code: def asy_ts(gp, anc_data):
      """Returns a recommendation via TS in the asyuential setting."""
      anc_data = copy(anc_data)
      # Always use a random optimiser with a vectorised sampler for TS.
      if anc_data.acq_opt_method != "rand":
          anc_data.acq_opt_method = "rand"
          anc_data.max_evals = 4 * anc_data.max_evals
      gp_sample = get_gp_sampler_for_parallel_strategy(gp, anc_data)
      return maximise_acquisition(gp_sample, anc_data, vectorised=True)
  
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 5 for calling a global function with example usage: asy_ts(gp, anc_data) and returns: maximise_acquisition