code: def on_batch_end(self) -> None:
      assert isinstance(self._first_loop_iter, bool)
      metrics = self.metrics
      self._progress_bar_metrics.update(metrics["pbar"])
      self._callback_metrics.update(metrics["callback"])
      self._logged_metrics.update(metrics["log"])
  
      assert self.trainer._results is not None
      # drop the reference to current batch and batch_size
      self.trainer._results.batch = None
      self.trainer._results.batch_size = None
  
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 15 for calling the _LoggerConnector obj's underlying member method code with example usage: obj.on_batch_end() without return types