class_docstr: Sparse GP regression.
  
  The key reference is :cite:t:`titsias2009variational`.
  
  For a use example see :doc:`../../../../notebooks/getting_started/large_data`.
code: def maximum_log_likelihood_objective(self) -> tf.Tensor:  # type: ignore[override]
      return self.elbo()
  
imports: from typing import NamedTuple, Optional, Tuple
  import numpy
  import tensorflow
  from check_shapes import check_shapes, inherit_check_shapes
  from ..None import posteriors
  from ..base import InputData, MeanAndVariance, RegressionData, TensorData
  from ..config import default_float, default_jitter
  from ..covariances.dispatch import Kuf, Kuu
  from ..inducing_variables import InducingPoints
  from ..kernels import Kernel
  from ..likelihoods import Gaussian
  from ..mean_functions import MeanFunction
  from ..utilities import add_noise_cov, assert_params_false, to_default_float
  from .model import GPModel
  from .training_mixins import InternalDataTrainingLossMixin
  from .util import InducingPointsLike, data_input_to_tensor, inducingpoint_wrapper
label_desc: Use image node_id 1 for calling the SGPR_deprecated obj's underlying member method code with example usage: obj.maximum_log_likelihood_objective() and returns: self