code: def __init__(
      self,
      train_inputs,
      train_prior_dist,
      train_labels,
      likelihood,
      root=None,
      inv_root=None,
  ):
      # Get training shape
      self._train_shape = train_prior_dist.event_shape
  
      # Flatten the training labels
      try:
          train_labels = train_labels.reshape(
              *train_labels.shape[: -len(self.train_shape)],
              self._train_shape.numel(),
          )
      except RuntimeError:
          raise RuntimeError(
              "Flattening the training labels failed. The most common cause of this error is "
              + "that the shapes of the prior mean and the training labels are mismatched. "
              + "The shape of the train targets is {0}, ".format(
                  train_labels.shape
              )
              + "while the reported shape of the mean is {0}.".format(
                  train_prior_dist.mean.shape
              )
          )
  
      self.train_inputs = train_inputs
      self.train_prior_dist = train_prior_dist
      self.train_labels = train_labels
      self.likelihood = likelihood
      self._last_test_train_covar = None
      mvn = self.likelihood(train_prior_dist, train_inputs)
      self.lik_train_train_covar = mvn.lazy_covariance_matrix
  
      if root is not None:
          add_to_cache(
              self.lik_train_train_covar,
              "root_decomposition",
              RootLinearOperator(root),
          )
  
      if inv_root is not None:
          add_to_cache(
              self.lik_train_train_covar,
              "root_inv_decomposition",
              RootLinearOperator(inv_root),
          )
  
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 to create a new DefaultPredictionStrategy object from inherited base classes: object with example: obj = DefaultPredictionStrategy(train_inputs, train_prior_dist, train_labels, likelihood, root, inv_root)