class_docstr: Class for Riemannian and pseudo-Riemannian metrics.
  
  The associated Levi-Civita connection on the tangent bundle.
class_docstr_tok: ["Class","for","Riemannian","and","pseudo-Riemannian","metrics",".","The","associated","Levi-Civita","connection","on","the","tangent","bundle","."]
docstr: Squared geodesic distance between two points.
  
  Parameters
  ----------
  point_a : array-like, shape=[..., dim]
      Point.
  point_b : array-like, shape=[..., dim]
      Point.
  
  Returns
  -------
  sq_dist : array-like, shape=[...,]
      Squared distance.
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code: def squared_dist(self, point_a, point_b, **kwargs):
      """Squared geodesic distance between two points.
  
      Parameters
      ----------
      point_a : array-like, shape=[..., dim]
          Point.
      point_b : array-like, shape=[..., dim]
          Point.
  
      Returns
      -------
      sq_dist : array-like, shape=[...,]
          Squared distance.
      """
      log = self.log(point=point_b, base_point=point_a, **kwargs)
  
      return self.squared_norm(vector=log, base_point=point_a)
  
imports: from abc import ABC
  import joblib
  import geomstats.backend
  import geomstats.geometry
  from geomstats.geometry.connection import Connection
  from geomstats.vectorization import check_is_batch
label_desc: Use image node_id 15 for calling the RiemannianMetric obj's underlying member method code with example usage: obj.squared_dist(point_a, point_b) and returns: self