code: def prepare_dense_wmat(self, covar=None):
      # prepare the w matrix which is batch shape x m x n, where n = covar.shape[-2]
      if covar is None:
          covar = self.train_prior_dist.lazy_covariance_matrix
      wmat = covar._sparse_left_interp_t(
          covar.left_interp_indices, covar.left_interp_values
      ).to_dense()
      return to_linear_operator(wmat)
  
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 5 for calling the InterpolatedPredictionStrategy obj's underlying member method code with example usage: obj.prepare_dense_wmat(covar) and returns: to_linear_operator