code: def build_train_valid_test_datasets(
      data_prefix,
      data_impl,
      splits_string,
      train_valid_test_num_samples,
      max_seq_length,
      masked_lm_prob,
      short_seq_prob,
      seed,
      skip_warmup,
      binary_head,
      dataset_type="standard_bert",
  ):
      if len(data_prefix) == 1:
          return _build_train_valid_test_datasets(
              data_prefix[0],
              data_impl,
              splits_string,
              train_valid_test_num_samples,
              max_seq_length,
              masked_lm_prob,
              short_seq_prob,
              seed,
              skip_warmup,
              binary_head,
              dataset_type=dataset_type,
          )
      # Blending dataset.
      # Parse the values.
      output = get_datasets_weights_and_num_samples(
          data_prefix, train_valid_test_num_samples
      )
      prefixes, weights, datasets_train_valid_test_num_samples = output
  
      # Build individual datasets.
      train_datasets = []
      valid_datasets = []
      test_datasets = []
      for i in range(len(prefixes)):
          (
              train_ds,
              valid_ds,
              test_ds,
          ) = _build_train_valid_test_datasets(
              prefixes[i],
              data_impl,
              splits_string,
              datasets_train_valid_test_num_samples[i],
              max_seq_length,
              masked_lm_prob,
              short_seq_prob,
              seed,
              skip_warmup,
              binary_head,
              dataset_type=dataset_type,
          )
          if train_ds:
              train_datasets.append(train_ds)
          if valid_ds:
              valid_datasets.append(valid_ds)
          if test_ds:
              test_datasets.append(test_ds)
  
          # Blend.
      blending_train_dataset = None
      if train_datasets:
          blending_train_dataset = BlendableDataset(
              train_datasets, weights
          )
      blending_valid_dataset = None
      if valid_datasets:
          blending_valid_dataset = BlendableDataset(
              valid_datasets, weights
          )
      blending_test_dataset = None
      if test_datasets:
          blending_test_dataset = BlendableDataset(
              test_datasets, weights
          )
  
      return (
          blending_train_dataset,
          blending_valid_dataset,
          blending_test_dataset,
      )
  
file_path: ColossalAI/examples/tutorial/sequence_parallel/data/datasets/dataset_utils.py
imports: import collections
  import math
  import time
  import numpy
  from colossalai.logging import get_dist_logger
  from .blendable_dataset import BlendableDataset
  from .indexed_dataset import make_dataset
label_desc: Use image node_id 9 for calling a global function with example usage: build_train_valid_test_datasets(data_prefix, data_impl, splits_string, train_valid_test_num_samples, max_seq_length, masked_lm_prob, short_seq_prob, seed, skip_warmup, binary_head, dataset_type) and returns: blending_train_dataset,  blending_valid_dataset,  blending_test_dataset, _build_train_valid_test_datasets