code: def xgb_repro():
      name_node = pyunit_utils.hadoop_namenode()
      data = h2o.import_file(
          "hdfs://"
          + name_node
          + "/user/jenkins/bigdata/laptop/airlinesBillion_7Columns_5GB.csv",
          na_strings=["NA"],
      )
  
      train, test = data.split_frame(ratios=[0.99], seed=1)
      x = data.names
      y = "C31"
      x.remove(y)
  
      model = H2OXGBoostEstimator(
          ntrees=5,
          max_depth=6,
          learn_rate=0.1,
          seed=12345,
          backend="CPU",
      )
      model.train(x=x, y=y, training_frame=train)
      p1 = model.predict(test)
      model.train(x=x, y=y, training_frame=train)
      p2 = model.predict(test)
      p = p1.cbind(p2)
  
      diff = (p[1] != p[4]).as_data_frame()
      ndiffs = 0
      for i in range(len(diff) - 1):
          if diff.iat[i, 0] != 0:
              ndiffs += 1
  
      assert ndiffs == 0, "diffs %d out of %d rows" % (ndiffs, p1.nrows)
  
imports: import sys
  import os
  from tests import pyunit_utils
  import h2o
  from h2o.estimators import H2OXGBoostEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: xgb_repro() without return types