code: def get_indexed_dataset_(
      data_prefix, data_impl, skip_warmup, data_impl_kwargs={}
  ):
      logging.info(" > building dataset index ...")
  
      start_time = time.time()
      indexed_dataset = make_indexed_dataset(
          data_prefix,
          data_impl,
          skip_warmup,
          impl_kwargs=data_impl_kwargs,
      )
      if data_impl in ["text_mmap", "csv_mmap"]:
          # make csv/text memmap compatible with Megatron sampling
          make_indexed_dataset_compatibility(indexed_dataset)
  
      assert (
          indexed_dataset.sizes.shape[0] == indexed_dataset.doc_idx[-1]
      )
      logging.info(
          " > finished creating indexed dataset in {:4f} "
          "seconds".format(time.time() - start_time)
      )
  
      logging.info(" > indexed dataset stats:")
      logging.info(
          "    number of documents: {}".format(
              indexed_dataset.doc_idx.shape[0] - 1
          )
      )
      logging.info(
          "    number of sentences: {}".format(
              indexed_dataset.sizes.shape[0]
          )
      )
  
      return indexed_dataset
  
imports: import collections
  import os
  import subprocess
  import time
  from typing import Any
  import numpy
  import torch
  from omegaconf import OmegaConf
  from omegaconf.dictconfig import DictConfig
  from nemo.collections.nlp.data.language_modeling.megatron.base_dataset_utils import get_datasets_weights_and_num_samples, get_train_valid_test_split_
  from nemo.collections.nlp.data.language_modeling.megatron.blendable_dataset import BlendableDataset
  from nemo.collections.nlp.data.language_modeling.megatron.indexed_dataset import deallocate_indexed_dataset_memory
  from nemo.collections.nlp.data.language_modeling.megatron.indexed_dataset import make_dataset
  from nemo.collections.nlp.data.language_modeling.megatron.indexed_dataset import make_indexed_dataset_compatibility
  from nemo.collections.nlp.data.language_modeling.megatron.length_distribution_type import LengthDistribution
  from nemo.collections.nlp.data.language_modeling.megatron.lm_adapted_t5_dataset import T5LMAdaptedDataset
  from nemo.utils import logging
  from nemo.utils.get_rank import is_global_rank_zero
label_desc: Use image node_id 14 for calling a global function with example usage: get_indexed_dataset_(data_prefix, data_impl, skip_warmup, data_impl_kwargs) and returns: indexed_dataset