code: def test_TransformOptimizationConfigMOO(self) -> None:
      m1 = Metric(name="m1", lower_is_better=False)
      m2 = Metric(name="m2", lower_is_better=True)
      mo = MultiObjective(
          objectives=[
              Objective(metric=m1, minimize=False),
              Objective(metric=m2, minimize=True),
          ],
      )
      objective_thresholds = [
          ObjectiveThreshold(metric=m1, bound=1.234, relative=False),
          ObjectiveThreshold(metric=m2, bound=3.456, relative=False),
      ]
      oc = MultiObjectiveOptimizationConfig(
          objective=mo,
          objective_thresholds=objective_thresholds,
      )
      tf = LogY(
          search_space=None,
          observations=self.observations,
          config={"metrics": ["m1"]},  # pyre-ignore
      )
      oc_tf = tf.transform_optimization_config(deepcopy(oc), None, None)
      oc.objective_thresholds[0].bound = math.log(1.234)
      self.assertEqual(oc_tf, oc)
  
imports: import math
  from copy import deepcopy
  import numpy
  from ax.core.metric import Metric
  from ax.core.objective import MultiObjective, Objective
  from ax.core.observation import Observation, ObservationData, ObservationFeatures
  from ax.core.optimization_config import MultiObjectiveOptimizationConfig, OptimizationConfig
  from ax.core.outcome_constraint import ObjectiveThreshold, OutcomeConstraint
  from ax.core.types import ComparisonOp
  from ax.modelbridge.transforms.log_y import lognorm_to_norm, LogY, match_ci_width, norm_to_lognorm
  from ax.utils.common.testutils import TestCase
label_desc: Use image node_id 6 for calling the LogYTransformTest obj's underlying member method code with example usage: obj.test_TransformOptimizationConfigMOO() without return types