code: def _cross_entropy_forward_step(batch, model):
      """Simple forward step with cross-entropy loss."""
      timers = get_timers()
  
      # Get the batch.
      timers("batch-generator", log_level=2).start()
      try:
          batch_ = next(batch)
      except BaseException:
          batch_ = batch
      tokens, types, labels, attention_mask = process_batch(batch_)
      timers("batch-generator").stop()
  
      # Forward model.
      output_tensor = model(tokens, attention_mask, tokentype_ids=types)
  
      return output_tensor, partial(cross_entropy_loss_func, labels)
  
imports: from functools import partial
  import sys
  import torch
  from megatron import get_args, get_num_microbatches
  from megatron import print_rank_0
  from megatron import get_timers
  from megatron.core import mpu
  from megatron.core.enums import ModelType
  from megatron.checkpointing import load_checkpoint
  from megatron.checkpointing import save_checkpoint
  from megatron.training import evaluate_and_print_results
  from megatron.training import setup_model_and_optimizer
  from megatron.training import train_step
  from megatron.training import training_log
  from megatron.utils import average_losses_across_data_parallel_group
  from megatron.utils import calc_params_l2_norm
  from megatron.utils import check_adlr_autoresume_termination
label_desc: Use image node_id 3 for calling a global function with example usage: _cross_entropy_forward_step(batch, model) and returns: output_tensor,  partial