code: def test_flag_jumps_on_issue_provided_use_case():
      """
      Test example provided in issue is solved with `flag_jumps()`
      See issue # 711
      """
      # Setup
      df = pd.DataFrame(
          data=[
              ["2015-01-01 00:00:00", -0.76, 2, 2, 1.2],
              ["2015-01-01 01:00:00", -0.73, 2, 4, 1.1],
              ["2015-01-01 02:00:00", -0.71, 2, 4, 1.1],
              ["2015-01-01 03:00:00", -0.68, 2, 32, 1.1],
              ["2015-01-01 04:00:00", -0.65, 2, 2, 1.0],
              ["2015-01-01 05:00:00", -0.76, 2, 2, 1.2],
              ["2015-01-01 06:00:00", -0.73, 2, 4, 1.1],
              ["2015-01-01 07:00:00", -0.71, 2, 4, 1.1],
              ["2015-01-01 08:00:00", -0.68, 2, 32, 1.1],
              ["2015-01-01 09:00:00", -0.65, 2, 2, 1.0],
              ["2015-01-01 10:00:00", -0.76, 2, 2, 1.2],
              ["2015-01-01 11:00:00", -0.73, 2, 4, 1.1],
              ["2015-01-01 12:00:00", -0.71, 2, 4, 1.1],
              ["2015-01-01 13:00:00", -0.68, 2, 32, 1.1],
              ["2015-01-01 14:00:00", -0.65, 2, 2, 1.0],
              ["2015-01-01 15:00:00", -0.76, 2, 2, 1.2],
              ["2015-01-01 16:00:00", -0.73, 2, 4, 1.1],
              ["2015-01-01 17:00:00", -0.71, 2, 4, 1.1],
              ["2015-01-01 18:00:00", -0.68, 2, 32, 1.1],
              ["2015-01-01 19:00:00", -0.65, 2, 2, 1.0],
              ["2015-01-01 20:00:00", -0.76, 2, 2, 1.2],
              ["2015-01-01 21:00:00", -0.73, 2, 4, 1.1],
              ["2015-01-01 22:00:00", -0.71, 2, 4, 1.1],
              ["2015-01-01 23:00:00", -0.68, 2, 32, 1.1],
              ["2015-01-02 00:00:00", -0.65, 2, 2, 1.0],
          ],
          columns=[
              "DateTime",
              "column1",
              "column2",
              "column3",
              "column4",
          ],
      )
  
      df["DateTime"] = pd.to_datetime(df["DateTime"])
      df = df.set_index("DateTime")
      orig_cols = df.columns
  
      expected = np.zeros((25, 4), dtype=int)
      expected[3, 2] = 1
      expected[8, 2] = 1
      expected[13, 2] = 1
      expected[18, 2] = 1
      expected[23, 2] = 1
      expected_cols = [f"{c}_jump_flag" for c in orig_cols]
      expected_df = pd.DataFrame(
          expected, columns=expected_cols, index=df.index
      )
  
      # Exercise
      result = df.flag_jumps(
          scale="absolute", direction="increasing", threshold=2
      )
  
      # Verify
      assert list(result.columns) == list(orig_cols) + expected_cols
      assert result.filter(regex="flag").equals(expected_df)
  
imports: import numpy
  import pandas
  import pytest
  import janitor.timeseries
  from janitor.errors import JanitorError
  from janitor.timeseries import _flag_jumps_single_col
label_desc: Use image node_id 20 for calling a global function with example usage: test_flag_jumps_on_issue_provided_use_case() without return types