code: def update_metric(self, eval_metric, labels, pre_sliced):
          
          for current_exec, (texec, islice) in enumerate(zip(self.execs, self.slices)):
              if not pre_sliced:
                  labels_slice = []
                  for label, axis in zip(labels, self.label_layouts):
                      if axis == 0:
                          # slicing NDArray along axis 0 can avoid copying
                          labels_slice.append(label[islice])
                      elif axis > 0:
                          # pylint: disable=no-member
                          label_my_slice = nd.slice_axis(label, axis=axis, begin=islice.start,
                                                         end=islice.stop).as_in_context(label.context)
                          # pylint: enable=no-member
                          labels_slice.append(label_my_slice)
                      else:
                          labels_slice.append(label)
              else:
                  labels_slice = labels[current_exec]
  
              labels_ = OrderedDict(zip(self.label_names, labels_slice))
              preds = OrderedDict(zip(self.output_names, texec.outputs))
              eval_metric.update_dict(labels_, preds)
docs: Accumulate the performance according to `eval_metric` on all devices
          by comparing outputs from [begin, end) to labels. By default use all
          outputs.
  
          Parameters
          ----------
          eval_metric : EvalMetric
              The metric used for evaluation.
          labels : list of NDArray
              Typically comes from `label` of a `DataBatch`.
          pre_sliced : bool
              Whether labels are already sliced.
          begin : int
              Starting index of used outputs.
          end : int or None
              Ending index of used outputs.