code: def test_normal_prior_batch_log_prob(self, cuda=False):
      device = torch.device("cuda") if cuda else torch.device("cpu")
  
      mean = torch.tensor([0.0, 1.0], device=device)
      variance = torch.tensor([1.0, 2.0], device=device)
      prior = NormalPrior(mean, variance)
      dist = Normal(mean, variance)
      t = torch.zeros(2, device=device)
      self.assertTrue(torch.equal(prior.log_prob(t), dist.log_prob(t)))
      t = torch.zeros(2, 2, device=device)
      self.assertTrue(torch.equal(prior.log_prob(t), dist.log_prob(t)))
      with self.assertRaises(RuntimeError):
          prior.log_prob(torch.zeros(3, device=device))
  
      mean = torch.tensor([[0.0, 1.0], [-1.0, 2.0]], device=device)
      variance = torch.tensor([[1.0, 2.0], [0.5, 1.0]], device=device)
      prior = NormalPrior(mean, variance)
      dist = Normal(mean, variance)
      t = torch.zeros(2, device=device)
      self.assertTrue(torch.equal(prior.log_prob(t), dist.log_prob(t)))
      t = torch.zeros(2, 2, device=device)
      self.assertTrue(torch.equal(prior.log_prob(t), dist.log_prob(t)))
      with self.assertRaises(RuntimeError):
          prior.log_prob(torch.zeros(3, device=device))
      with self.assertRaises(RuntimeError):
          prior.log_prob(torch.zeros(2, 3, device=device))
  
file_path: gpytorch/test/priors/test_normal_prior.py
imports: import unittest
  import torch
  from torch.distributions import Normal
  from gpytorch.priors import NormalPrior
  from gpytorch.test.utils import least_used_cuda_device
label_desc: Use image node_id 7 for calling the TestNormalPrior obj's underlying member method code with example usage: obj.test_normal_prior_batch_log_prob(cuda) without return types