code: def update_train_epoch_metrics(self) -> None:
      # add the metrics to the loggers
      assert self._first_loop_iter is None
      self.log_metrics(self.metrics["log"])
  
      # reset result collection for next epoch
      self.reset_results()
  
imports: from typing import Any, Iterable, Optional, Union
  from lightning_utilities.core.apply_func import apply_to_collection
  from torch import Tensor
  import lightning.pytorch
  from lightning.fabric.loggers.tensorboard import _TENSORBOARD_AVAILABLE, _TENSORBOARDX_AVAILABLE
  from lightning.fabric.plugins.environments import SLURMEnvironment
  from lightning.fabric.utilities import move_data_to_device
  from lightning.fabric.utilities.apply_func import convert_tensors_to_scalars
  from lightning.pytorch.loggers import CSVLogger, Logger, TensorBoardLogger
  from lightning.pytorch.trainer.connectors.logger_connector.result import _METRICS, _OUT_DICT, _PBAR_DICT
  from lightning.pytorch.utilities.rank_zero import WarningCache
label_desc: Use image node_id 11 for calling the _LoggerConnector obj's underlying member method code with example usage: obj.update_train_epoch_metrics() without return types