code: def test_EnsemblePosterior_as_Deterministic(self):
      for shape, dtype in itertools.product(
          ((1, 3, 2), (2, 1, 3, 2)), (torch.float, torch.double)
      ):
          values = torch.randn(*shape, device=self.device, dtype=dtype)
          p = EnsemblePosterior(values)
          self.assertEqual(p.ensemble_size, 1)
          self.assertEqual(p.device.type, self.device.type)
          self.assertEqual(p.dtype, dtype)
          self.assertEqual(
              p._extended_shape(torch.Size((1,))),
              torch.Size((1, 3, 2))
              if len(shape) == 3
              else torch.Size((1, 2, 3, 2)),
          )
          self.assertEqual(p.weights, torch.ones(1))
          with self.assertRaises(NotImplementedError):
              p.base_sample_shape
          self.assertTrue(torch.equal(p.mean, values.squeeze(-3)))
          self.assertTrue(
              torch.equal(
                  p.variance, torch.zeros_like(values.squeeze(-3))
              )
          )
          # test sampling
          samples = p.rsample()
          self.assertTrue(
              torch.equal(samples, values.squeeze(-3).unsqueeze(0))
          )
          samples = p.rsample(torch.Size([2]))
          self.assertEqual(
              samples.shape, p._extended_shape(torch.Size([2]))
          )
  
file_path: botorch/test/posteriors/test_ensemble.py
imports: import itertools
  import torch
  from botorch.posteriors.ensemble import EnsemblePosterior
  from botorch.utils.testing import BotorchTestCase
label_desc: Use image node_id 2 for calling the TestEnsemblePosterior obj's underlying member method code with example usage: obj.test_EnsemblePosterior_as_Deterministic() without return types