class_docstr: Base class for kernels that are stationary, that is, they only depend on
  
      d = x - x'
  
  This class handles 'ard' behaviour, which stands for 'Automatic Relevance
  Determination'. This means that the kernel has one lengthscale per
  dimension, otherwise the kernel is isotropic (has a single lengthscale).
class_docstr_tok: ["Base","class","for","kernels","that","are","stationary",",","that","is",",","they","only","depend","on","d","=","x","-","x'","This","class","handles","'ard","'","behaviour",",","which","stands","for","'Automatic","Relevance","Determination","'",".","This","means","that","the","kernel","has","one","lengthscale","per","dimension",",","otherwise","the","kernel","is","isotropic","(","has","a","single","lengthscale",")","."]
code: def scale(self, X: TensorType) -> TensorType:
      X_scaled = X / self.lengthscales if X is not None else X
      return X_scaled
  
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 3 for calling the Stationary obj's underlying member method code with example usage: obj.scale(X) and returns: X_scaled