class_docstr: Exponential Curve Kernel.
  
  This kernel is defined as over positive real numbers as:
  
  ```none
  k(x, y) = int_0^inf exp(-s * x) exp(-s * y) GammaPDF(c, r)
  ```
  where:
    * `GammaPDF` is the pdf of the Gamma distribution.
    * `c` is the concentration parameter of the Gamma distribution.
    * `r` is the rate parameter of the Gamma distribution.
  
  See equation 6 in https://arxiv.org/pdf/1406.3896.pdf
  
  When parameterizing a 1-dimensional Gaussian Process, this kernel produces
  samples that strongly favor exponential functions.
  
  When called on multiple feature dimensions, all feature dimensions are added
  together before the kernel function is applied.
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docstr: Concentration parameter.
docstr_tok: ["Concentration","parameter","."]
code: def concentration(self):
      """Concentration parameter."""
      return self._concentration
  
file_path: probability/tensorflow_probability/python/math/psd_kernels/exponential_curve.py
imports: 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 positive_semidefinite_kernel
  from tensorflow_probability.python.math.psd_kernels.internal import util
label_desc: Use image node_id 2 for calling the ExponentialCurve obj's underlying member method code with example usage: obj.concentration() and returns: self