class_docstr: The Matern 5/2 kernel. Functions drawn from a GP with this kernel are twice
  differentiable. The kernel equation is
  
  k(r) = σ² (1 + √5r + 5/3r²) exp{-√5 r}
  
  where:
  r  is the Euclidean distance between the input points, scaled by the lengthscales parameter ℓ,
  σ² is the variance parameter.
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code: def K_r(self, r: TensorType) -> tf.Tensor:
      sqrt5 = np.sqrt(5.0)
      return (
          self.variance
          * (1.0 + sqrt5 * r + 5.0 / 3.0 * tf.square(r))
          * tf.exp(-sqrt5 * r)
      )
  
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 Matern52 obj's underlying member method code with example usage: obj.K_r(r) and returns: unknown