code: def test_time_split_dataset(test_df=df):
      in_time_train_set, out_time_test_set = time_split_dataset(
          dataset=test_df,
          train_start_date="2016-10-01",
          train_end_date="2016-11-01",
          holdout_end_date="2016-12-01",
          time_column="time",
      )
  
      expected_train = pd.DataFrame(
          {
              "space": ["space1", "space2"],
              "time": [
                  pd.to_datetime("2016-10-01"),
                  pd.to_datetime("2016-10-01"),
              ],
          }
      )
  
      expected_test = pd.DataFrame(
          {
              "space": ["space1", "space2"],
              "time": [
                  pd.to_datetime("2016-11-01"),
                  pd.to_datetime("2016-11-01"),
              ],
          }
      )
  
      assert in_time_train_set.reset_index(drop=True).equals(
          expected_train
      )
      assert out_time_test_set.reset_index(drop=True).equals(
          expected_test
      )
  
      # Testing optional argument `holdout_start_date`
      in_time_train_set, out_time_test_set = time_split_dataset(
          dataset=test_df,
          train_start_date="2016-10-01",
          train_end_date="2016-11-01",
          holdout_start_date="2016-11-03",
          holdout_end_date="2017-01-01",
          time_column="time",
      )
  
      expected_train = pd.DataFrame(
          {
              "space": ["space1", "space2"],
              "time": [
                  pd.to_datetime("2016-10-01"),
                  pd.to_datetime("2016-10-01"),
              ],
          }
      )
  
      expected_test = pd.DataFrame(
          {
              "space": ["space1", "space2"],
              "time": [
                  pd.to_datetime("2016-12-01"),
                  pd.to_datetime("2016-12-01"),
              ],
          }
      )
  
      assert in_time_train_set.reset_index(drop=True).equals(
          expected_train
      )
      assert out_time_test_set.reset_index(drop=True).equals(
          expected_test
      )
  
file_path: fklearn/tests/preprocessing/test_splitting.py
imports: from collections import Counter
  import hypothesis.strategies
  import pandas
  from hypothesis import given
  from hypothesis.extra.pandas import columns, data_frames, range_indexes
  from pandas.testing import assert_frame_equal
  from fklearn.preprocessing.splitting import space_time_split_dataset, time_split_dataset, stratified_split_dataset
label_desc: Use image node_id 1 for calling a global function with example usage: test_time_split_dataset(test_df) without return types