code: def trim_check():
      # Connect to a pre-existing cluster
      # testing on a string column
      frame = h2o.import_file(
          path=pyunit_utils.locate("smalldata/junit/cars_trim.csv"),
          col_types=[
              "string",
              "numeric",
              "numeric",
              "numeric",
              "numeric",
              "numeric",
              "numeric",
              "numeric",
          ],
      )
  
      # single column (frame)
      trimmed_frame = frame["name"].trim()
      assert (
          trimmed_frame[0, 0] == "AMC Ambassador Brougham"
      ), "Expected 'AMC Ambassador Brougham', but got {}".format(
          trimmed_frame[0, 0]
      )
      assert (
          trimmed_frame[1, 0] == "AMC Ambassador DPL"
      ), "Expected 'AMC Ambassador DPL', but got {}".format(
          trimmed_frame[1, 0]
      )
      assert (
          trimmed_frame[2, 0] == "AMC Ambassador SST"
      ), "Expected 'AMC Ambassador SST', but got {}".format(
          trimmed_frame[2, 0]
      )
  
      # single column (vec)
      vec = frame["name"]
      trimmed_vec = vec.trim()
      assert (
          trimmed_vec[0, 0] == "AMC Ambassador Brougham"
      ), "Expected 'AMC Ambassador Brougham', but got {}".format(
          trimmed_frame[0, 0]
      )
      assert (
          trimmed_vec[1, 0] == "AMC Ambassador DPL"
      ), "Expected 'AMC Ambassador DPL', but got {}".format(
          trimmed_frame[1, 0]
      )
      assert (
          trimmed_vec[2, 0] == "AMC Ambassador SST"
      ), "Expected 'AMC Ambassador SST', but got {}".format(
          trimmed_frame[2, 0]
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: trim_check() without return types