code: def test_DeterministicPosterior(self):
      for shape, dtype in itertools.product(
          ((3, 2), (2, 3, 1)), (torch.float, torch.double)
      ):
          values = torch.randn(*shape, device=self.device, dtype=dtype)
          p = DeterministicPosterior(values)
          with catch_warnings(record=True) as ws:
              p = DeterministicPosterior(values)
              self.assertTrue(
                  any(
                      "marked for deprecation" in str(w.message)
                      for w in ws
                  )
              )
          self.assertEqual(p.device.type, self.device.type)
          self.assertEqual(p.dtype, dtype)
          self.assertEqual(p._extended_shape(), values.shape)
          with self.assertRaises(NotImplementedError):
              p.base_sample_shape
          self.assertTrue(torch.equal(p.mean, values))
          self.assertTrue(
              torch.equal(p.variance, torch.zeros_like(values))
          )
          # test sampling
          samples = p.rsample()
          self.assertTrue(torch.equal(samples, values.unsqueeze(0)))
          samples = p.rsample(torch.Size([2]))
          self.assertTrue(
              torch.equal(samples, values.expand(2, *values.shape))
          )
  
file_path: botorch/test/posteriors/test_deterministic.py
imports: import itertools
  from warnings import catch_warnings
  import torch
  from botorch.posteriors.deterministic import DeterministicPosterior
  from botorch.utils.testing import BotorchTestCase
label_desc: Use image node_id 1 for calling the TestDeterministicPosterior obj's underlying member method code with example usage: obj.test_DeterministicPosterior() without return types