class_docstr: Bayesian model.
  
  This is a base class for all GPflow models. See also :class:`GPModel`.
  
  A bayesian model provides methods for computing prior- and posterior densities, and a maximum
  likelihood objective; allowing you to use generic code to optimise model parameters to fit data.
  
  Most bayesian models are expected to hold their data internally, but the methods take ``*args``
  and ``**kwargs`` allowing you to write implementations that take data as parameters.
  See also :class:`gpflow.models.training_mixins.InternalDataTrainingLossMixin`,
  :class:`gpflow.models.training_mixins.ExternalDataTrainingLossMixin`,
  and :func:`gpflow.models.training_loss`.
docstr: Training loss definition. To allow MAP (maximum a-posteriori) estimation,
  adds the log density of all priors to maximum_log_likelihood_objective().
docstr_tok: ["Training","loss","definition",".","To","allow","MAP","(","maximum","a-posteriori",")","estimation",",","adds","the","log","density","of","all","priors","to","maximum_log_likelihood_objective","(",")","."]
code: def _training_loss(self, *args: Any, **kwargs: Any) -> tf.Tensor:
      """
      Training loss definition. To allow MAP (maximum a-posteriori) estimation,
      adds the log density of all priors to maximum_log_likelihood_objective().
      """
      return -(
          self.maximum_log_likelihood_objective(*args, **kwargs)
          + self.log_prior_density()
      )
  
imports: import abc
  from typing import Any, Optional
  import tensorflow
  from check_shapes import check_shapes
  from ..base import InputData, MeanAndVariance, Module, RegressionData
  from ..conditionals.util import sample_mvn
  from ..kernels import Kernel, MultioutputKernel
  from ..likelihoods import Likelihood, SwitchedLikelihood
  from ..mean_functions import MeanFunction, Zero
  from ..utilities import assert_params_false, to_default_float
label_desc: Use image node_id 3 for calling the BayesianModel obj's underlying member method code with example usage: obj._training_loss() and returns: unknown