code: def test_multivariate_normal_batch_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, 3], [4, 5]], dtype=dtype, device=device
          ).repeat(2, 1, 1)
          variance = torch.tensor(
              [[1, 2], [3, 4], [5, 6]], dtype=dtype, device=device
          ).repeat(2, 1, 1)
          covmat = variance.view(2, 1, -1) * torch.eye(
              6, device=device, dtype=dtype
          )
          mtmvn = MultitaskMultivariateNormal(
              mean=mean, covariance_matrix=covmat
          )
          base_samples = mtmvn.get_base_samples(torch.Size((3, 4)))
          self.assertTrue(
              mtmvn.sample(base_samples=base_samples).shape
              == torch.Size([3, 4, 2, 3, 2])
          )
          base_samples = mtmvn.get_base_samples()
          self.assertTrue(
              mtmvn.sample(base_samples=base_samples).shape
              == torch.Size([2, 3, 2])
          )
  
imports: import math
  import os
  import random
  import unittest
  import torch
  from linear_operator.operators import DiagLinearOperator, KroneckerProductLinearOperator
  from gpytorch.distributions import MultitaskMultivariateNormal, MultivariateNormal
  from gpytorch.test.base_test_case import BaseTestCase
  from gpytorch.test.utils import least_used_cuda_device
label_desc: Use image node_id 11 for calling the TestMultiTaskMultivariateNormal obj's underlying member method code with example usage: obj.test_multivariate_normal_batch_correlated_samples(cuda) without return types