code: def countmatches_check():
      # Connect to a pre-existing cluster
  
      frame = h2o.import_file(
          path=pyunit_utils.locate("smalldata/iris/iris.csv"),
          col_types=[
              "numeric",
              "numeric",
              "numeric",
              "numeric",
              "string",
          ],
      )
  
      # single column (frame)
      result = frame["C5"].countmatches("o")
      assert result.nrow == 150 and result.ncol == 1
      assert (
          result[0, 0] == 1
          and result[50, 0] == 2
          and result[100, 0] == 0
      ), "Expected 1, 2, 0 but got {0}, {1}, and " "{2}".format(
          result[0, 0], result[50, 0], result[100, 0]
      )
  
      # single column (vec)
      vec = frame["C5"]
      result = vec.countmatches("ic")
      assert result.nrow == 150 and result.ncol == 1
      assert (
          result[0, 0] == 0
          and result[50, 0] == 1
          and result[100, 0] == 1
      ), "Expected 0, 1, 1 but got {0}, {1}, and " "{2}".format(
          result[0, 0], result[50, 0], result[100, 0]
      )
  
      # array of targets
      vec = frame["C5"]
      result = vec.countmatches(["ic", "ri", "ca"])
      assert result.nrow == 150 and result.ncol == 1
      assert (
          result[0, 0] == 1
          and result[50, 0] == 2
          and result[100, 0] == 3
      ), "Expected 1, 2, 3 but got {0}, {1}, and " "{2}".format(
          result[0, 0], result[50, 0], result[100, 0]
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: countmatches_check() without return types