code: def interp_inner_prod(self):
      # the W'W cache
      wmat = self.prepare_dense_wmat()
      noise_term = self.likelihood.noise_covar(
          wmat.transpose(-1, -2) if len(wmat.shape) > 2 else wmat
      )
      interp_inner_prod = wmat.matmul(
          noise_term.solve(wmat.transpose(-1, -2))
      )
      return interp_inner_prod
  
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 6 for calling the InterpolatedPredictionStrategy obj's underlying member method code with example usage: obj.interp_inner_prod() and returns: interp_inner_prod