class_docstr: Class for the Fisher information metric on exponential distributions.
  
  References
  ----------
  .. [AM1981] Atkinson, C., & Mitchell, A. F. (1981). Rao's distance measure.
      Sankhyā: The Indian Journal of Statistics, Series A, 345-365.
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docstr: Compute squared distance associated with the exponential Fisher Rao metric.
  
  Parameters
  ----------
  point_a : array-like, shape=[..., 1]
      Point representing an exponential distribution (scale parameter).
  point_b : array-like, shape=[..., 1]
      Point representing a exponential distribution (scale parameter).
  
  Returns
  -------
  squared_dist : array-like, shape=[...,]
      Squared distance between points point_a and point_b.
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code: def squared_dist(self, point_a, point_b, **kwargs):
      """Compute squared distance associated with the exponential Fisher Rao metric.
  
      Parameters
      ----------
      point_a : array-like, shape=[..., 1]
          Point representing an exponential distribution (scale parameter).
      point_b : array-like, shape=[..., 1]
          Point representing a exponential distribution (scale parameter).
  
      Returns
      -------
      squared_dist : array-like, shape=[...,]
          Squared distance between points point_a and point_b.
      """
      return gs.squeeze(gs.log(point_a / point_b) ** 2)
  
imports: from scipy.stats import expon
  import geomstats.backend
  from geomstats.geometry.base import VectorSpaceOpenSet
  from geomstats.geometry.euclidean import Euclidean
  from geomstats.geometry.riemannian_metric import RiemannianMetric
  from geomstats.information_geometry.base import InformationManifoldMixin, ScipyUnivariateRandomVariable
label_desc: Use image node_id 1 for calling the ExponentialMetric obj's underlying member method code with example usage: obj.squared_dist(point_a, point_b) and returns: gs