def process(self, data_batch, data_samples: Sequence[dict]):
        """Process one batch of data samples.

        The processed results should be stored in ``self.results``, which will
        be used to computed the metrics when all batches have been processed.

        Args:
            data_batch: A batch of data from the dataloader.
            data_samples (Sequence[dict]): A batch of outputs from the model.
        """

        for data_sample in data_samples:
            result = dict()

            result['pred_score'] = data_sample['pred_score'].clone()
            num_classes = result['pred_score'].size()[-1]

            if 'gt_score' in data_sample:
                result['gt_score'] = data_sample['gt_score'].clone()
            else:
                result['gt_score'] = label_to_onehot(data_sample['gt_label'],
                                                     num_classes)

            # Save the result to `self.results`.
            self.results.append(result)
Process one batch of data samples.

        The processed results should be stored in ``self.results``, which will
        be used to computed the metrics when all batches have been processed.

        Args:
            data_batch: A batch of data from the dataloader.
            data_samples (Sequence[dict]): A batch of outputs from the model.
        
mmpretrain/evaluation/metrics/multi_label.py