code: def test_extract_batch_covar(self):
      tkwargs = {"device": self.device}
      for dtype in (torch.float, torch.double):
          tkwargs["dtype"] = dtype
          base_covar = torch.tensor(
              [[1.0, 0.6, 0.9], [0.6, 1.0, 0.5], [0.9, 0.5, 1.0]],
              **tkwargs,
          )
          lazy_covar = to_linear_operator(
              torch.stack([base_covar, base_covar * 2], dim=0)
          )
          block_diag_covar = BlockDiagLinearOperator(lazy_covar)
          mt_mvn = MultitaskMultivariateNormal(
              torch.zeros(3, 2, **tkwargs), block_diag_covar
          )
          batch_covar = extract_batch_covar(mt_mvn=mt_mvn)
          self.assertTrue(
              torch.equal(batch_covar.to_dense(), lazy_covar.to_dense())
          )
          # test non BlockDiagLinearOperator
          mt_mvn = MultitaskMultivariateNormal(
              torch.zeros(3, 2, **tkwargs), block_diag_covar.to_dense()
          )
          with self.assertRaises(BotorchError):
              extract_batch_covar(mt_mvn=mt_mvn)
  
file_path: botorch/test/utils/test_low_rank.py
imports: import torch
  from botorch.exceptions.errors import BotorchError
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.models.model_list_gp_regression import ModelListGP
  from botorch.sampling.normal import IIDNormalSampler
  from botorch.utils.low_rank import extract_batch_covar, sample_cached_cholesky
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.distributions.multitask_multivariate_normal import MultitaskMultivariateNormal
  from linear_operator.operators import BlockDiagLinearOperator, to_linear_operator
  from linear_operator.utils.errors import NanError
label_desc: Use image node_id 1 for calling the TestExtractBatchCovar obj's underlying member method code with example usage: obj.test_extract_batch_covar() without return types