code: def test_sample_polytope(self):
      for dtype in (torch.float, torch.double):
          A = self.A.to(dtype)
          b = self.b.to(dtype)
          x0 = self.x0.to(dtype)
          bounds = self.bounds.to(dtype)
          for interior_point in [x0, None]:
              sampler = self.sampler_class(
                  inequality_constraints=(A, b),
                  bounds=bounds,
                  interior_point=interior_point,
                  **self.sampler_kwargs,
              )
              samples = sampler.draw(n=10, seed=1)
              self.assertEqual(
                  ((A @ samples.t() - b) > 0).sum().item(), 0
              )
              self.assertTrue((samples <= bounds[1]).all())
              self.assertTrue((samples >= bounds[0]).all())
              # make sure we can draw mulitple samples
              more_samples = sampler.draw(n=5)
              self.assertEqual(
                  ((A @ more_samples.t() - b) > 0).sum().item(), 0
              )
              self.assertTrue((more_samples <= bounds[1]).all())
              self.assertTrue((more_samples >= bounds[0]).all())
  
imports: from __future__ import annotations
  import itertools
  import warnings
  from typing import Any, Dict, Type
  from unittest import mock
  import numpy
  import torch
  from botorch.exceptions.errors import BotorchError
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.sampling.pathwise import draw_matheron_paths
  from botorch.utils.sampling import _convert_bounds_to_inequality_constraints, batched_multinomial, DelaunayPolytopeSampler, draw_sobol_samples, find_interior_point, get_polytope_samples, HitAndRunPolytopeSampler, manual_seed, normalize_linear_constraints, optimize_posterior_samples, PolytopeSampler, sample_hypersphere, sample_simplex, sparse_to_dense_constraints
  from botorch.utils.testing import BotorchTestCase
label_desc: Use image node_id 2 for calling the PolytopeSamplerTestBase obj's underlying member method code with example usage: obj.test_sample_polytope() without return types