docstr: Generates uniform sampling points in a hypercube and scales the points
  to the bound limits.
code: def sampling_custom(self, n, dim):
      """
      Generates uniform sampling points in a hypercube and scales the points
      to the bound limits.
      """
      # Generate sampling points.
      # Generate uniform sample points in [0, 1]^m \subset R^m
      if self.n_sampled == 0:
          self.C = self.sampling_function(n, dim)
      else:
          self.C = self.sampling_function(n, dim)
      # Distribute over bounds
      for i in range(len(self.bounds)):
          self.C[:, i] = (
              self.C[:, i] * (self.bounds[i][1] - self.bounds[i][0])
              + self.bounds[i][0]
          )
      return self.C
  
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 29 for calling the SHGO obj's underlying member method code with example usage: obj.sampling_custom(n, dim) and returns: self