class_docstr: Pullback metric induced by a diffeomorphism.
class_docstr_tok: ["Pullback","metric","induced","by","a","diffeomorphism","."]
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):
      """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.
      """
      image_point = self.diffeo.diffeomorphism(base_point)
      return self.image_space.metric.norm(
          self.diffeo.tangent_diffeomorphism(
              vector,
              base_point=base_point,
              image_point=image_point,
          ),
          image_point,
      )
  
imports: import geomstats.backend
  from geomstats.geometry.riemannian_metric import RiemannianMetric
  from geomstats.numerics.geodesic import ExpODESolver, LogShootingSolver
  from geomstats.numerics.ivp import GSIVPIntegrator
  from geomstats.vectorization import check_is_batch
label_desc: Use image node_id 5 for calling the PullbackDiffeoMetric obj's underlying member method code with example usage: obj.norm(vector, base_point) and returns: self