class_docstr: Entropy model for power-law distributed random variables.
  
  This entropy model handles quantization and compression of a bottleneck tensor
  and implements a penalty that encourages compressibility under the Elias gamma
  code.
  
  The gamma code has code lengths `1 + 2 floor(log_2(x))`, for `x` a positive
  integer, and is close to optimal if `x` is distributed according to a power
  law. Being a universal code, it also guarantees that in the worst case, the
  expected code length is no more than 3 times the entropy of the empirical
  distribution of `x`, as long as probability decreases with increasing `x`. For
  details on the gamma code, see:
  
  > "Universal Codeword Sets and Representations of the Integers"<br />
  > P. Elias<br />
  > https://doi.org/10.1109/TIT.1975.1055349
  
  Given a signed integer, `run_length_gamma_encode` encodes zeros using a
  run-length code, the sign using a uniform bit, and applies the gamma code to
  the magnitude.
  
  The penalty applied by this class is given by:
  ```
  log((abs(x) + alpha) / alpha)
  ```
  This encourages `x` to follow a symmetrized power law, but only approximately
  for `alpha > 0`. Without `alpha`, the penalty would have a singularity at
  zero. Setting `alpha` to a small positive value ensures that the penalty is
  non-negative, and that its gradients are useful for optimization.
docstr: Alpha parameter.
docstr_tok: ["Alpha","parameter","."]
code: def alpha(self):
      """Alpha parameter."""
      return self._alpha
  
file_path: compression/tensorflow_compression/python/entropy_models/power_law.py
imports: import tensorflow
  from tensorflow_compression.python.ops import gen_ops
  from tensorflow_compression.python.ops import round_ops
label_desc: Use image node_id 2 for calling the PowerLawEntropyModel obj's underlying member method code with example usage: obj.alpha() and returns: self