Implements conditional entropy models.

Also refactors entropy models to minimize deep TensorFlow imports, and removes
necessity to clip large values in `EntropyBottleneck`.

PiperOrigin-RevId: 242486869

Author: jballe

examples/bls2017.py

@@ -168,10 +168,6 @@ def train():
   tf.summary.image("original", quantize_image(x))
   tf.summary.image("reconstruction", quantize_image(x_tilde))
 
-  # Creates summary for the probability mass function (PMF) estimated in the
-  # bottleneck.
-  entropy_bottleneck.visualize()
-
   hooks = [
       tf.train.StopAtStepHook(last_step=args.last_step),
       tf.train.NanTensorHook(train_loss),