code: def train_batch(self, data_iterator, epoch_idx, batch_idx):
      if self.neox_args.is_pipe_parallel:
          reduced_loss = megatron_train.train_step_pipe(
              neox_args=self.neox_args,
              timers=self.timers,
              model=self.model,
              data_iterator=data_iterator,
          )
      else:
          losses = []
          for _ in range(self.neox_args.gradient_accumulation_steps):
              self.timers("forward").start()
              loss = megatron_train.forward_step(
                  neox_args=self.neox_args,
                  timers=self.timers,
                  data_iterator=data_iterator,
                  model=self.model,
              )
              self.timers("forward").stop()
              losses.append(loss)
              # Calculate gradients, reduce across processes, and clip.
              self.timers("backward").start()
              megatron_train.backward_step(
                  neox_args=self.neox_args,
                  timers=self.timers,
                  optimizer=self.optimizer,
                  model=self.model,
                  loss=loss,
              )
              self.timers("backward").stop()
              # Update parameters.
              self.timers("optimizer").start()
              if self.neox_args.deepspeed:
                  self.model.step()
              else:
                  raise ValueError(
                      "Must be using deepspeed to run neox"
                  )
              self.timers("optimizer").stop()
          reduced_loss = {
              "lm_loss": megatron_utils.reduce_losses(losses).mean()
          }
  
      if (
          self.neox_args.precision == "fp16"
          and self.model.optimizer.overflow
      ):
          skipped_iter = 1
      else:
          skipped_iter = 0
      self.neox_args.iteration += 1
  
      self.overflow_monitor.check(
          skipped_iter
      )  # check for repeated overflow
      if (
          self.neox_args.log_gradient_noise_scale
      ):  # log noise scale if applicable
          self.noise_scale_logger.update()
  
      # get learning rate (if present) - if doing soft prompt tuning + pipe parallel, you
      # may have no tunable parameters on a specific rank
      if self.optimizer.param_groups:
          lr = self.optimizer.param_groups[0].get("lr", 0)
      else:
          lr = 0
  
      # Logging.
      (
          self.report_memory_flag,
          additional_metrics,
      ) = megatron_train.training_log(
          neox_args=self.neox_args,
          timers=self.timers,
          loss_dict=reduced_loss,
          total_loss_dict=self.total_train_loss_dict,
          learning_rate=lr,
          iteration=self.neox_args.iteration,
          loss_scale=self.optimizer.cur_scale
          if self.neox_args.precision == "fp16"
          else None,
          report_memory_flag=self.report_memory_flag,
          skipped_iter=skipped_iter,
          model=self.model,
          optimizer=self.optimizer,
          noise_scale_logger=self.noise_scale_logger,
          return_metrics=True,
      )
      if (
          additional_metrics is not None
          and additional_metrics["num_nans"] == 0
          and additional_metrics["num_skipped"] == 0
      ):
          self.tflops = (
              additional_metrics["flops_per_sec_per_gpu"] / 10**12
          )
  
      if (
          self.neox_args.exit_interval
          and self.neox_args.iteration % self.neox_args.exit_interval
          == 0
      ):
          torch.distributed.barrier()
          time_str = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
          megatron_utils.print_rank_0(
              "time: {} | exiting the program at iteration {}".format(
                  time_str, self.neox_args.iteration
              )
          )
          self.context.set_stop_requested(True)
      return reduced_loss
  
imports: import logging
  import pathlib
  import traceback
  from datetime import datetime
  import deepspeed
  import megatron.training
  import megatron.utils
  import torch
  from attrdict import AttrMap
  from det_utils import EarlyStoppingCallback, EvalHarness, LMReducers, TensorboardWriter, get_neox_args
  from megatron import mpu
  from megatron.checkpointing import load_checkpoint, save_checkpoint
  from megatron.data.data_utils import build_datasets_from_neox_args
  from determined import LOG_FORMAT, InvalidHP
  from determined.pytorch import DataLoader
  from determined.pytorch.deepspeed import DeepSpeedTrial, DeepSpeedTrialContext, ModelParallelUnit
label_desc: Use image node_id 4 for calling the GPT2Trial obj's underlying member method code with example usage: obj.train_batch(data_iterator, epoch_idx, batch_idx) and returns: reduced_loss