code: def _import_warnings(self):
          
          warnings = (
              r"Warning: BMDL computation is at best imprecise for these data",
              r"THE MODEL HAS PROBABLY NOT CONVERGED!!!",
              "THIS USUALLY MEANS THE MODEL HAS NOT CONVERGED!",
              r"BMR value is not in the range of the mean function",
              r"BMD = 100\*\(maximum dose\)",
              r"BMDL computation failed\.",
              "Warning:  optimum may not have been found.  Bad completion code in Optimization routine.",  # noqa
              "Warning: Likelihood for fitted model larger than the Likelihood for model A3.",  # noqa
          )
          self.output["warnings"] = []
          for warning in warnings:
              m = re.search(warning, self.output_text)
              if m:
                  self.output["warnings"].append(m.group())
docs: Add custom warnings found in output files.
  
          Warnings in output files are searched for using this method; if a
          warning is found then it will be appended to the warnings list.