code: def h2o_H2OFrame_stats():
      """
      Python API test: h2o.frame.H2OFrame.max(), h2o.frame.H2OFrame.mean(), h2o.frame.H2OFrame.median(),
      h2o.frame.H2OFrame.min(),
      """
      row_num = randrange(1, 10)
      col_num = randrange(1, 10)
      python_lists = np.random.randint(-5, 5, (row_num, col_num))
      h2oframe = h2o.H2OFrame(python_obj=python_lists)
      assert (
          abs(h2oframe.max() - np.ndarray.max(python_lists)) < 1e-12
      ), "h2o.H2OFrame.max() command is not working."
      assert (
          abs(h2oframe.min() - np.ndarray.min(python_lists)) < 1e-12
      ), "h2o.H2OFrame.min() command is not working."
  
      h2oMean = h2oframe.mean(skipna=False, axis=0)
      assert_is_type(h2oMean, H2OFrame)
      numpmean = list(np.mean(python_lists, axis=0))
      h2omean = h2oMean.as_data_frame(use_pandas=True, header=False)
      assert pyunit_utils.equal_two_arrays(
          numpmean, h2omean.values.tolist()[0], 1e-12, 1e-6
      ), "h2o.H2OFrame.mean() command is not working."
  
      h2oMedian = h2oframe.median(na_rm=True)
      assert_is_type(h2oMedian, list)
      numpmedian = list(np.median(python_lists, axis=0))
      assert pyunit_utils.equal_two_arrays(
          numpmedian, h2oMedian, 1e-12, 1e-6
      ), "h2o.H2OFrame.median() command is not working."
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.utils.typechecks import assert_is_type
  from random import randrange
  import numpy
  from h2o.frame import H2OFrame
label_desc: Use image node_id 1 for calling a global function with example usage: h2o_H2OFrame_stats() without return types