code: def prediction_strategy(
      train_inputs, train_prior_dist, train_labels, likelihood
  ):
      train_train_covar = train_prior_dist.lazy_covariance_matrix
      if isinstance(train_train_covar, LazyEvaluatedKernelTensor):
          cls = train_train_covar.kernel.prediction_strategy
      else:
          cls = DefaultPredictionStrategy
      return cls(
          train_inputs, train_prior_dist, train_labels, likelihood
      )
  
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 1 for calling a global function with example usage: prediction_strategy(train_inputs, train_prior_dist, train_labels, likelihood) and returns: cls