docstr: Find subspace of feasible points from g_func definition
code: def sampling_subspace(self):
      """Find subspace of feasible points from g_func definition"""
      # Subspace of feasible points.
      for ind, g in enumerate(self.g_cons):
          # C.shape = (Z, dim) where Z is the number of sampling points to
          # evaluate and dim is the dimensionality of the problem.
          # the constraint function may not be vectorised so have to step
          # through each sampling point sequentially.
          feasible = np.array(
              [
                  np.all(g(x_C, *self.g_args[ind]) >= 0.0)
                  for x_C in self.C
              ],
              dtype=bool,
          )
          self.C = self.C[feasible]
  
          if self.C.size == 0:
              self.res.message = (
                  "No sampling point found within the "
                  + "feasible set. Increasing sampling "
                  + "size."
              )
              # sampling correctly for both 1-D and >1-D cases
              if self.disp:
                  logging.info(self.res.message)
  
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 30 for calling the SHGO obj's underlying member method code with example usage: obj.sampling_subspace() without return types