code: def compute_max_ei_point(
      points: numpy.ndarray | Sequence[numpy.ndarray],
      below_likelis: Sequence[float] | numpy.ndarray,
      above_likelis: Sequence[float] | numpy.ndarray,
  ) -> numpy.ndarray:
      """Compute ei among points based on their log likelihood and return the point with max ei.
  
      :param points: list of point with real values.
      :param below_likelis: list of log likelihood for each point in the good GMM.
      :param above_likelis: list of log likelihood for each point in the bad GMM.
      """
      max_ei = -numpy.inf
      point_index = 0
      for i, (lik_b, lik_a) in enumerate(
          zip(below_likelis, above_likelis)
      ):
          ei = lik_b - lik_a
          if ei > max_ei:
              max_ei = ei
              point_index = i
      return points[point_index]
  
imports: from __future__ import annotations
  import logging
  from typing import Any, Callable, ClassVar, Iterable, Sequence, TypeVar
  import numpy
  from scipy.stats import norm
  from scipy.stats.distributions import rv_frozen
  from orion.algo.base import BaseAlgorithm
  from orion.algo.base.parallel_strategy import ParallelStrategy, strategy_factory
  from orion.algo.space import Integer, Real, Space
  from orion.core.utils import format_trials
  from orion.core.utils.flatten import flatten
  from orion.core.worker.trial import Trial
label_desc: Use image node_id 1 for calling a global function with example usage: compute_max_ei_point(points, below_likelis, above_likelis) and returns: points