code: def test_api_timestamp():
      prostate_train = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/logreg/prostate_train.csv"
          )
      )
  
      prostate_train["CAPSULE"] = prostate_train["CAPSULE"].asfactor()
  
      ntrees = 1
      learning_rate = 0.1
      depth = 5
      min_rows = 10
  
      # Build H2O GBM classification model:
      gbm_h2o = H2OGradientBoostingEstimator(
          ntrees=ntrees,
          learn_rate=learning_rate,
          max_depth=depth,
          min_rows=min_rows,
          distribution="bernoulli",
          model_id="test_timestamp",
      )
      gbm_h2o.train(
          x=list(range(1, prostate_train.ncol)),
          y="CAPSULE",
          training_frame=prostate_train,
      )
  
      model = h2o.get_model(model_id="test_timestamp")
      models = h2o.api("GET /3/Models")
      assert (
          model._model_json["timestamp"]
          == models["models"][0]["timestamp"]
      ), "Timestamp should be the same."
  
      assert gbm_h2o.start_time is not None and gbm_h2o.start_time > 0
      assert gbm_h2o.end_time is not None and gbm_h2o.end_time > 0
      assert gbm_h2o.run_time is not None and gbm_h2o.run_time > 0
  
      assert gbm_h2o.end_time - gbm_h2o.start_time == gbm_h2o.run_time
  
imports: from builtins import range
  import sys, os
  import h2o
  from tests import pyunit_utils
  import numpy
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: test_api_timestamp() without return types