code: def test_pubdev_3847():
      train = h2o.import_file(
          path=pyunit_utils.locate("smalldata/jira/pubdev_3847.csv"),
          destination_frame="train",
      )
      train.describe()
  
      ntrees = 100
      max_depth = 6
      min_rows = 5
      learn_rate = 0.1
      sample_rate = 0.8
      col_sample_rate_per_tree = 0.6
      nfolds = 2
      min_split_improvement = 1e-04
      response = "class"
      features = train.col_names.remove(response)
  
      print("Train 100 GBM models to test if it fails.")
      for i in range(1, 100):
          seed = randint(1000, 2000)
          print(i, ": train model with random seed: ", seed)
          my_gbm = H2OGradientBoostingEstimator(
              ntrees=ntrees,
              max_depth=max_depth,
              min_rows=min_rows,
              learn_rate=learn_rate,
              sample_rate=sample_rate,
              col_sample_rate_per_tree=col_sample_rate_per_tree,
              nfolds=nfolds,
              min_split_improvement=min_split_improvement,
              seed=seed,
          )
          my_gbm.train(
              x=features,
              y=response,
              training_frame=train,
              validation_frame=train,
          )
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
  from random import randint
label_desc: Use image node_id 1 for calling a global function with example usage: test_pubdev_3847() without return types