code: def process_batch(batch):
      """Process batch and produce inputs for the model."""
      args = get_args()
      tokenizer = get_tokenizer()
  
      loss_mask = batch["pad_mask"].long().cuda().contiguous().byte()
      tokens_ = batch["text"].long().cuda().contiguous()
      labels = tokens_[:, 1:].contiguous()
      tokens = tokens_[:, :-1].contiguous()
  
      # Get the masks and postition ids.
      attention_mask, _, position_ids = get_ltor_masks_and_position_ids(
          tokens,
          tokenizer.eod,
          args.reset_position_ids,
          args.reset_attention_mask,
          args.eod_mask_loss,
      )
  
      return tokens, labels, attention_mask, position_ids, loss_mask
  
file_path: megatron-lm/tasks/zeroshot_gpt/evaluate.py
imports: import math
  import torch
  from megatron import get_args
  from megatron import print_rank_0, is_last_rank
  from megatron import get_tokenizer
  from megatron.core import parallel_state, tensor_parallel
  from megatron.checkpointing import load_checkpoint
  from megatron.model import GPTModel
  from megatron.training import get_model
  from megatron.utils import get_ltor_masks_and_position_ids, unwrap_model
  from megatron.core.pipeline_parallel.p2p_communication import recv_forward, send_forward
  from megatron.arguments import core_transformer_config_from_args
  from tasks.finetune_utils import build_data_loader
  from .datasets import build_dataset
label_desc: Use image node_id 2 for calling a global function with example usage: process_batch(batch) and returns: tokens,  labels,  attention_mask,  position_ids,  loss_mask