class_docstr: Stochastic gradient Langevin dynamics [@welling2011bayesian].
  
  #### Notes
  
  In conditional inference, we infer $z$ in $p(z, \beta
  \mid x)$ while fixing inference over $\beta$ using another
  distribution $q(\beta)$.
  `SGLD` substitutes the model's log marginal density
  
  $\log p(x, z) = \log \mathbb{E}_{q(\beta)} [ p(x, z, \beta) ]
                \approx \log p(x, z, \beta^*)$
  
  leveraging a single Monte Carlo sample, where $\beta^* \sim
  q(\beta)$. This is unbiased (and therefore asymptotically exact as a
  pseudo-marginal method) if $q(\beta) = p(\beta \mid x)$.
  
  #### Examples
  
  ```python
  mu = Normal(loc=0.0, scale=1.0)
  x = Normal(loc=mu, scale=1.0, sample_shape=10)
  
  qmu = Empirical(tf.Variable(tf.zeros(500)))
  inference = ed.SGLD({mu: qmu}, {x: np.zeros(10, dtype=np.float32)})
  ```
code: def __init__(self, *args, **kwargs):
      super(SGLD, self).__init__(*args, **kwargs)
  
file_path: edward/edward/inferences/sgld.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import six
  import tensorflow
  from edward.inferences.monte_carlo import MonteCarlo
  from edward.models import RandomVariable
  from edward.util import copy
label_desc: Use image node_id 1 to create a new SGLD object from inherited base classes: MonteCarlo with example: obj = SGLD()