code: def _relativize_values(
      means: List[float],
      sq_mean: float,
      sems: List[float],
      sq_sem: float,
  ) -> Tuple[List[float], List[float]]:
      """
      Relativize values, using delta method if SEMs provided, or just by relativizing
      means if not. Relativization is as percent.
      """
      if np.isnan(sq_sem) or np.isnan(sems).any():
          # Just relativize means
          means = [(mu / sq_mean - 1) * 100 for mu in means]
      else:
          # Use delta method
          means_arr, sems_arr = relativize(
              means_t=np.array(means),
              sems_t=np.array(sems),
              mean_c=sq_mean,
              sem_c=sq_sem,
              as_percent=True,
          )
          means, sems = list(means), list(sems)
      return means, sems
  
imports: import copy
  from copy import deepcopy
  from itertools import combinations
  from logging import Logger
  from typing import cast, Dict, List, NamedTuple, Optional, Tuple, Union
  import numpy
  import torch
  from ax.core.batch_trial import BatchTrial
  from ax.core.data import Data
  from ax.core.experiment import Experiment
  from ax.core.metric import Metric
  from ax.core.objective import ScalarizedObjective
  from ax.core.observation import ObservationFeatures
  from ax.core.optimization_config import MultiObjectiveOptimizationConfig, OptimizationConfig
  from ax.core.outcome_constraint import ComparisonOp, ObjectiveThreshold, OutcomeConstraint
  from ax.core.search_space import RobustSearchSpace, SearchSpace
  from ax.core.types import TParameterization
  from ax.exceptions.core import AxError, UnsupportedError, UserInputError
  from ax.modelbridge.modelbridge_utils import _get_modelbridge_training_data, get_pareto_frontier_and_configs, observed_pareto_frontier
  from ax.modelbridge.registry import Models
  from ax.modelbridge.torch import TorchModelBridge
  from ax.modelbridge.transforms.search_space_to_float import SearchSpaceToFloat
  from ax.models.torch.posterior_mean import get_PosteriorMean
  from ax.models.torch_base import TorchModel
  from ax.utils.common.logger import get_logger
  from ax.utils.stats.statstools import relativize
  from botorch.utils.multi_objective import is_non_dominated
  from botorch.utils.multi_objective.hypervolume import infer_reference_point
label_desc: Use image node_id 3 for calling a global function with example usage: _relativize_values(means, sq_mean, sems, sq_sem) and returns: means,  sems