code: def test_row_to_tree_assignment_output_on_large_dataset():
      fr = h2o.import_file(
          path=pyunit_utils.locate(
              "bigdata/laptop/creditcardfraud/creditcardfraud.csv"
          )
      )
      target = "Class"
      fr[target] = fr[target].asfactor()
  
      gbm = H2OGradientBoostingEstimator(ntrees=1000, sample_rate=0.6)
      gbm.train(y=target, training_frame=fr)
  
      row_to_tree_assignment = gbm.row_to_tree_assignment(fr)
  
      names_expected = ["row_id"]
      names_expected.extend(
          [("tree_" + str(item)) for item in range(1, 1001)]
      )
  
      assert_equals(names_expected, row_to_tree_assignment.names)
      assert_equals(fr.nrows, row_to_tree_assignment.nrows)
  
imports: import os
  import sys
  import h2o
  from tests import pyunit_utils, assert_equals
  from h2o.estimators import H2OGradientBoostingEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: test_row_to_tree_assignment_output_on_large_dataset() without return types