code: def _calculate_split_for_dataset_type(self, dataset_type):
      start = 0.0
      for data_type in self.config.split_train:
          if data_type == "seed":
              continue
          if dataset_type == data_type:
              return (start, start + self.config.split_train[data_type])
          start += self.config.split_train[data_type]
  
      if start > 1.0:
          raise ValueError(
              "Ratios of val plus test should not exceed 100%."
              + " Need to leave some percentage for training."
          )
      elif start == 1.0:
          warnings.warn(
              "All data in training set is used for val and/or test."
          )
  
      if dataset_type == "train":
          return (start, 1.0)
  
imports: import collections
  import os
  import typing
  import warnings
  from copy import deepcopy
  import mmf.utils.download
  import torch
  from mmf.datasets.base_dataset_builder import BaseDatasetBuilder
  from mmf.datasets.concat_dataset import MMFConcatDataset
  from mmf.datasets.subset_dataset import MMFSubset
  from mmf.utils.configuration import get_global_config, get_mmf_env, get_zoo_config
  from mmf.utils.general import get_absolute_path
  from omegaconf import open_dict
label_desc: Use image node_id 16 for calling the MMFDatasetBuilder obj's underlying member method code with example usage: obj._calculate_split_for_dataset_type(dataset_type) and returns: start,  int, start,  unknown