code: def covar_cache(self):
      lt = self.train_prior_dist.lazy_covariance_matrix
      if isinstance(lt, ConstantMulLinearOperator):
          constant = lt.expanded_constant
          lt = lt.base_linear_op
      else:
          constant = torch.tensor(1.0, dtype=lt.dtype, device=lt.device)
  
      train_factor = lt.root.to_dense()
      train_train_covar = self.lik_train_train_covar
      inner_term = (
          torch.eye(
              train_factor.size(-1),
              dtype=train_factor.dtype,
              device=train_factor.device,
          )
          - (
              train_factor.transpose(-1, -2)
              @ train_train_covar.solve(train_factor)
          )
          * constant
      )
      return psd_safe_cholesky(inner_term)
  
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 3 for calling the RFFPredictionStrategy obj's underlying member method code with example usage: obj.covar_cache() and returns: psd_safe_cholesky