code: def _make_gpytorch_posterior(self, shape, dtype):
      mean = torch.rand(*shape, dtype=dtype, device=self.device)
      variance = 1 + torch.rand(*shape, dtype=dtype, device=self.device)
      covar = torch.diag_embed(variance)
      mvn = MultivariateNormal(mean, to_linear_operator(covar))
      return GPyTorchPosterior(distribution=mvn)
  
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 1 for calling the TestPosteriorList obj's underlying member method code with example usage: obj._make_gpytorch_posterior(shape, dtype) and returns: GPyTorchPosterior