code: def fast_estimator(estimator, **kwargs):
      additional_args = dict(seed=123456)
      if estimator in (
          H2ORandomForestEstimator,
          H2OGradientBoostingEstimator,
          H2OXGBoostEstimator,
      ):
          additional_args["ntrees"] = 5
      if estimator == H2ODeepLearningEstimator:
          additional_args["hidden"] = [5]
      if estimator == H2OGeneralizedAdditiveEstimator:
          additional_args["gam_columns"] = ["age"]
  
      return estimator(**additional_args, **kwargs)
  
file_path: h2o-3/h2o-py/tests/testdir_misc/pyunit_gainslift_bins.py
imports: import sys
  import h2o
  import pandas
  from tests import pyunit_utils
  from h2o.estimators import *
  from h2o.utils.threading import local_context
label_desc: Use image node_id 1 for calling a global function with example usage: fast_estimator(estimator) and returns: estimator