code: def test_split_dataset():
      current_dir = os.path.dirname(os.path.realpath(__file__))
      data_dir = os.path.join(current_dir, "fixtures/data/distillation")
      num_of_examples = count_examples(data_dir + os.sep + "train.txt")
      labeled_precentage = 0.4
      unlabeled_precentage = 0.5
      if os.path.exists(data_dir):
          labeled_file = "labeled.txt"
          unlabeled_file = "unlabeled.txt"
          split_column_dataset(
              dataset=os.path.join(data_dir, "train.txt"),
              first_count=math.ceil(
                  num_of_examples * labeled_precentage
              ),
              second_count=math.ceil(
                  num_of_examples * unlabeled_precentage
              ),
              out_folder=data_dir,
              first_filename=labeled_file,
              second_filename=unlabeled_file,
          )
          check_labeled_count = count_examples(
              data_dir + os.sep + labeled_file
          )
          assert check_labeled_count == math.ceil(
              num_of_examples * labeled_precentage
          )
          check_unlabeled_count = count_examples(
              data_dir + os.sep + unlabeled_file
          )
          assert check_unlabeled_count == math.ceil(
              num_of_examples * unlabeled_precentage
          )
          os.remove(data_dir + os.sep + "labeled.txt")
          os.remove(data_dir + os.sep + "unlabeled.txt")
  
file_path: nlp-architect/tests/test_data_utils.py
imports: import math
  import os
  import torch
  from nlp_architect.data.utils import split_column_dataset
  from tests.utils import count_examples
  from nlp_architect.nn.torch.data.dataset import CombinedTensorDataset
  from torch.utils.data import TensorDataset
label_desc: Use image node_id 2 for calling a global function with example usage: test_split_dataset() without return types