code: def get_batch(data_iterator):
      """Generate a batch"""
      args = get_args()
      tokenizer = get_tokenizer()
  
      # Items and their type.
      keys = ["text"]
      datatype = torch.int64
  
      # Broadcast data.
      if data_iterator is not None:
          data = next(data_iterator)
      else:
          data = None
      data_b = mpu.broadcast_data(keys, data, datatype)
  
      # Unpack.
      tokens_ = data_b["text"].long()
      labels = tokens_[:, 1:].contiguous()
      tokens = tokens_[:, :-1].contiguous()
  
      # Get the masks and postition ids.
      (
          attention_mask,
          loss_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, loss_mask, attention_mask, position_ids
  
imports: import torch
  from functools import partial
  import os
  import sys
  from megatron import get_args
  from megatron import get_timers
  from megatron import get_tokenizer
  from megatron import print_rank_0
  from megatron.core import mpu
  from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder
  from megatron.core.datasets.blended_megatron_dataset_config import GPTDatasetConfig
  from megatron.core.datasets.gpt_dataset import GPTDataset
  from megatron.model import GPTModel
  from megatron.core.enums import ModelType
  from megatron.training import pretrain
  from megatron.utils import get_ltor_masks_and_position_ids
  from megatron.utils import average_losses_across_data_parallel_group
label_desc: Use image node_id 2 for calling a global function with example usage: get_batch(data_iterator) and returns: tokens,  labels,  loss_mask,  attention_mask,  position_ids