code: def asy_ttei(gp, anc_data):
      """Top-Two expected improvement."""
      if np.random.random() < 0.5:
          # With probability 1/2, return the EI point
          return asy_ei(gp, anc_data)
      else:
          max_acq_opt_evals = anc_data.max_evals
          anc_data = copy(anc_data)
          anc_data.max_evals = max_acq_opt_evals // 2
          ei_argmax = asy_ei(gp, anc_data)
          # Now return the second argmax
          gp_eval = _get_gp_eval_for_parallel_strategy(
              gp, anc_data, "std"
          )
          return _ttei(gp_eval, anc_data, ei_argmax)
  
imports: from __future__ import division
  from argparse import Namespace
  from copy import copy
  import numpy
  from scipy.stats import norm
  from ..utils.general_utils import solve_lower_triangular
  from ..gp.gp_core import get_post_covar_from_raw_covar
  from ..exd.domains import EuclideanDomain
  from ..exd.exd_utils import maximise_with_method
label_desc: Use image node_id 21 for calling a global function with example usage: asy_ttei(gp, anc_data) and returns: asy_ei, _ttei