class_docstr: The radial basis function (RBF) or squared exponential kernel. The kernel equation is
  
      k(r) = σ² exp{-½ r²}
  
  where:
  r   is the Euclidean distance between the input points, scaled by the lengthscales parameter ℓ.
  σ²  is the variance parameter
  
  Functions drawn from a GP with this kernel are infinitely differentiable!
class_docstr_tok: ["The","radial","basis","function","(","RBF",")","or","squared","exponential","kernel",".","The","kernel","equation","is","k","(","r",")","=","\u03c3\u00b2","exp","{","-\u00bd","r\u00b2","}","where",":","r","is","the","Euclidean","distance","between","the","input","points",",","scaled","by","the","lengthscales","parameter","\u2113.","\u03c3\u00b2","is","the","variance","parameter","Functions","drawn","from","a","GP","with","this","kernel","are","infinitely","differentiable","!"]
code: def K_r2(self, r2: TensorType) -> tf.Tensor:
      return self.variance * tf.exp(-0.5 * r2)
  
imports: from typing import Any, Optional
  import numpy
  import tensorflow
  from check_shapes import check_shapes, inherit_check_shapes
  from ..base import Parameter, TensorType
  from ..utilities import positive
  from ..utilities.ops import difference_matrix, square_distance
  from .base import ActiveDims, Kernel
label_desc: Use image node_id 1 for calling the SquaredExponential obj's underlying member method code with example usage: obj.K_r2(r2) and returns: unknown