code: def test_sample_polytope_unbounded(self):
      A = torch.tensor(
          [
              [-1.0, 0.0, 0.0],
              [0.0, -1.0, 0.0],
              [0.0, 0.0, -1.0],
              [0.0, 4.0, 1.0],
          ],
          device=self.device,
      )
      b = torch.tensor([[0.0], [0.0], [0.0], [1.0]], device=self.device)
      with self.assertRaises(ValueError):
          with warnings.catch_warnings():
              warnings.simplefilter("ignore")
              self.sampler_class(
                  inequality_constraints=(A, b),
                  interior_point=self.x0,
                  **self.sampler_kwargs,
              )
  
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 1 for calling the TestDelaunayPolytopeSampler obj's underlying member method code with example usage: obj.test_sample_polytope_unbounded() without return types