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"]
code: def _mode(self):
      # Same logic as for _quantile.
      if not self.invertible:
          raise NotImplementedError()
      return self.transform(self.base._mode())
  
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 17 for calling the MonotonicAdapter obj's underlying member method code with example usage: obj._mode() and returns: self