code: def exact_prediction(self, joint_mean, joint_covar):
      # Find the components of the distribution that contain test data
      test_mean = joint_mean[..., self.num_train :]
      test_test_covar = joint_covar[
          ..., self.num_train :, self.num_train :
      ].evaluate_kernel()
      test_train_covar = joint_covar[
          ..., self.num_train :, : self.num_train
      ].evaluate_kernel()
  
      return (
          self.exact_predictive_mean(test_mean, test_train_covar),
          self.exact_predictive_covar(
              test_test_covar, test_train_covar
          ),
      )
  
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 4 for calling the RFFPredictionStrategy obj's underlying member method code with example usage: obj.exact_prediction(joint_mean, joint_covar) and returns: self,  self