A template for implementing custom random variables in Edward.

concrete.py

from edward.models import RandomVariable
from tensorflow.contrib.distributions import Distribution

class Concrete(RandomVariable, Distribution):
  def __init__(self, *args, **kwargs):
    kwargs['is_continuous'] = True
    kwargs['is_reparameterized'] = True
    super(Concrete, self).__init__(*args, **kwargs)
    
  def _log_prob(self, value):
    # TODO
    # https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/distributions/python/ops/distribution.py#L183
    raise NotImplementedError("log_prob is not implemented")

  def _sample_n(self, n, seed=None):
    # TODO it takes an integer n as input and outputs a tensor of shape 
    # (n,) + batch_shape + event_shape
    # https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/distributions/python/ops/distribution.py#L557
    raise NotImplementedError("sample_n is not implemented")