How to use the torchelastic.metrics.get_elapsed_time_ms function in torchelastic

get_elapsed_time_ms(start_time),
            )

        # Note that the loop might not even start if the rendezvous was closed
        # due to one of the trainer processes completing earlier.
        while not elastic_coordinator.should_stop_training():
            start_time = time.time()
            snapshot = state.capture_snapshot()

            try:
                train_step_start_time = time.time()
                state, worker_stats = train_step(state)
                publish_metric(
                    "torchelastic",
                    "train_step.duration.ms",
                    get_elapsed_time_ms(train_step_start_time),
                )

                elastic_coordinator.monitor_progress(state, worker_stats)

                checkpoint_util.save_checkpoint(state, rank)
                if elastic_coordinator.should_rendezvous(state):
                    log.info("Rank {0} will re-rendezvous".format(rank))
                    # Executor told us, for whatever reason, to re-rendezvous.
                    # This can occur if another node encounters an error,
                    # if a new node becomes available to train,
                    # or potentially even if it's time to checkpoint.
                    break
                elastic_coordinator.report_progress(state)
            except StopIteration:
                log.info("Rank {0} finished all the iterations".format(rank))
                # Current trainer process completed processing assigned subset of

start_time = time.time()
        snapshot = state.capture_snapshot()

        try:
            store, rank, world_size = elastic_coordinator.rendezvous_barrier()
            elastic_coordinator.init_process_group()

            # load checkpoint if necessary
            state = checkpoint_util.load_checkpoint(state, rank)

            state_sync_start_time = time.time()
            state.sync(world_size, rank)
            publish_metric(
                "torchelastic",
                "state_sync.duration.ms",
                get_elapsed_time_ms(state_sync_start_time),
            )
            checkpoint_util.set_checkpoint_loaded()
            log.info("Rank {0} synced state with other nodes".format(rank))
        except StopException:
            log.info("Rank {0} received stopped signal. Exiting training.".format(rank))
            break
        except RuntimeError as e:
            # See: https://github.com/pytorch/elastic/issues/7
            elastic_coordinator.on_error(e)
            state.apply_snapshot(snapshot)
            failure_count += 1
            continue
        except (NonRetryableException, Exception) as e:
            elastic_coordinator.on_error(e)
            raise
        finally:

log.info("Rank {0} received stopped signal. Exiting training.".format(rank))
            break
        except RuntimeError as e:
            # See: https://github.com/pytorch/elastic/issues/7
            elastic_coordinator.on_error(e)
            state.apply_snapshot(snapshot)
            failure_count += 1
            continue
        except (NonRetryableException, Exception) as e:
            elastic_coordinator.on_error(e)
            raise
        finally:
            publish_metric(
                "torch_elastic",
                "outer_train_loop.duration.ms",
                get_elapsed_time_ms(start_time),
            )

        # Note that the loop might not even start if the rendezvous was closed
        # due to one of the trainer processes completing earlier.
        while not elastic_coordinator.should_stop_training():
            start_time = time.time()
            snapshot = state.capture_snapshot()

            try:
                train_step_start_time = time.time()
                state, worker_stats = train_step(state)
                publish_metric(
                    "torchelastic",
                    "train_step.duration.ms",
                    get_elapsed_time_ms(train_step_start_time),
                )