code: def run_training_epoch(self) -> None:
      should_break = False
      while self.num_updates < self.max_updates and not should_break:
          self.current_epoch += 1
          registry.register("current_epoch", self.current_epoch)
  
          # Seed the sampler in case if it is distributed
          self.dataset_loader.seed_sampler("train", self.current_epoch)
  
          # For iterable datasets we cannot determine length of dataset properly.
          # For those cases we set num_remaining_batches to be the (number of
          # updates remaining x update_frequency)
          num_remaining_batches = (
              (
                  (self.max_updates - self.num_updates)
                  * self.training_config.update_frequency
              )
              if isinstance(
                  self.train_loader.current_dataset,
                  torch.utils.data.IterableDataset,
              )
              else len(self.train_loader)
          )
  
          should_start_update = True
          for idx, batch in enumerate(self.train_loader):
              if should_start_update:
                  combined_report = None
                  self._start_update()
                  num_batches_for_this_update = min(
                      self.training_config.update_frequency,
                      num_remaining_batches,
                  )
                  should_start_update = False
  
              self.current_iteration += 1
              # batch execution starts here
              self.on_batch_start()
              self.profile("Batch load time")
  
              report = self.run_training_batch(
                  batch, num_batches_for_this_update
              )
              report = report.detach()
  
              # accumulate necessary params (including loss) for metric calculation
              if combined_report is None:
                  combined_report = report
              else:
                  combined_report.accumulate_tensor_fields_and_loss(
                      report, self.metrics.required_params
                  )
                  combined_report.batch_size += report.batch_size
  
              # batch execution ends here
              self.on_batch_end(
                  report=combined_report, meter=self.meter
              )
  
              # check if an update has finished or if it is the last, if no continue
              if (
                  (idx + 1) % self.training_config.update_frequency
                  and num_remaining_batches
                  != num_batches_for_this_update
              ):
                  continue
  
              self._finish_update()
              should_start_update = True
  
              should_log = False
              if (
                  self.num_updates
                  % self.logistics_callback.log_interval
                  == 0
              ):
                  should_log = True
                  # Calculate metrics every log interval for debugging
                  if self.training_config.evaluate_metrics:
                      combined_report.metrics = self.metrics(
                          combined_report, combined_report
                      )
                  self.meter.update_from_report(combined_report)
  
              self.on_update_end(
                  report=combined_report,
                  meter=self.meter,
                  should_log=should_log,
              )
  
              num_remaining_batches -= num_batches_for_this_update
  
              # Check if training should be stopped
              should_break = False
  
              if (
                  self.num_updates
                  % self.training_config.evaluation_interval
                  == 0
              ):
                  # Validation begin callbacks
                  self.on_validation_start()
  
                  logger.info(
                      "Evaluation time. Running on full validation set..."
                  )
                  # Validation and Early stopping
                  # Create a new meter for this case
                  report, meter = self.evaluation_loop("val")
  
                  # Validation end callbacks
                  stop = self.early_stop_callback.on_validation_end(
                      report=report, meter=meter
                  )
                  self.on_validation_end(report=report, meter=meter)
  
                  gc.collect()
  
                  if "cuda" in str(self.device):
                      torch.cuda.empty_cache()
  
                  if stop is True:
                      logger.info("Early stopping activated")
                      should_break = True
  
              if self.num_updates >= self.max_updates:
                  should_break = True
  
              if should_break:
                  break
  
imports: import gc
  import logging
  from abc import ABC
  from typing import Any, Dict
  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 to_device
  from mmf.utils.distributed import is_xla
  from mmf.utils.general import clip_gradients, extract_loss, get_max_updates
  from torch import Tensor
label_desc: Use image node_id 3 for calling the TrainerTrainingLoopMixin obj's underlying member method code with example usage: obj.run_training_epoch() without return types