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: Computes penalty encouraging compressibility.
  
  Args:
    bottleneck: `tf.Tensor` containing the data to be compressed. Must have at
      least `self.coding_rank` dimensions.
  
  Returns:
    Penalty value, which has the same shape as `bottleneck` without the
    `self.coding_rank` innermost dimensions.
docstr_tok: ["Computes","penalty","encouraging","compressibility",".","Args",":","bottleneck",":","`","tf.Tensor","`","containing","the","data","to","be","compressed",".","Must","have","at","least","`","self.coding_rank","`","dimensions",".","Returns",":","Penalty","value",",","which","has","the","same","shape","as","`","bottleneck","`","without","the","`","self.coding_rank","`","innermost","dimensions","."]
code: def penalty(self, bottleneck):
      """Computes penalty encouraging compressibility.
  
      Args:
        bottleneck: `tf.Tensor` containing the data to be compressed. Must have at
          least `self.coding_rank` dimensions.
  
      Returns:
        Penalty value, which has the same shape as `bottleneck` without the
        `self.coding_rank` innermost dimensions.
      """
      bottleneck = tf.convert_to_tensor(
          bottleneck, dtype=self.bottleneck_dtype
      )
      penalty = tf.math.log((abs(bottleneck) + self.alpha) / self.alpha)
      return tf.reduce_sum(
          penalty, axis=tuple(range(-self.coding_rank, 0))
      )
  
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 6 for calling the PowerLawEntropyModel obj's underlying member method code with example usage: obj.penalty(bottleneck) and returns: tf