docstr: Xnew is a data matrix, point at which we want to predict
  
  This method computes
  
      p(F* | (F=LV) )
  
  where F* are points on the GP at Xnew, F=LV are points on the GP at X.
docstr_tok: ["Xnew","is","a","data","matrix",",","point","at","which","we","want","to","predict","This","method","computes","p","(","F","*","|","(","F=LV",")",")","where","F","*","are","points","on","the","GP","at","Xnew",",","F=LV","are","points","on","the","GP","at","X","."]
code: def predict_f(
      self,
      Xnew: InputData,
      full_cov: bool = False,
      full_output_cov: bool = False,
  ) -> MeanAndVariance:
      """
      Xnew is a data matrix, point at which we want to predict
  
      This method computes
  
          p(F* | (F=LV) )
  
      where F* are points on the GP at Xnew, F=LV are points on the GP at X.
  
      """
      assert_params_false(
          self.predict_f, full_output_cov=full_output_cov
      )
  
      X_data, _Y_data = self.data
      mu, var = conditional(
          Xnew,
          X_data,
          self.kernel,
          self.V,
          full_cov=full_cov,
          q_sqrt=None,
          white=True,
      )
      return mu + self.mean_function(Xnew), var
  
imports: from typing import Optional
  import numpy
  import tensorflow
  import tensorflow_probability
  from check_shapes import check_shapes, inherit_check_shapes
  from ..base import InputData, MeanAndVariance, Parameter, RegressionData
  from ..conditionals import conditional
  from ..config import default_float, default_jitter
  from ..kernels import Kernel
  from ..likelihoods import Likelihood
  from ..mean_functions import MeanFunction
  from ..utilities import assert_params_false, to_default_float
  from .model import GPModel
  from .training_mixins import InternalDataTrainingLossMixin
  from .util import data_input_to_tensor
label_desc: Use image node_id 6 for calling the GPMC obj's underlying member method code with example usage: obj.predict_f(Xnew, full_cov, full_output_cov) and returns: unknown,  var