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: Give the distance between two farthest points.
  
  Distance between the two points that are farthest away from each other
  in points.
  
  Parameters
  ----------
  points : array-like, shape=[..., dim]
      Points.
  
  Returns
  -------
  diameter : float
      Distance between two farthest points.
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code: def diameter(self, points):
      """Give the distance between two farthest points.
  
      Distance between the two points that are farthest away from each other
      in points.
  
      Parameters
      ----------
      points : array-like, shape=[..., dim]
          Points.
  
      Returns
      -------
      diameter : float
          Distance between two farthest points.
      """
      diameter = 0.0
      n_points = points.shape[0]
  
      for i in range(n_points - 1):
          dist_to_neighbors = self.dist(
              points[i, :], points[i + 1 :, :]
          )
          dist_to_farthest_neighbor = gs.amax(dist_to_neighbors)
          diameter = gs.maximum(diameter, dist_to_farthest_neighbor)
  
      return diameter
  
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 19 for calling the RiemannianMetric obj's underlying member method code with example usage: obj.diameter(points) and returns: diameter