class_docstr: Implements the ESP kernel from Kandasamy, Yu 2016, 'Additive Approximations in High
  Dimensional Nonparametric Regression. 
class_docstr_tok: ["Implements","the","ESP","kernel","from","Kandasamy",",","Yu","2016",",","'Additive","Approximations","in","High","Dimensional","Nonparametric","Regression","."]
code: def __init__(self, scale, order, kernel_list):
      super(ESPKernel, self).__init__()
      self.dim = len(kernel_list)
      self.kernel_list = kernel_list
      self.add_hyperparams(scale=scale, order=order)
  
      if self.dim < 0:
          raise ValueError("dim cannot not be negative.")
      if order > self.dim:
          raise ValueError("order must be less than or equal to dim.")
      if order < 1:
          raise ValueError(
              "order must be an integer between 1 and dim."
          )
  
imports: from __future__ import print_function
  import numpy
  from scipy.linalg import solve_triangular
label_desc: Use image node_id 1 to create a new ESPKernel object from inherited base classes: Kernel with example: obj = ESPKernel(scale, order, kernel_list)