def write_metrics(
        loss_dict: Mapping[str, torch.Tensor],
        data_time: float,
        prefix: str = "",
    ) -> None:
        """
        Args:
            loss_dict (dict): dict of scalar losses
            data_time (float): time taken by the dataloader iteration
            prefix (str): prefix for logging keys
        """
        metrics_dict = {k: v.detach().cpu().item() for k, v in loss_dict.items()}
        metrics_dict["data_time"] = data_time

        # Gather metrics among all workers for logging
        # This assumes we do DDP-style training, which is currently the only
        # supported method in detectron2.
        all_metrics_dict = comm.gather(metrics_dict)

        if comm.is_main_process():
            storage = get_event_storage()

            # data_time among workers can have high variance. The actual latency
            # caused by data_time is the maximum among workers.
            data_time = np.max([x.pop("data_time") for x in all_metrics_dict])
            storage.put_scalar("data_time", data_time)

            # average the rest metrics
            metrics_dict = {
                k: np.mean([x[k] for x in all_metrics_dict]) for k in all_metrics_dict[0].keys()
            }
            total_losses_reduced = sum(metrics_dict.values())
            if not np.isfinite(total_losses_reduced):
                raise FloatingPointError(
                    f"Loss became infinite or NaN at iteration={storage.iter}!\n"
                    f"loss_dict = {metrics_dict}"
                )

            storage.put_scalar("{}total_loss".format(prefix), total_losses_reduced)
            if len(metrics_dict) > 1:
                storage.put_scalars(**metrics_dict)

        Args:
            loss_dict (dict): dict of scalar losses
            data_time (float): time taken by the dataloader iteration
            prefix (str): prefix for logging keys
        
ControlNet-v1-1-nightly/annotator/oneformer/detectron2/engine/train_loop.py