docstr: Sample the function surface.
  
  There are 2 modes, if ``infty_cons_sampl`` is True then the sampled
  points that are generated outside the feasible domain will be
  assigned an ``inf`` value in accordance with SHGO rules.
  This guarantees convergence and usually requires less objective
  function evaluations at the computational costs of more Delaunay
  triangulation points.
  
  If ``infty_cons_sampl`` is False, then the infeasible points are
  discarded and only a subspace of the sampled points are used. This
  comes at the cost of the loss of guaranteed convergence and usually
  requires more objective function evaluations.
code: def sampled_surface(self, infty_cons_sampl=False):
      """
      Sample the function surface.
  
      There are 2 modes, if ``infty_cons_sampl`` is True then the sampled
      points that are generated outside the feasible domain will be
      assigned an ``inf`` value in accordance with SHGO rules.
      This guarantees convergence and usually requires less objective
      function evaluations at the computational costs of more Delaunay
      triangulation points.
  
      If ``infty_cons_sampl`` is False, then the infeasible points are
      discarded and only a subspace of the sampled points are used. This
      comes at the cost of the loss of guaranteed convergence and usually
      requires more objective function evaluations.
      """
      # Generate sampling points
      if self.disp:
          logging.info("Generating sampling points")
      self.sampling(self.nc, self.dim)
      if len(self.LMC.xl_maps) > 0:
          self.C = np.vstack((self.C, np.array(self.LMC.xl_maps)))
      if not infty_cons_sampl:
          # Find subspace of feasible points
          if self.g_cons is not None:
              self.sampling_subspace()
  
      # Sort remaining samples
      self.sorted_samples()
  
      # Find objective function references
      self.n_sampled = self.nc
  
imports: from collections import namedtuple
  import time
  import logging
  import warnings
  import sys
  import numpy
  from scipy import spatial
  from scipy.optimize import OptimizeResult, minimize, Bounds
  from scipy.optimize._optimize import MemoizeJac
  from scipy.optimize._constraints import new_bounds_to_old
  from scipy.optimize._minimize import standardize_constraints
  from scipy._lib._util import _FunctionWrapper
  from scipy.optimize._shgo_lib._complex import Complex
label_desc: Use image node_id 28 for calling the SHGO obj's underlying member method code with example usage: obj.sampled_surface(infty_cons_sampl) without return types