code: def _update_and_create_report(
      self,
      batch: Dict,
      batch_idx: int,
      step_output: Dict,
      pl_module: LightningModule,
      combined_report: Report = None,
      update_meter: Meter = None,
  ):
      report = Report(batch, step_output)
  
      # Normalize losses
      for key in report.losses.keys():
          report.losses[key] = (
              report.losses[key]
              / self.trainer_config.accumulate_grad_batches
          )
  
      if update_meter:
          update_meter.update_from_report(report)
  
      should_accumulate = not (
          batch_idx % self.trainer_config.accumulate_grad_batches == 0
      )
  
      final_report = report
      if should_accumulate and combined_report is not None:
          combined_report.accumulate_tensor_fields_and_loss(
              report, pl_module.metrics.required_params
          )
          combined_report.batch_size += report.batch_size
          final_report = combined_report
  
      return final_report
  
imports: import logging
  from typing import Any, Dict, List
  import torch
  from mmf.common.meter import Meter
  from mmf.common.registry import registry
  from mmf.common.report import Report
  from mmf.common.sample import SampleList
  from mmf.utils.logger import calculate_time_left, summarize_report
  from mmf.utils.timer import Timer
  from pytorch_lightning import LightningModule, Trainer
  from pytorch_lightning.callbacks.base import Callback
label_desc: Use image node_id 7 for calling the LightningLoopCallback obj's underlying member method code with example usage: obj._update_and_create_report(batch, batch_idx, step_output, pl_module, combined_report, update_meter) and returns: final_report