code: def train_batch(
      self,
      dataloader_iter: Optional[Iterator[pytorch.TorchData]],
      epoch_idx: int,
      batch_idx: int,
  ) -> Union[torch.Tensor, Dict[str, Any]]:
      losses = []
      num_batches = 1
      if self.hparams.test_manual_grad_acc:
          num_batches = self.model.gradient_accumulation_steps()
      if self.hparams.test_fail_manual_grad_acc:
          num_batches = self.model.gradient_accumulation_steps() - 1
      for _ in range(num_batches):
          x, y = self.context.to_device(next(dataloader_iter))
          preds = self.model(x)
          loss = self.loss(y, preds)
          self.model.backward(loss)
          self.model.step()
          losses.append(loss.cpu().detach().numpy())
      if self.reducer is not None:
          self.reducer.update(losses)
  
      if self.hparams.return_non_scalar_metrics:
          return {"loss": np.mean(losses), "losses": losses}
      return {"loss": np.mean(losses)}
  
imports: from typing import Any, Dict, Iterator, Optional, Tuple, Union
  import attrdict
  import deepspeed
  import deepspeed.runtime.dataloader
  import numpy
  import torch
  import determined.pytorch.deepspeed
  import tests.experiment.fixtures.pytorch_counter_callback
  from determined import pytorch
label_desc: Use image node_id 4 for calling the LinearDeepSpeedTrial obj's underlying member method code with example usage: obj.train_batch(dataloader_iter, epoch_idx, batch_idx) and returns: dict, dict