class_docstr: This is an implementation of GPR that provides a posterior() method that
  enables caching for faster subsequent predictions.
class_docstr_tok: ["This","is","an","implementation","of","GPR","that","provides","a","posterior","(",")","method","that","enables","caching","for","faster","subsequent","predictions","."]
docstr: For backwards compatibility, GPR's predict_f uses the fused (no-cache)
  computation, which is more efficient during training.
  
  For faster (cached) prediction, predict directly from the posterior object, i.e.,:
      model.posterior().predict_f(Xnew, ...)
docstr_tok: ["For","backwards","compatibility",",","GPR","'s","predict_f","uses","the","fused","(","no-cache",")","computation",",","which","is","more","efficient","during","training",".","For","faster","(","cached",")","prediction",",","predict","directly","from","the","posterior","object",",","i.e.",",",":","model.posterior","(",")",".predict_f","(","Xnew",",","...",")"]
code: def predict_f(
      self,
      Xnew: InputData,
      full_cov: bool = False,
      full_output_cov: bool = False,
  ) -> MeanAndVariance:
      """
      For backwards compatibility, GPR's predict_f uses the fused (no-cache)
      computation, which is more efficient during training.
  
      For faster (cached) prediction, predict directly from the posterior object, i.e.,:
          model.posterior().predict_f(Xnew, ...)
      """
      return self.posterior(
          posteriors.PrecomputeCacheType.NOCACHE
      ).fused_predict_f(
          Xnew, full_cov=full_cov, full_output_cov=full_output_cov
      )
  
imports: from typing import Optional
  import tensorflow
  from check_shapes import check_shapes, inherit_check_shapes
  import gpflow
  from ..None import posteriors
  from ..base import InputData, MeanAndVariance, RegressionData, TensorData
  from ..kernels import Kernel
  from ..likelihoods import Gaussian
  from ..logdensities import multivariate_normal
  from ..mean_functions import MeanFunction
  from ..utilities import add_likelihood_noise_cov, assert_params_false
  from .model import GPModel
  from .training_mixins import InternalDataTrainingLossMixin
  from .util import data_input_to_tensor
label_desc: Use image node_id 2 for calling the GPR_with_posterior obj's underlying member method code with example usage: obj.predict_f(Xnew, full_cov, full_output_cov) and returns: self