code: def relativize(
      m_t: float, sem_t: float, m_c: float, sem_c: float
  ) -> List[float]:
      r_hat = (m_t - m_c) / abs(m_c) - sem_c**2 * m_t / abs(m_c) ** 3
      variance = (sem_t**2 + (m_t / m_c * sem_c) ** 2) / m_c**2
      return [r_hat, math.sqrt(variance)]
  
imports: import math
  from collections import Counter
  from logging import Logger
  from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
  import numpy
  from ax.core.generator_run import GeneratorRun
  from ax.core.observation import Observation, ObservationFeatures
  from ax.core.parameter import ChoiceParameter, FixedParameter, Parameter, ParameterType, RangeParameter
  from ax.core.types import TParameterization
  from ax.modelbridge.base import ModelBridge
  from ax.modelbridge.prediction_utils import _compute_scalarized_outcome, predict_at_point
  from ax.modelbridge.transforms.ivw import IVW
  from ax.plot.base import DECIMALS, PlotData, PlotInSampleArm, PlotOutOfSampleArm, Z
  from ax.utils.common.logger import get_logger
  from ax.utils.common.typeutils import not_none
label_desc: Use image node_id 19 for calling a global function with example usage: relativize(m_t, sem_t, m_c, sem_c) and returns: list