code: def update_train_step_metrics(self) -> None:
      if (
          self.trainer.fit_loop._should_accumulate()
          and self.trainer.lightning_module.automatic_optimization
      ):
          return
  
      # when metrics should be logged
      assert isinstance(self._first_loop_iter, bool)
      if self.should_update_logs or self.trainer.fast_dev_run:
          self.log_metrics(self.metrics["log"])
  
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 10 for calling the _LoggerConnector obj's underlying member method code with example usage: obj.update_train_step_metrics() without return types