code: def get_indexed_dataset_(
      data_prefix, data_impl, skip_warmup, delay_data_mmap=False
  ):
      """Build indexed dataset."""
      logging.info(" > building dataset index ...")
  
      start_time = time.time()
      indexed_dataset = make_indexed_dataset(
          data_prefix,
          data_impl,
          skip_warmup,
          delay_data_mmap=delay_data_mmap,
      )
      logging.info(
          " > finished creating indexed dataset in {:4f} "
          "seconds".format(time.time() - start_time)
      )
      logging.info(
          "    number of documents: {}".format(
              indexed_dataset.sizes.shape[0]
          )
      )
  
      return indexed_dataset
  
imports: import os
  import time
  import numpy
  import torch
  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.core import Dataset
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling a global function with example usage: get_indexed_dataset_(data_prefix, data_impl, skip_warmup, delay_data_mmap) and returns: indexed_dataset