code: def test_multivariate_normal_correlated_samples(self, cuda=False):
      device = torch.device("cuda") if cuda else torch.device("cpu")
      for dtype in (torch.float, torch.double):
          mean = torch.tensor([0, 1, 2], device=device, dtype=dtype)
          covmat = torch.diag(
              torch.tensor([1, 0.75, 1.5], device=device, dtype=dtype)
          )
          mvn = MultivariateNormal(
              mean=mean, covariance_matrix=DenseLinearOperator(covmat)
          )
          base_samples = mvn.get_base_samples(torch.Size([3, 4]))
          self.assertTrue(
              mvn.sample(base_samples=base_samples).shape
              == torch.Size([3, 4, 3])
          )
          base_samples = mvn.get_base_samples()
          self.assertTrue(
              mvn.sample(base_samples=base_samples).shape
              == torch.Size([3])
          )
  
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 9 for calling the TestMultivariateNormal obj's underlying member method code with example usage: obj.test_multivariate_normal_correlated_samples(cuda) without return types