code: def _classification_fetch(self, skip_missing=None):
          
          skip_missing = skip_missing if skip_missing else self._kwargs["skip_missing"]
  
          new_classifications = []
  
          for a in self._res_list:
              if a.__class__.__name__ == "Samples":
                  c = a.primary_classification
              elif a.__class__.__name__ == "Classifications":
                  c = a
              else:
                  raise OneCodexException(
                      "Objects in SampleCollection must be one of: Classifications, Samples"
                  )
  
              if skip_missing and not c.success:
                  warnings.warn("Classification {} not successful. Skipping.".format(c.id))
                  continue
  
              new_classifications.append(c)
  
          self._cached["classifications"] = new_classifications
docs: Turns a list of objects associated with a classification result into a list of
          Classifications objects.
  
          Parameters
          ----------
          skip_missing : `bool`
              If an analysis was not successful, exclude it, warn, and keep going
  
          Returns
          -------
          None, but stores a result in self._cached.