code: def exact_predictive_covar(self, test_test_covar, test_train_covar):
      if (
          settings.fast_pred_var.off()
          and settings.fast_pred_samples.off()
      ):
          return super(
              InterpolatedPredictionStrategy, self
          ).exact_predictive_covar(test_test_covar, test_train_covar)
  
      self._last_test_train_covar = test_train_covar
      test_interp_indices = test_train_covar.left_interp_indices
      test_interp_values = test_train_covar.left_interp_values
  
      if self.uses_wiski:
          precomputed_cache = self.fantasy_covar_cache
          fps = settings.fast_pred_samples.on()
          if fps:
              root = left_interp(
                  test_interp_indices,
                  test_interp_values,
                  precomputed_cache[0].to_dense(),
              )
              res = RootLinearOperator(root)
          else:
              root = left_interp(
                  test_interp_indices,
                  test_interp_values,
                  precomputed_cache[1].to_dense(),
              )
              res = test_test_covar + RootLinearOperator(root).mul(-1)
          return res
      else:
          precomputed_cache = self.covar_cache
          fps = settings.fast_pred_samples.on()
          if (fps and precomputed_cache[0] is None) or (
              not fps and precomputed_cache[1] is None
          ):
              pop_from_cache(self, "covar_cache")
              precomputed_cache = self.covar_cache
  
          # Compute the exact predictive posterior
          if settings.fast_pred_samples.on():
              res = self._exact_predictive_covar_inv_quad_form_root(
                  precomputed_cache[0], test_train_covar
              )
              res = RootLinearOperator(res)
          else:
              root = left_interp(
                  test_interp_indices,
                  test_interp_values,
                  precomputed_cache[1],
              )
              res = test_test_covar + RootLinearOperator(root).mul(-1)
          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 14 for calling the InterpolatedPredictionStrategy obj's underlying member method code with example usage: obj.exact_predictive_covar(test_test_covar, test_train_covar) and returns: super, res, res