code: def interp_response_cache(self):
      wmat = self.prepare_dense_wmat()
      noise_term = self.likelihood.noise_covar(
          wmat.transpose(-1, -2) if len(wmat.shape) > 2 else wmat
      )
      demeaned_train_targets = (
          self.train_labels - self.train_prior_dist.mean
      )
      dinv_y = noise_term.solve(demeaned_train_targets.unsqueeze(-1))
      return wmat.matmul(dinv_y)
  
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 7 for calling the InterpolatedPredictionStrategy obj's underlying member method code with example usage: obj.interp_response_cache() and returns: wmat