class_docstr: Variational inference with the KL divergence
  
  $\text{KL}( q(z; \lambda) \| p(z \mid x) ).$
  
  This class minimizes the objective using the score function gradient
  and Rao-Blackwellization.
  
  #### Notes
  
  Current Rao-Blackwellization is limited to Rao-Blackwellizing across
  stochastic nodes in the computation graph. It does not
  Rao-Blackwellize within a node such as when a node represents
  multiple random variables via non-scalar batch shape.
  
  The objective function also adds to itself a summation over all
  tensors in the `REGULARIZATION_LOSSES` collection.
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code: def build_loss_and_gradients(self, var_list):
      return build_score_rb_loss_and_gradients(self, var_list)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import six
  import tensorflow
  from edward.inferences.variational_inference import VariationalInference
  from edward.models import RandomVariable
  from edward.util import copy, get_descendants
label_desc: Use image node_id 3 for calling the ScoreRBKLqp obj's underlying member method code with example usage: obj.build_loss_and_gradients(var_list) and returns: build_score_rb_loss_and_gradients