code: def gbm_predict_contributions_sorting_large():
      fr = h2o.import_file(
          path=pyunit_utils.locate(
              "bigdata/laptop/creditcardfraud/creditcardfraud.csv"
          )
      )
  
      m = H2OGradientBoostingEstimator(ntrees=10, seed=1234)
      m.train(x=list(range(0, fr.ncol)), y=30, training_frame=fr)
  
      contributions = m.predict_contributions(
          fr, top_n=-1, bottom_n=0, compare_abs=False
      )
      assert_equals(
          61, contributions.shape[1], "Wrong number of columns"
      )
      assert_equals(
          284807, contributions.shape[0], "Wrong number of rows"
      )
  
imports: from builtins import range
  import sys
  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: gbm_predict_contributions_sorting_large() without return types