code: def bernoulli_gbm():
      prostate_train = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/logreg/prostate_train.csv"
          )
      )
      prostate_train["CAPSULE"] = prostate_train["CAPSULE"].asfactor()
      gbm_h2o = H2OGradientBoostingEstimator(
          seed=1234,
          ntrees=100,
          learn_rate=0.1,
          max_depth=5,
          min_rows=10,
          distribution="bernoulli",
      )
      gbm_h2o.train(
          x=list(range(1, prostate_train.ncol)),
          y="CAPSULE",
          training_frame=prostate_train,
      )
      h = gbm_h2o.h(prostate_train, ["DPROS", "DCAPS"])
  
      #  Prediction has changed since PUBDEV-8768.
      #  Because previous implementation took also CAPSULE into input which is a bug.
      assert_equals(0.03924324673367584, h, delta=1e-5)
  
imports: from builtins import range
  import sys, os
  import h2o
  from tests import pyunit_utils, assert_equals
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: bernoulli_gbm() without return types