class_docstr: Class for product of Riemannian metrics.
class_docstr_tok: ["Class","for","product","of","Riemannian","metrics","."]
docstr: Geodesic distance between two points.
  
  Parameters
  ----------
  point_a : array-like, shape=[..., self.shape]
      Point.
  point_b : array-like, shape=[..., self.shape]
      Point.
  
  Returns
  -------
  dist : array-like, shape=[...,]
      Distance.
docstr_tok: ["Geodesic","distance","between","two","points",".","Parameters","--","--","--","--","--","point_a",":","array-like",",","shape=","[","...",",","self.shape","]","Point",".","point_b",":","array-like",",","shape=","[","...",",","self.shape","]","Point",".","Returns","--","--","--","-","dist",":","array-like",",","shape=","[","...",",","]","Distance","."]
code: def dist(self, point_a, point_b):
      """Geodesic distance between two points.
  
      Parameters
      ----------
      point_a : array-like, shape=[..., self.shape]
          Point.
      point_b : array-like, shape=[..., self.shape]
          Point.
  
      Returns
      -------
      dist : array-like, shape=[...,]
          Distance.
      """
      args = {"point_a": point_a, "point_b": point_b}
      dists = gs.array(self._iterate_over_factors("dist", args))
      return gs.linalg.norm(dists, ord=2, axis=0)
  
imports: import math
  import geomstats.backend
  import geomstats.errors
  from geomstats.geometry.complex_manifold import ComplexManifold
  from geomstats.geometry.complex_riemannian_metric import ComplexRiemannianMetric
  from geomstats.geometry.manifold import Manifold
  from geomstats.geometry.riemannian_metric import RiemannianMetric
  from geomstats.vectorization import get_batch_shape
label_desc: Use image node_id 9 for calling the ProductRiemannianMetric obj's underlying member method code with example usage: obj.dist(point_a, point_b) and returns: gs