class_docstr: The ExponentiatedQuadratic kernel.
  
  Sometimes called the "squared exponential", "Gaussian" or "radial basis
  function", this kernel function has the form
  
    ```none
    k(x, y) = amplitude**2 * exp(-||x - y||**2 / (2 * length_scale**2))
    ```
  
  where the double-bars represent vector length (ie, Euclidean, or L2 norm).
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code: def _matrix(self, x1, x2):
      pairwise_square_distance = util.pairwise_square_distance_matrix(
          x1, x2, self.feature_ndims
      )
      return self._apply_with_distance(
          x1, x2, pairwise_square_distance, example_ndims=2
      )
  
file_path: probability/tensorflow_probability/python/math/psd_kernels/exponentiated_quadratic.py
imports: 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 9 for calling the ExponentiatedQuadratic obj's underlying member method code with example usage: obj._matrix(x1, x2) and returns: self