class_docstr: Transform inputs by Kumaraswamy bijector.
  
  Uses Kumaraswamy bijector to warp features. The purpose of this is to is turn
  a stationary kernel in to a non-stationary one. Note that this kernel requires
  inputs to be in `[0, 1]` (the domain of the bijector).
class_docstr_tok: ["Transform","inputs","by","Kumaraswamy","bijector",".","Uses","Kumaraswamy","bijector","to","warp","features",".","The","purpose","of","this","is","to","is","turn","a","stationary","kernel","in","to","a","non-stationary","one",".","Note","that","this","kernel","requires","inputs","to","be","in","`","[","0",",","1","]","`","(","the","domain","of","the","bijector",")","."]
code: def concentration0(self):
      return self._concentration0
  
file_path: probability/tensorflow_probability/python/math/psd_kernels/kumaraswamy_transformed.py
imports: import collections
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.math.psd_kernels import feature_transformed
  from tensorflow_probability.python.math.psd_kernels.internal import util
label_desc: Use image node_id 3 for calling the KumaraswamyTransformed obj's underlying member method code with example usage: obj.concentration0() and returns: self