code: def print_summary(self, decimals=2, **kwargs):
          
  
          # Print information about data first
          justify = string_justify(18)
  
          print(self)
          print("{} = '{}'".format(justify("event col"), self.event_col))
  
          if self.weights_col:
              print("{} = '{}'".format(justify("weights col"), self.weights_col))
  
          if self.strata:
              print("{} = {}".format(justify("strata"), self.strata))
  
          if self.penalizer > 0:
              print("{} = {}".format(justify("penalizer"), self.penalizer))
  
          print("{} = {}".format(justify("number of subjects"), self._n_unique))
          print("{} = {}".format(justify("number of periods"), self._n_examples))
          print("{} = {}".format(justify("number of events"), self.event_observed.sum()))
          print("{} = {:.{prec}f}".format(justify("log-likelihood"), self._log_likelihood, prec=decimals))
          print("{} = {} UTC".format(justify("time fit was run"), self._time_fit_was_called))
  
          for k, v in kwargs.items():
              print("{} = {}\n".format(justify(k), v))
  
          print(end="\n")
          print("---")
  
          df = self.summary
          # Significance codes last
          print(
              df.to_string(
                  float_format=format_floats(decimals),
                  formatters={"p": format_p_value(decimals), "exp(coef)": format_exp_floats(decimals)},
              )
          )
  
          # Significance code explanation
          print("---")
          print(
              "Log-likelihood ratio test = {:.{prec}f} on {} df, -log2(p)={:.{prec}f}".format(
                  *self._compute_likelihood_ratio_test(), prec=decimals
              )
          )
docs: Print summary statistics describing the fit, the coefficients, and the error bounds.
  
          Parameters
          -----------
          decimals: int, optional (default=2)
              specify the number of decimal places to show
          kwargs:
              print additional meta data in the output (useful to provide model names, dataset names, etc.) when comparing
              multiple outputs.