class_docstr: Shared logic for amplitude/length_scale parameterized kernels.
class_docstr_tok: ["Shared","logic","for","amplitude\/length_scale","parameterized","kernels","."]
code: def _apply(self, x1, x2, example_ndims=0):
      pairwise_square_distance = (
          util.sum_rightmost_ndims_preserving_shape(
              tf.math.squared_difference(x1, x2),
              ndims=self.feature_ndims,
          )
      )
      return self._apply_with_distance(
          x1, x2, pairwise_square_distance, example_ndims=example_ndims
      )
  
imports: import numpy
  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 import bessel
  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 8 for calling the _AmplitudeLengthScaleMixin obj's underlying member method code with example usage: obj._apply(x1, x2, example_ndims) and returns: self