code: def test_split_subset_data_by_fold(self):
      TOTAL_LEN = random.randint(100, 200)
      a_end = random.randrange(1, TOTAL_LEN)
      b_end = random.randrange(a_end, TOTAL_LEN)
      SUBSET_A = [i for i in range(0, a_end)]
      SUBSET_B = [i for i in range(a_end, b_end)]
      SUBSET_C = [i for i in range(b_end, TOTAL_LEN)]
  
      SUBSETS_A = [deepcopy(SUBSET_A)]
      SUBSETS_A_B = [deepcopy(SUBSET_A), deepcopy(SUBSET_B)]
      SUBSETS_C_B_A = [
          deepcopy(SUBSET_C),
          deepcopy(SUBSET_B),
          deepcopy(SUBSET_A),
      ]
  
      train_frac = random.uniform(0, 1)
      valid_frac = random.uniform(0, 1 - train_frac)
      test_frac = 1 - train_frac - valid_frac
  
      TRAIN_A = DatatypeHelper.split_subset_data_by_fold(
          "train", SUBSETS_A, train_frac, valid_frac, test_frac
      )
      TRAIN_A_B = DatatypeHelper.split_subset_data_by_fold(
          "train", SUBSETS_A_B, train_frac, valid_frac, test_frac
      )
      TRAIN_C_B_A = DatatypeHelper.split_subset_data_by_fold(
          "train",
          deepcopy(SUBSETS_C_B_A),
          train_frac,
          valid_frac,
          test_frac,
      )
  
      # Check to make sure selected values for a fold within a domain are consistent even if different domains are used, and presented in different orders
      for val in SUBSET_A:
          state = bool(val in TRAIN_A)
          assert bool(val in TRAIN_A_B) == state
          assert bool(val in TRAIN_C_B_A) == state
  
      for val in SUBSET_B:
          state = bool(val in TRAIN_A_B)
          assert bool(val in TRAIN_C_B_A) == state
  
      # Check that train + valid + test covers everything
      VALID_C_B_A = DatatypeHelper.split_subset_data_by_fold(
          "valid",
          deepcopy(SUBSETS_C_B_A),
          train_frac,
          valid_frac,
          test_frac,
      )
      TEST_C_B_A = DatatypeHelper.split_subset_data_by_fold(
          "test",
          deepcopy(SUBSETS_C_B_A),
          train_frac,
          valid_frac,
          test_frac,
      )
  
      assert (
          len(TRAIN_C_B_A) + len(VALID_C_B_A) + len(TEST_C_B_A)
          is TOTAL_LEN
      )
      assert (
          len(set(TRAIN_C_B_A + VALID_C_B_A + TEST_C_B_A)) is TOTAL_LEN
      )
  
imports: from parlai.core.opt import Opt
  from parlai.utils.misc import Timer, round_sigfigs, set_namedtuple_defaults, nice_report
  import parlai.utils.strings
  from copy import deepcopy
  import random
  import time
  import unittest
  from parlai.utils.data import DatatypeHelper
  from parlai.utils.curated_response import generate_init_prompot, generate_safe_response
label_desc: Use image node_id 5 for calling the TestDatatypeHelper obj's underlying member method code with example usage: obj.test_split_subset_data_by_fold() without return types