code: def test_lkj_prior_sample(self):
      prior = LKJCovariancePrior(
          2, 0.5, sd_prior=SmoothedBoxPrior(exp(-1), exp(1))
      )
      random_samples = prior.sample(torch.Size((6,)))
      # need to check that these are positive semi-sefinite
      min_eval = torch.linalg.eigh(random_samples)[0].min()
      self.assertTrue(min_eval >= 0)
      # and that they are symmetric
      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((6, 2, 2)))
  
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 9 for calling the TestLKJCovariancePrior obj's underlying member method code with example usage: obj.test_lkj_prior_sample() without return types