class_docstr: Class for the Fisher information metric on binomial 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 binomial Fisher Rao metric.
  
  Parameters
  ----------
  point_a : array-like, shape=[..., 1]
      Point representing a binomial distribution (probability of success).
  point_b : array-like, shape=[..., 1]
      Point representing a binomial distribution (probability of success).
  
  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):
      """Compute squared distance associated with the binomial Fisher Rao metric.
  
      Parameters
      ----------
      point_a : array-like, shape=[..., 1]
          Point representing a binomial distribution (probability of success).
      point_b : array-like, shape=[..., 1]
          Point representing a binomial distribution (probability of success).
  
      Returns
      -------
      squared_dist : array-like, shape=[...,]
          Squared distance between points point_a and point_b.
      """
      return gs.squeeze(
          4
          * self._space.n_draws
          * (gs.arcsin(gs.sqrt(point_a)) - gs.arcsin(gs.sqrt(point_b)))
          ** 2,
          axis=-1,
      )
  
imports: from scipy.special import factorial
  from scipy.stats import binom
  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 BinomialMetric obj's underlying member method code with example usage: obj.squared_dist(point_a, point_b) and returns: gs