code: def test_if_h_fast_enough():
      import numpy as np
      import pandas as pd
  
      DATA_COUNT = 10000
      RANDOM_SEED = 137
      TRAIN_FRACTION = 0.9
  
      np.random.seed(RANDOM_SEED)
  
      xs = pd.DataFrame(np.random.uniform(size=(DATA_COUNT, 3)))
      xs.columns = ["x0", "x1", "x2"]
  
      y = pd.DataFrame(
          xs.x0 * xs.x1
          + xs.x2
          + pd.Series(0.1 * np.random.randn(DATA_COUNT))
      )
      y.columns = ["y"]
  
      train_ilocs = range(int(TRAIN_FRACTION * DATA_COUNT))
      test_ilocs = range(int(TRAIN_FRACTION * DATA_COUNT), DATA_COUNT)
  
      merged_data = pd.concat((xs, y), axis=1)
  
      gbm = H2OGradientBoostingEstimator(max_depth=50)
  
      data_sample = h2o.H2OFrame(merged_data)
      gbm.train(x=["x0", "x1", "x2"], y="y", training_frame=data_sample)
  
      h2o_single_pair_start_time = time.time()
      h2o_single_pair_h = gbm.h(
          frame=data_sample, variables=["x0", "x1"]
      )
      h2o_single_pair_end_time = time.time()
      print(
          "Result: {}; Diff: {}".format(
              h2o_single_pair_h, h2o_single_pair_h - 0.3804505590934481
          )
      )
      print(
          "Computing H took {}s".format(
              h2o_single_pair_end_time - h2o_single_pair_start_time
          )
      )
  
      assert (
          abs(h2o_single_pair_h - 0.3804505590934481) < 1e-10
      )  # Check the result with the pre-speedup result
      assert h2o_single_pair_end_time - h2o_single_pair_start_time < 90
  
imports: from builtins import range
  import sys, os, time
  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_if_h_fast_enough() without return types