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: Compute norm of a vector.
  
  Norm of a vector associated to the inner product
  at the tangent space at a base point.
  
  Note: This only works for positive-definite
  Riemannian metrics and inner products.
  
  Parameters
  ----------
  vector : array-like, shape=[..., dim]
      Vector.
  base_point : array-like, shape=[..., dim]
      Base point.
      Optional, default: None.
  
  Returns
  -------
  norm : array-like, shape=[...,]
      Norm.
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code: def norm(self, vector, base_point=None):
      """Compute norm of a vector.
  
      Norm of a vector associated to the inner product
      at the tangent space at a base point.
  
      Note: This only works for positive-definite
      Riemannian metrics and inner products.
  
      Parameters
      ----------
      vector : array-like, shape=[..., dim]
          Vector.
      base_point : array-like, shape=[..., dim]
          Base point.
          Optional, default: None.
  
      Returns
      -------
      norm : array-like, shape=[...,]
          Norm.
      """
      sq_norm = self.squared_norm(vector, base_point)
      return gs.sqrt(sq_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 12 for calling the RiemannianMetric obj's underlying member method code with example usage: obj.norm(vector, base_point) and returns: gs