code: def core_gpt_dataset_config_from_args(args):
      return GPTDatasetConfig(
          is_built_on_rank=is_dataset_built_on_rank,
          random_seed=args.seed,
          sequence_length=args.seq_length,
          blend=args.data_path,
          blend_per_split=[
              args.train_data_path,
              args.valid_data_path,
              args.test_data_path,
          ],
          split=args.split,
          path_to_cache=args.data_cache_path,
          return_document_ids=args.retro_return_doc_ids,
          reset_position_ids=args.reset_position_ids,
          reset_attention_mask=args.reset_attention_mask,
          eod_mask_loss=args.eod_mask_loss,
          eod_id=get_tokenizer().eod,
      )
  
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 6 for calling a global function with example usage: core_gpt_dataset_config_from_args(args) and returns: GPTDatasetConfig