class_docstr: Base class for continuous entropy models.
  
  The basic functionality of this class is to pre-compute integer probability
  tables based on the provided `tfp.distributions.Distribution` object, which
  can then be used reliably across different platforms by the range coder and
  decoder.
class_docstr_tok: ["Base","class","for","continuous","entropy","models",".","The","basic","functionality","of","this","class","is","to","pre-compute","integer","probability","tables","based","on","the","provided","`","tfp.distributions.Distribution","`","object",",","which","can","then","be","used","reliably","across","different","platforms","by","the","range","coder","and","decoder","."]
docstr: Number of innermost dimensions considered a coding unit.
docstr_tok: ["Number","of","innermost","dimensions","considered","a","coding","unit","."]
code: def coding_rank(self):
      """Number of innermost dimensions considered a coding unit."""
      return self._coding_rank
  
imports: import abc
  from absl import logging
  import tensorflow
  from tensorflow_compression.python.distributions import helpers
  from tensorflow_compression.python.distributions import uniform_noise
  from tensorflow_compression.python.ops import gen_ops
label_desc: Use image node_id 10 for calling the ContinuousEntropyModelBase obj's underlying member method code with example usage: obj.coding_rank() and returns: self