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: Normalize tangent vector at a given point.
  
  Parameters
  ----------
  vector : array-like, shape=[..., dim]
      Tangent vector at base_point.
  base_point : array-like, shape=[..., dim]
      Point.
  
  Returns
  -------
  normalized_vector : array-like, shape=[..., dim]
      Unit tangent vector at base_point.
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code: def normalize(self, vector, base_point):
      """Normalize tangent vector at a given point.
  
      Parameters
      ----------
      vector : array-like, shape=[..., dim]
          Tangent vector at base_point.
      base_point : array-like, shape=[..., dim]
          Point.
  
      Returns
      -------
      normalized_vector : array-like, shape=[..., dim]
          Unit tangent vector at base_point.
      """
      norm = self.norm(vector, base_point)
      norm = gs.where(norm == 0, gs.ones(norm.shape), norm)
      indices = "ijk"[: self._space.point_ndim]
      return gs.einsum(
          f"...{indices},...->...{indices}", vector, 1 / norm
      )
  
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 13 for calling the RiemannianMetric obj's underlying member method code with example usage: obj.normalize(vector, base_point) and returns: gs