code: def test_mltuning_values(
      cls_params: tp.Dict[str, tp.Any],
      func_params: tp.Dict[str, tp.Any],
      expected: float,
  ) -> None:
      np.random.seed(12)
      func = mlfunctionlib.MLTuning(**cls_params)
      if any(
          params.get("regressor", "") == "mlp"
          for params in (func_params, cls_params)
      ):
          # make it way faster
          func.num_data = 12
          func._cross_val_num = 2
      outputs = [func(**func_params) for _ in range(2)]
      assert (
          outputs[0] == outputs[1]
      )  # function is deterministic once initialized
      np.testing.assert_almost_equal(outputs[0], expected, decimal=8)
      func.evaluation_function(func.parametrization)
  
imports: import typing
  import numpy
  from nevergrad.common import testing
  from .None import mlfunctionlib
label_desc: Use image node_id 1 for calling a global function with example usage: test_mltuning_values(cls_params, func_params, expected) without return types