class_docstr: Exponentiated Sine Squared Kernel.
  
  Also known as the "Periodic" kernel, this kernel, when
  parameterizing a Gaussian Process, results in random functions that are
  periodic almost everywhere (with the same period for every dimension).
  
  ```none
  k(x, y) = amplitude**2 * exp(
    -2  / length_scale ** 2 * sum_k sin(pi * |x_k - y_k| / period) ** 2)
  ```
  
  This kernel acts over the space `S = R^(D1 x D2 x D3 ... Dd)`.
class_docstr_tok: ["Exponentiated","Sine","Squared","Kernel",".","Also","known","as","the","``","Periodic","''","kernel",",","this","kernel",",","when","parameterizing","a","Gaussian","Process",",","results","in","random","functions","that","are","periodic","almost","everywhere","(","with","the","same","period","for","every","dimension",")",".","``","`","none","k","(","x",",","y",")","=","amplitude","*","*","2","*","exp","(","-2","\/","length_scale","*","*","2","*","sum_k","sin","(","pi","*","|x_k","-","y_k|","\/","period",")","*","*","2",")","``","`","This","kernel","acts","over","the","space","`","S","=","R^","(","D1","x","D2","x","D3","...","Dd",")","`","."]
docstr: Length scale parameter.
docstr_tok: ["Length","scale","parameter","."]
code: def length_scale(self):
      """Length scale parameter."""
      return self._length_scale
  
file_path: probability/tensorflow_probability/python/math/psd_kernels/exp_sin_squared.py
imports: import numpy
  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 4 for calling the ExpSinSquared obj's underlying member method code with example usage: obj.length_scale() and returns: self