class_docstr: Adapt a continuous distribution via an ascending monotonic function.
  
  This is described in Appendix E. in the paper
  > "Universally Quantized Neural Compression"<br />
  > Eirikur Agustsson & Lucas Theis<br />
  > https://arxiv.org/abs/2006.09952
class_docstr_tok: ["Adapt","a","continuous","distribution","via","an","ascending","monotonic","function",".","This","is","described","in","Appendix","E.","in","the","paper",">","``","Universally","Quantized","Neural","Compression","''","<","br","\/",">",">","Eirikur","Agustsson","&","Lucas","Theis","<","br","\/",">",">","https",":","\/\/arxiv.org\/abs\/2006.09952"]
docstr: Initializer.
  
  Args:
    base: A `tfp.distributions.Distribution` object representing a
      continuous-valued random variable.
    name: String. A name for this distribution.
docstr_tok: ["Initializer",".","Args",":","base",":","A","`","tfp.distributions.Distribution","`","object","representing","a","continuous-valued","random","variable",".","name",":","String",".","A","name","for","this","distribution","."]
code: def __init__(self, base, name="MonotonicAdapter"):
      """Initializer.
  
      Args:
        base: A `tfp.distributions.Distribution` object representing a
          continuous-valued random variable.
        name: String. A name for this distribution.
      """
      parameters = dict(locals())
      self._base = base
      super().__init__(
          dtype=base.dtype,
          reparameterization_type=base.reparameterization_type,
          validate_args=base.validate_args,
          allow_nan_stats=base.allow_nan_stats,
          parameters=parameters,
          name=name,
      )
  
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 MonotonicAdapter object from inherited base classes: tfp.distributions with example: obj = MonotonicAdapter(base, name)