code: def test_covariance_matrix(self):
      for d in self.distributions:
          cov = d.covariance_matrix
          self.assertTrue(isinstance(cov, Tensor))
  
          # testing for symmetry
          self.assertAllClose(cov, cov.mT)
  
          # testing for positive-definiteness
          ispd = False
          try:
              torch.linalg.cholesky(cov)
              ispd = True
          except RuntimeError:
              pass
          self.assertTrue(ispd)
  
          # checking that linear operator to tensor conversion
          # leads to same covariance matrix
          xcov_linop = DenseLinearOperator(d.cross_covariance_matrix)
          usn_linop = UnifiedSkewNormal(
              trunc=d.trunc,
              gauss=d.gauss,
              cross_covariance_matrix=xcov_linop,
          )
          cov_linop = usn_linop.covariance_matrix
          self.assertTrue(isinstance(cov_linop, Tensor))
          self.assertAllClose(cov, cov_linop)
  
imports: from __future__ import annotations
  from copy import deepcopy
  from itertools import count
  from typing import Any, Dict, Optional, Sequence, Tuple
  import torch
  from botorch.utils.probability.mvnxpb import MVNXPB
  from botorch.utils.probability.truncated_multivariate_normal import TruncatedMultivariateNormal
  from botorch.utils.probability.unified_skew_normal import UnifiedSkewNormal
  from botorch.utils.testing import BotorchTestCase
  from linear_operator.operators import DenseLinearOperator
  from torch import Tensor
  from torch.distributions import MultivariateNormal
  from torch.special import ndtri
label_desc: Use image node_id 9 for calling the TestUnifiedSkewNormal obj's underlying member method code with example usage: obj.test_covariance_matrix() without return types