code: def test_log_prob(self, cuda=False):
      device = torch.device("cuda") if cuda else torch.device("cpu")
      for dtype in (torch.float, torch.double):
          mean = torch.randn(4, device=device, dtype=dtype)
          var = torch.randn(4, device=device, dtype=dtype).abs_()
          values = torch.randn(4, device=device, dtype=dtype)
  
          res = MultivariateNormal(
              mean, DiagLinearOperator(var)
          ).log_prob(values)
          actual = TMultivariateNormal(
              mean, torch.eye(4, device=device, dtype=dtype) * var
          ).log_prob(values)
          self.assertLess((res - actual).div(res).abs().item(), 1e-2)
  
          mean = torch.randn(3, 4, device=device, dtype=dtype)
          var = torch.randn(3, 4, device=device, dtype=dtype).abs_()
          values = torch.randn(3, 4, device=device, dtype=dtype)
  
          res = MultivariateNormal(
              mean, DiagLinearOperator(var)
          ).log_prob(values)
          actual = TMultivariateNormal(
              mean,
              var.unsqueeze(-1)
              * torch.eye(4, device=device, dtype=dtype).repeat(
                  3, 1, 1
              ),
          ).log_prob(values)
          self.assertLess((res - actual).div(res).abs().norm(), 1e-2)
  
imports: import math
  import unittest
  import torch
  from linear_operator import to_linear_operator
  from linear_operator.operators import DenseLinearOperator, DiagLinearOperator, LinearOperator, RootLinearOperator
  from torch.distributions import MultivariateNormal
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.test.base_test_case import BaseTestCase
  from gpytorch.test.utils import least_used_cuda_device
label_desc: Use image node_id 13 for calling the TestMultivariateNormal obj's underlying member method code with example usage: obj.test_log_prob(cuda) without return types