code: def test_describe_timeseries():
      # testing the describe timeseries function
      x = [
          datetime.datetime(2018, 1, 1, 0, 0, 1),
          datetime.datetime(2018, 1, 1, 0, 0, 2),
          datetime.datetime(2018, 1, 1, 0, 0, 3),
      ]
  
      time_stats = describe_timeseries(
          df=pd.DataFrame({"x": x}), time_col="x"
      )
      assert time_stats["regular_increments"] is True
      assert time_stats["min_timestamp"] == datetime.datetime(
          2018, 1, 1, 0, 0, 1
      )
      assert time_stats["max_timestamp"] == datetime.datetime(
          2018, 1, 1, 0, 0, 3
      )
      assert time_stats["mean_increment_secs"] == 1.0
      assert time_stats["min_increment_secs"] == 1.0
      assert time_stats["median_increment_secs"] == 1.0
      assert time_stats["freq_in_secs"] == 1.0
      assert time_stats["freq_in_days"] == 1 / (24 * 3600)
      assert time_stats["freq_in_timedelta"] == datetime.timedelta(
          days=1 / (24 * 3600)
      )
  
      x = [
          datetime.datetime(2018, 1, 1, 0, 0, 1),
          datetime.datetime(2018, 1, 1, 0, 0, 2),
          datetime.datetime(2018, 1, 1, 0, 0, 4),
      ]
  
      time_stats = describe_timeseries(
          df=pd.DataFrame({"x": x}), time_col="x"
      )
      assert time_stats["regular_increments"] is False
      assert time_stats["min_timestamp"] == datetime.datetime(
          2018, 1, 1, 0, 0, 1
      )
      assert time_stats["max_timestamp"] == datetime.datetime(
          2018, 1, 1, 0, 0, 4
      )
      assert time_stats["mean_increment_secs"] == 1.5
      assert time_stats["min_increment_secs"] == 1.0
      assert time_stats["median_increment_secs"] == 1.5
      assert time_stats["freq_in_secs"] == 1.5
      assert time_stats["freq_in_days"] == 1.5 / (24 * 3600)
      assert time_stats["freq_in_timedelta"] == datetime.timedelta(
          days=1.5 / (24 * 3600)
      )
  
      x = [
          datetime.datetime(2018, 1, 1, 0, 0, 3),
          datetime.datetime(2018, 1, 1, 0, 0, 2),
          datetime.datetime(2018, 1, 1, 0, 0, 1),
      ]
  
      time_stats = describe_timeseries(
          df=pd.DataFrame({"x": x}), time_col="x"
      )
      assert time_stats["increasing"] is False
      assert time_stats["regular_increments"] is True
  
imports: import datetime
  import numpy
  import pandas
  import pytest
  from greykite.common.constants import ADJUSTMENT_DELTA_COL
  from greykite.common.constants import END_TIME_COL
  from greykite.common.constants import METRIC_COL
  from greykite.common.constants import START_TIME_COL
  from greykite.common.constants import TIME_COL
  from greykite.common.constants import VALUE_COL
  from greykite.common.data_loader import DataLoader
  from greykite.common.enums import TimeEnum
  from greykite.common.python_utils import assert_equal
  from greykite.common.testing_utils import generate_df_with_reg_for_tests
  from greykite.common.time_properties import describe_timeseries
  from greykite.common.time_properties import fill_missing_dates
  from greykite.common.time_properties import find_missing_dates
  from greykite.common.time_properties import get_canonical_data
  from greykite.common.time_properties import infer_freq
  from greykite.common.time_properties import min_gap_in_seconds
label_desc: Use image node_id 1 for calling a global function with example usage: test_describe_timeseries() without return types