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: Compresses a floating-point tensor.
  
  Compresses the tensor to bit strings. `bottleneck` is first quantized
  as in `quantize()`, and then compressed using the run-length gamma code. The
  quantized tensor can later be recovered by calling `decompress()`.
  
  The innermost `self.coding_rank` dimensions are treated as one coding unit,
  i.e. are compressed into one string each. Any additional dimensions to the
  left are treated as batch dimensions.
  
  Args:
    bottleneck: `tf.Tensor` containing the data to be compressed. Must have at
      least `self.coding_rank` dimensions.
  
  Returns:
    A `tf.Tensor` having the same shape as `bottleneck` without the
    `self.coding_rank` innermost dimensions, containing a string for each
    coding unit.
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code: def compress(self, bottleneck):
      """Compresses a floating-point tensor.
  
      Compresses the tensor to bit strings. `bottleneck` is first quantized
      as in `quantize()`, and then compressed using the run-length gamma code. The
      quantized tensor can later be recovered by calling `decompress()`.
  
      The innermost `self.coding_rank` dimensions are treated as one coding unit,
      i.e. are compressed into one string each. Any additional dimensions to the
      left are treated as batch dimensions.
  
      Args:
        bottleneck: `tf.Tensor` containing the data to be compressed. Must have at
          least `self.coding_rank` dimensions.
  
      Returns:
        A `tf.Tensor` having the same shape as `bottleneck` without the
        `self.coding_rank` innermost dimensions, containing a string for each
        coding unit.
      """
      bottleneck = tf.convert_to_tensor(
          bottleneck, dtype=self.bottleneck_dtype
      )
  
      shape = tf.shape(bottleneck)
      if self.coding_rank == 0:
          flat_shape = [-1]
          strings_shape = shape
      else:
          flat_shape = tf.concat([[-1], shape[-self.coding_rank :]], 0)
          strings_shape = shape[: -self.coding_rank]
  
      symbols = tf.cast(tf.round(bottleneck), tf.int32)
      symbols = tf.reshape(symbols, flat_shape)
  
      strings = tf.map_fn(
          gen_ops.run_length_gamma_encode,
          symbols,
          fn_output_signature=tf.TensorSpec((), dtype=tf.string),
      )
      return tf.reshape(strings, strings_shape)
  
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 8 for calling the PowerLawEntropyModel obj's underlying member method code with example usage: obj.compress(bottleneck) and returns: tf