code: def mo_tch_asy_ucb(gps, anc_data):
      """Returns a recommendation via UCB with Linear Scalarization in the
      asynchronous setting for MOO"""
      beta_th = _get_ucb_beta_th(anc_data.domain.dim, anc_data.t)
  
      def acquisition(x):
          """Computes the GP-UCB acquisition."""
          ret = np.asarray([np.inf for _ in range(len(x))])
          for gp, weight, ref in zip(
              gps, anc_data.obj_weights, anc_data.reference_point
          ):
              mu, sigma2 = gp.eval(x, uncert_form="std")
              ucb = mu + beta_th * np.sqrt(sigma2) - ref
              ret = np.minimum(ret, ucb / weight)
          return ret
  
      return maximise_acquisition(acquisition, anc_data)
  
file_path: dragonfly/dragonfly/opt/multiobjective_gpb_acquisitions.py
imports: from __future__ import division
  from argparse import Namespace
  from copy import copy
  import numpy
  from .gpb_acquisitions import maximise_acquisition
  from .gpb_acquisitions import get_gp_sampler_for_parallel_strategy
label_desc: Use image node_id 5 for calling a global function with example usage: mo_tch_asy_ucb(gps, anc_data) and returns: maximise_acquisition, ret