1: Use a t-distribution instead for MLE/MAP"
          )
          print(
              "Workaround 2: Use M-H or BBVI inference for Skew t distribution"
          )
  
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 5 for calling the MLEResults obj's underlying member method code with example usage: obj.summary_without_hessian() without return types