code: def process_batch(batch):
      """Process batch and produce inputs for the model."""
      args = get_args()
  
      tokens = batch["text"].long().cuda().contiguous()
      types = batch["types"].long().cuda().contiguous()
      labels = batch["label"].long().cuda().contiguous()
      attention_mask = batch["padding_mask"].float().cuda().contiguous()
      if args.fp16:
          attention_mask = attention_mask.half()
  
      return tokens, types, labels, attention_mask
  
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 1 for calling a global function with example usage: process_batch(batch) and returns: tokens,  types,  labels,  attention_mask