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, 3, device=device, dtype=dtype)
          var = torch.randn(12, device=device, dtype=dtype).abs_()
          values = mean + 0.5
          diffs = (values - mean).view(-1)
  
          res = MultitaskMultivariateNormal(
              mean, DiagLinearOperator(var)
          ).log_prob(values)
          actual = -0.5 * (
              math.log(math.pi * 2) * 12
              + var.log().sum()
              + (diffs / var * diffs).sum()
          )
          self.assertLess((res - actual).div(res).abs().item(), 1e-2)
  
          mean = torch.randn(3, 4, 3, device=device, dtype=dtype)
          var = torch.randn(3, 12, device=device, dtype=dtype).abs_()
          values = mean + 0.5
          diffs = (values - mean).view(3, -1)
  
          res = MultitaskMultivariateNormal(
              mean, DiagLinearOperator(var)
          ).log_prob(values)
          actual = -0.5 * (
              math.log(math.pi * 2) * 12
              + var.log().sum(-1)
              + (diffs / var * diffs).sum(-1)
          )
          self.assertLess((res - actual).div(res).abs().norm(), 1e-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 13 for calling the TestMultiTaskMultivariateNormal obj's underlying member method code with example usage: obj.test_log_prob(cuda) without return types