def initialize(self, step_size=0.25, friction=0.1, *args, **kwargs):
    'Initialize inference algorithm.\n\n    Args:\n      step_size: float, optional.\n        Constant scale factor of learning rate.\n      friction: float, optional.\n        Constant scale on the friction term in the Hamiltonian system.\n    '
    self.step_size = step_size
    self.friction = friction
    self.v = {z: tf.Variable(tf.zeros(qz.params.shape[1:])) for (z, qz) in six.iteritems(self.latent_vars)}
    return super(SGHMC, self).initialize(*args, **kwargs)
Initialize inference algorithm.

Args:
  step_size: float, optional.
    Constant scale factor of learning rate.
  friction: float, optional.
    Constant scale on the friction term in the Hamiltonian system.
def initialize(self, step_size=0.25, friction=0.1, *args, **kwargs):
    'Initialize inference algorithm.\n\n    Args:\n      step_size: float, optional.\n        Constant scale factor of learning rate.\n      friction: float, optional.\n        Constant scale on the friction term in the Hamiltonian system.\n    '
    self.step_size = step_size
    self.friction = friction
    self.v = {z: tf.Variable(tf.zeros(qz.params.shape[1:])) for (z, qz) in six.iteritems(self.latent_vars)}
    return super(SGHMC, self).initialize(*args, **kwargs)