code: def test_posterior_list(self):
      for dtype, use_deterministic in product(
          (torch.float, torch.double), (False, True)
      ):
          shape = torch.Size([3])
          make_posterior = (
              self._make_deterministic_posterior
              if use_deterministic
              else self._make_gpytorch_posterior
          )
          p_1 = make_posterior(shape, dtype)
          p_2 = make_posterior(shape, dtype)
          p = PosteriorList(p_1, p_2)
          with self.assertWarnsRegex(
              DeprecationWarning, "The `event_shape` attribute"
          ):
              self.assertEqual(p.event_shape, p._extended_shape())
          with self.assertRaisesRegex(
              NotImplementedError, "base_sample_shape"
          ):
              p.base_sample_shape
          self.assertEqual(p._extended_shape(), shape + torch.Size([2]))
          self.assertEqual(p.device.type, self.device.type)
          self.assertEqual(p.dtype, dtype)
          self.assertTrue(
              torch.equal(
                  p.mean, torch.cat([p_1.mean, p_2.mean], dim=-1)
              )
          )
          self.assertTrue(
              torch.equal(
                  p.variance,
                  torch.cat([p_1.variance, p_2.variance], dim=-1),
              )
          )
          # Test sampling.
          sample_shape = torch.Size([4])
          samples = p.rsample(sample_shape=sample_shape)
          self.assertEqual(samples.shape, torch.Size([4, 3, 2]))
  
imports: from itertools import product
  import torch
  from botorch.posteriors import GPyTorchPosterior, Posterior, PosteriorList
  from botorch.posteriors.deterministic import DeterministicPosterior
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.distributions import MultivariateNormal
  from linear_operator.operators import to_linear_operator
label_desc: Use image node_id 3 for calling the TestPosteriorList obj's underlying member method code with example usage: obj.test_posterior_list() without return types