class_docstr: Gibbs sampling [@geman1984stochastic].
  
  Note `Gibbs` assumes the proposal distribution has the same
  support as the prior. The `auto_transform` attribute in
  the method `initialize()` is not applicable.
  
  #### Examples
  
  ```python
  x_data = np.array([0, 1, 0, 0, 0, 0, 0, 0, 0, 1])
  
  p = Beta(1.0, 1.0)
  x = Bernoulli(probs=p, sample_shape=10)
  
  qp = Empirical(tf.Variable(tf.zeros(500)))
  inference = ed.Gibbs({p: qp}, data={x: x_data})
  ```
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docstr: #### Notes
  
  The updates assume each Empirical random variable is directly
  parameterized by `tf.Variable`s.
docstr_tok: ["#","#","#","#","Notes","The","updates","assume","each","Empirical","random","variable","is","directly","parameterized","by","`","tf.Variable","`","s","."]
code: def build_update(self):
      """
      #### Notes
  
      The updates assume each Empirical random variable is directly
      parameterized by `tf.Variable`s.
      """
      # Update Empirical random variables according to the complete
      # conditionals. We will feed the conditionals when calling `update()`.
      assign_ops = []
      for z, qz in six.iteritems(self.latent_vars):
          variable = qz.get_variables()[0]
          assign_ops.append(
              tf.scatter_update(variable, self.t, self.proposal_vars[z])
          )
  
      # Increment n_accept (if accepted).
      assign_ops.append(self.n_accept.assign_add(1))
      return tf.group(*assign_ops)
  
file_path: edward/edward/inferences/gibbs.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import random
  import six
  import tensorflow
  from collections import OrderedDict
  from edward.inferences.conjugacy import complete_conditional
  from edward.inferences.monte_carlo import MonteCarlo
  from edward.models import RandomVariable
  from edward.util import check_latent_vars, get_session
label_desc: Use image node_id 4 for calling the Gibbs obj's underlying member method code with example usage: obj.build_update() and returns: tf