code: def test_train_returns_the_trained_model():
      fr = h2o.import_file(
          path=pu.locate("smalldata/prostate/prostate.csv")
      )
      target = "CAPSULE"
      fr[target] = fr[target].asfactor()
  
      gbm = H2OGradientBoostingEstimator(
          model_id="py_gbm_train_result", seed=42
      )
      model = gbm.train(y=target, training_frame=fr)
  
      assert isinstance(model, ModelBase)
      assert model is gbm
      model.predict(fr)
  
imports: import os
  import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators import H2OGradientBoostingEstimator
  from h2o.model import ModelBase
label_desc: Use image node_id 1 for calling a global function with example usage: test_train_returns_the_trained_model() without return types