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 an analytic entropy term.
  
  The objective function also adds to itself a summation over all
  tensors in the `REGULARIZATION_LOSSES` collection.
class_docstr_tok: ["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","an","analytic","entropy","term",".","The","objective","function","also","adds","to","itself","a","summation","over","all","tensors","in","the","`","REGULARIZATION_LOSSES","`","collection","."]
docstr: Initialize inference algorithm. It initializes hyperparameters
  and builds ops for the algorithm's computation graph.
  
  Args:
    n_samples: int.
      Number of samples from variational model for calculating
      stochastic gradients.
code: def initialize(self, n_samples=1, *args, **kwargs):
      """Initialize inference algorithm. It initializes hyperparameters
      and builds ops for the algorithm's computation graph.
  
      Args:
        n_samples: int.
          Number of samples from variational model for calculating
          stochastic gradients.
      """
      if n_samples <= 0:
          raise ValueError(
              "n_samples should be greater than zero: {}".format(
                  n_samples
              )
          )
      self.n_samples = n_samples
      return super(ScoreEntropyKLqp, self).initialize(*args, **kwargs)
  
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 2 for calling the ScoreEntropyKLqp obj's underlying member method code with example usage: obj.initialize(n_samples) and returns: super