code: def training_step(self, dataloader_iter: Iterator) -> STEP_OUTPUT:
      if self.batch_i_handle is None:
          batch_i_raw, _, _ = next(dataloader_iter)
          self.num_batches_processed += 1
          self.batch_i_handle = self._async_op(batch_i_raw)
  
      # Invariant: _async_op for batch[i] has been initiated
      batch_ip1_handle = None
      is_last = False
      try:
          batch_ip1_raw, _, _ = next(dataloader_iter)
          self.num_batches_processed += 1
          batch_ip1_handle = self._async_op(batch_ip1_raw)
      except StopIteration:
          is_last = True
  
      batch_i = self.batch_i_handle.wait()
  
      loss = self.step(batch_i)
      loss.backward()
      self.optimizers().step()
      self.optimizers().zero_grad()
  
      self.batch_i_handle = batch_ip1_handle
  
      return {"loss": loss, "is_last": is_last}
  
imports: from collections import Counter
  from typing import Any, Iterator
  import pytest
  import torch
  from lightning.pytorch import LightningDataModule, Trainer
  from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
  from lightning.pytorch.loops.fetchers import _DataLoaderIterDataFetcher, _PrefetchDataFetcher
  from lightning.pytorch.profilers import SimpleProfiler
  from lightning.pytorch.utilities.combined_loader import CombinedLoader
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.types import STEP_OUTPUT
  from torch import Tensor
  from torch.utils.data import DataLoader, Dataset, IterableDataset
  from tests_pytorch.helpers.runif import RunIf
label_desc: Use image node_id 3 for calling the AsyncBoringModel obj's underlying member method code with example usage: obj.training_step(dataloader_iter) and returns: dict