code: def test_multitask_multivariate_normal_batch(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)
  
          # interleaved
          covmat = variance.view(2, 1, -1) * torch.eye(
              6, device=device, dtype=dtype
          )
          mtmvn = MultitaskMultivariateNormal(
              mean=mean, covariance_matrix=covmat
          )
          self.assertTrue(torch.equal(mtmvn.mean, mean))
          self.assertTrue(torch.allclose(mtmvn.variance, variance))
          self.assertTrue(
              torch.allclose(mtmvn.scale_tril, covmat.sqrt())
          )
          self.assertTrue(mtmvn.event_shape == torch.Size([3, 2]))
          self.assertTrue(mtmvn.batch_shape == torch.Size([2]))
          mvn_plus1 = mtmvn + 1
          self.assertTrue(torch.equal(mvn_plus1.mean, mtmvn.mean + 1))
          self.assertTrue(
              torch.equal(
                  mvn_plus1.covariance_matrix, mtmvn.covariance_matrix
              )
          )
          mvn_times2 = mtmvn * 2
          self.assertTrue(torch.equal(mvn_times2.mean, mtmvn.mean * 2))
          self.assertTrue(
              torch.equal(
                  mvn_times2.covariance_matrix,
                  mtmvn.covariance_matrix * 4,
              )
          )
          mvn_divby2 = mtmvn / 2
          self.assertTrue(torch.equal(mvn_divby2.mean, mtmvn.mean / 2))
          self.assertTrue(
              torch.equal(
                  mvn_divby2.covariance_matrix,
                  mtmvn.covariance_matrix / 4,
              )
          )
          self.assertTrue(
              torch.allclose(
                  mtmvn.entropy(),
                  11.80326 * torch.ones(2, device=device, dtype=dtype),
              )
          )
          logprob = mtmvn.log_prob(
              torch.zeros(2, 3, 2, device=device, dtype=dtype)
          )
          logprob_expected = -14.52826 * torch.ones(
              2, device=device, dtype=dtype
          )
          self.assertTrue(torch.allclose(logprob, logprob_expected))
          logprob = mtmvn.log_prob(
              torch.zeros(3, 2, 3, 2, device=device, dtype=dtype)
          )
          logprob_expected = -14.52826 * torch.ones(
              3, 2, device=device, dtype=dtype
          )
          self.assertTrue(torch.allclose(logprob, logprob_expected))
          conf_lower, conf_upper = mtmvn.confidence_region()
          self.assertTrue(
              torch.allclose(conf_lower, mtmvn.mean - 2 * mtmvn.stddev)
          )
          self.assertTrue(
              torch.allclose(conf_upper, mtmvn.mean + 2 * mtmvn.stddev)
          )
          self.assertTrue(mtmvn.sample().shape == torch.Size([2, 3, 2]))
          self.assertTrue(
              mtmvn.sample(torch.Size([3])).shape
              == torch.Size([3, 2, 3, 2])
          )
          self.assertTrue(
              mtmvn.sample(torch.Size([3, 4])).shape
              == torch.Size([3, 4, 2, 3, 2])
          )
  
          # non-interleaved
          covmat = variance.transpose(-1, -2).reshape(
              2, 1, -1
          ) * torch.eye(6, device=device, dtype=dtype)
          mtmvn = MultitaskMultivariateNormal(
              mean=mean, covariance_matrix=covmat, interleaved=False
          )
          self.assertTrue(torch.equal(mtmvn.mean, mean))
          self.assertTrue(torch.allclose(mtmvn.variance, variance))
          self.assertTrue(
              torch.allclose(mtmvn.scale_tril, covmat.sqrt())
          )
          self.assertTrue(mtmvn.event_shape == torch.Size([3, 2]))
          self.assertTrue(mtmvn.batch_shape == torch.Size([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 7 for calling the TestMultiTaskMultivariateNormal obj's underlying member method code with example usage: obj.test_multitask_multivariate_normal_batch(cuda) without return types