code: def test_lkj_prior_sample(self, seed=0):
      torch.random.manual_seed(seed)
  
      prior = LKJPrior(n=5, eta=0.5)
      random_samples = prior.sample(torch.Size((8,)))
      self.assertTrue(_is_valid_correlation_matrix(random_samples))
  
      max_non_symm = (
          (random_samples - random_samples.transpose(-1, -2))
          .abs()
          .max()
      )
      self.assertLess(max_non_symm, 1e-4)
  
      self.assertEqual(random_samples.shape, torch.Size((8, 5, 5)))
  
imports: import unittest
  from math import exp
  import torch
  from torch.distributions import LKJCholesky
  from gpytorch.priors import LKJCholeskyFactorPrior, LKJCovariancePrior, LKJPrior, SmoothedBoxPrior
  from gpytorch.priors.lkj_prior import _is_valid_correlation_matrix, _is_valid_correlation_matrix_cholesky_factor
  from gpytorch.test.utils import approx_equal, least_used_cuda_device
label_desc: Use image node_id 7 for calling the TestLKJPrior obj's underlying member method code with example usage: obj.test_lkj_prior_sample(seed) without return types