code: def h2o_H2OFrame_columns_by_type():
      """
      Python API test: h2o.frame.H2OFrame.columns_by_type(coltype='numeric')
  
      Copied from pyunit_colnames_by_type.py
      """
      fr = h2o.import_file(
          pyunit_utils.locate("smalldata/jira/filter_type.csv")
      )
  
      # Positive case. Look for all coltypes
      num_type = fr.columns_by_type()  # numeric by default
      cat_type = fr.columns_by_type(coltype="categorical")
      str_type = fr.columns_by_type(coltype="string")
      time_type = fr.columns_by_type(coltype="time")
      uuid_type = fr.columns_by_type(coltype="uuid")
      bad_type = fr.columns_by_type(coltype="bad")
  
      assert_is_type(num_type, list)  # check for correct return type
      assert_is_type(cat_type, list)
      assert_is_type(str_type, list)
      assert_is_type(time_type, list)
      assert_is_type(uuid_type, list)
      assert_is_type(bad_type, list)
  
      # check for correct grouping
      assert (
          2.0 in bad_type
      ), "h2o.H2OFrame.columns_by_type command is not working."
      assert (0.0 in num_type) and (
          2.0 in num_type
      ), "h2o.H2OFrame.columns_by_type command is not working."
      assert (1.0 in str_type) and (
          4.0 in str_type
      ), "h2o.H2OFrame.columns_by_type command is not working."
      assert (
          3.0 in time_type
      ), "h2o.H2OFrame.columns_by_type command is not working."
      assert (
          5.0 in uuid_type
      ), "h2o.H2OFrame.columns_by_type command is not working."
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.utils.typechecks import assert_is_type
label_desc: Use image node_id 1 for calling a global function with example usage: h2o_H2OFrame_columns_by_type() without return types