code: def test_sparse_to_dense_constraints(self):
      tkwargs = {"device": self.device}
      for dtype in (torch.float, torch.double):
          tkwargs["dtype"] = dtype
          inequality_constraints = [
              (
                  torch.tensor([3], **tkwargs),
                  torch.tensor([4], **tkwargs),
                  3,
              )
          ]
          (A, b) = sparse_to_dense_constraints(
              d=4, constraints=inequality_constraints
          )
          expected_A = torch.tensor([[0.0, 0.0, 0.0, 4.0]], **tkwargs)
          self.assertTrue(torch.equal(A, expected_A))
          expected_b = torch.tensor([[3.0]], **tkwargs)
          self.assertTrue(torch.equal(b, expected_b))
  
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 6 for calling the TestSampleUtils obj's underlying member method code with example usage: obj.test_sparse_to_dense_constraints() without return types