code: def exact_predictive_covar(self, test_test_covar, test_train_covar):
      if settings.skip_posterior_variances.on():
          return ZeroLinearOperator(*test_test_covar.size())
  
      if isinstance(test_test_covar, ConstantMulLinearOperator):
          constant = test_test_covar.expanded_constant
          test_test_covar = test_test_covar.base_linear_op
      else:
          constant = torch.tensor(
              1.0,
              dtype=test_test_covar.dtype,
              device=test_test_covar.device,
          )
  
      covar_cache = self.covar_cache
      factor = test_test_covar.root.to_dense() * constant.sqrt()
      res = RootLinearOperator(factor @ covar_cache)
      return res
  
imports: import functools
  import string
  import warnings
  import torch
  from linear_operator import to_dense, to_linear_operator
  from linear_operator.operators import AddedDiagLinearOperator, BatchRepeatLinearOperator, ConstantMulLinearOperator, DenseLinearOperator, InterpolatedLinearOperator, LinearOperator, LowRankRootAddedDiagLinearOperator, MaskedLinearOperator, MatmulLinearOperator, RootLinearOperator, ZeroLinearOperator
  from linear_operator.utils.cholesky import psd_safe_cholesky
  from linear_operator.utils.interpolation import left_interp, left_t_interp
  from torch import Tensor
  from ..None import settings
  from ..distributions import MultitaskMultivariateNormal
  from ..lazy import LazyEvaluatedKernelTensor
  from ..utils.memoize import add_to_cache, cached, clear_cache_hook, pop_from_cache
label_desc: Use image node_id 5 for calling the RFFPredictionStrategy obj's underlying member method code with example usage: obj.exact_predictive_covar(test_test_covar, test_train_covar) and returns: res, ZeroLinearOperator