code: def test_staged_predict_proba():
      prostate = h2o.import_file(
          path=pyunit_utils.locate("smalldata/prostate/prostate.csv")
      )
      prostate["CAPSULE"] = prostate["CAPSULE"].asfactor()
  
      prostate_gbm_50 = H2OGradientBoostingEstimator(
          ntrees=50, seed=123
      )
      prostate_gbm_50.train(
          x=["AGE", "RACE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON"],
          y="CAPSULE",
          training_frame=prostate,
      )
      preds_50 = prostate_gbm_50.predict(prostate)
  
      prostate_gbm_10 = H2OGradientBoostingEstimator(
          ntrees=10, seed=123
      )
      prostate_gbm_10.train(
          x=["AGE", "RACE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON"],
          y="CAPSULE",
          training_frame=prostate,
      )
      preds_10 = prostate_gbm_10.predict(prostate)
  
      staged_preds = prostate_gbm_50.staged_predict_proba(prostate)
  
      pyunit_utils.compare_frames_local(
          preds_50["p0"], staged_preds["T50.C1"], prob=1.0
      )
      pyunit_utils.compare_frames_local(
          preds_10["p0"], staged_preds["T10.C1"], prob=1.0
      )
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: test_staged_predict_proba() without return types