code: def test_event_log():
      ds = import_dataset()
      aml = H2OAutoML(
          project_name="test_event_log", max_models=2, seed=1234
      )
      aml.train(y=ds.target, training_frame=ds.train)
  
      print(aml.event_log)
      assert aml.event_log.columns == [
          "timestamp",
          "level",
          "stage",
          "message",
          "name",
          "value",
      ]
      assert aml.event_log.nrows > 10
  
      print(aml.training_info)
      assert int(aml.training_info["stop_epoch"]) > int(
          aml.training_info["start_epoch"]
      )
      stop_dt = dt.datetime.fromtimestamp(
          int(aml.training_info["stop_epoch"])
      )
      now = dt.datetime.now()
      # test that stop_epoch is time encoded as unix epoch
      assert abs(stop_dt - now) < dt.timedelta(minutes=1)
      assert (
          abs(
              int(aml.training_info["duration_secs"])
              - (
                  int(aml.training_info["stop_epoch"])
                  - int(aml.training_info["start_epoch"])
              )
          )
          <= 1
      )
  
imports: import sys, os, datetime
  from h2o.automl import H2OAutoML
  from tests import pyunit_utils
  from _automl_utils import import_dataset
label_desc: Use image node_id 1 for calling a global function with example usage: test_event_log() without return types