class_docstr: Differentiable approximation to round.
class_docstr_tok: ["Differentiable","approximation","to","round","."]
docstr: Initializer.
  
  Args:
    base: A `tfp.distributions.Distribution` object representing a
      continuous-valued random variable.
    alpha: Float or tf.Tensor. Controls smoothness of the approximation.
    name: String. A name for this distribution.
docstr_tok: ["Initializer",".","Args",":","base",":","A","`","tfp.distributions.Distribution","`","object","representing","a","continuous-valued","random","variable",".","alpha",":","Float","or","tf.Tensor",".","Controls","smoothness","of","the","approximation",".","name",":","String",".","A","name","for","this","distribution","."]
code: def __init__(self, base, alpha, name="SoftRoundAdapter"):
      """Initializer.
  
      Args:
        base: A `tfp.distributions.Distribution` object representing a
          continuous-valued random variable.
        alpha: Float or tf.Tensor. Controls smoothness of the approximation.
        name: String. A name for this distribution.
      """
      super().__init__(base=base, name=name)
      self._alpha = alpha
  
imports: import tensorflow
  import tensorflow_probability
  from tensorflow_compression.python.distributions import deep_factorized
  from tensorflow_compression.python.distributions import helpers
  from tensorflow_compression.python.distributions import uniform_noise
  from tensorflow_compression.python.ops import round_ops
label_desc: Use image node_id 1 to create a new SoftRoundAdapter object from inherited base classes: MonotonicAdapter with example: obj = SoftRoundAdapter(base, alpha, name)