code: def logging_run(self, round_start, start, model_history):
      import time
  
      # count the duration of the round
      self._round_seconds = time.time() - start
  
      # set end time and log
      round_end = time.strftime("%D-%H%M%S")
      self.round_times.append(
          [round_start, round_end, self._round_seconds]
      )
  
      # handle first round only things
      if self.first_round:
          # capture the history keys for later
          self._all_keys = list(model_history.history.keys())
          self._metric_keys = [
              k for k in self._all_keys if "val_" not in k
          ]
          self._val_keys = [k for k in self._all_keys if "val_" in k]
  
          # create a header column for output
          _results_header = (
              ["round_epochs"] + self._all_keys + self._param_dict_keys
          )
          self.result.append(_results_header)
  
          # save the results
          from .results import save_result
  
          save_result(self)
  
          # avoid doing this again
          self.first_round = False
  
      # create log and other stats
      from ..metrics.entropy import epoch_entropy
  
      self.epoch_entropy.append(
          epoch_entropy(self, model_history.history)
      )
  
      # get round results to the results table and save it
      from .results import run_round_results
  
      _round_results = run_round_results(self, model_history)
  
      self.result.append(_round_results)
  
      from .results import save_result
  
      save_result(self)
  
      # return the Scan() self
      return self
  
label_desc: Use image node_id 1 for calling a global function with example usage: logging_run(round_start, start, model_history) and returns: self