code: def get_batch(data_iterator):
      """Generate a batch."""
  
      # TODO: this is pretty hacky, find a better way
      if (not mpu.is_pipeline_first_stage()) and (
          not mpu.is_pipeline_last_stage()
      ):
          return None, None, None, None, None
  
      # get batches based on the TP rank you are on
      batch = get_batch_on_this_tp_rank(data_iterator)
  
      # slice batch along sequence dimension for context parallelism
      batch = get_batch_on_this_cp_rank(batch)
  
      return batch.values()
  
imports: import os
  import torch
  from torch import Tensor
  from functools import partial
  from typing import Union
  from megatron import get_args
  from megatron import print_rank_0
  from megatron import get_timers
  from megatron import get_tokenizer
  from megatron.core import mpu, tensor_parallel
  from megatron.core.enums import ModelType
  from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder
  from megatron.core.datasets.gpt_dataset import GPTDatasetConfig
  from megatron.core.datasets.gpt_dataset import GPTDataset
  import megatron.model
  from megatron.core.models.gpt import GPTModel
  from megatron.training import pretrain
  from megatron.core.transformer.spec_utils import import_module
  from megatron.utils import get_batch_on_this_cp_rank, get_batch_on_this_tp_rank, average_losses_across_data_parallel_group
  from megatron.arguments import core_transformer_config_from_args
  from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
label_desc: Use image node_id 2 for calling a global function with example usage: get_batch(data_iterator) and returns: batch, None,  None,  None,  None,  None