class_docstr: Class that defines operations using a hyperbolic metric.
class_docstr_tok: ["Class","that","defines","operations","using","a","hyperbolic","metric","."]
docstr: Compute the squared norm of a vector.
  
  Squared norm of a vector associated with the inner-product
  at the tangent space at a base point.
  
  Parameters
  ----------
  vector : array-like, shape=[..., dim + 1]
      Vector on the tangent space of the hyperbolic space at base point.
  base_point : array-like, shape=[..., dim + 1], optional
      Point in hyperbolic space in extrinsic coordinates.
  
  Returns
  -------
  sq_norm : array-like, shape=[...,]
      Squared norm of the vector.
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code: def squared_norm(self, vector, base_point=None):
      """Compute the squared norm of a vector.
  
      Squared norm of a vector associated with the inner-product
      at the tangent space at a base point.
  
      Parameters
      ----------
      vector : array-like, shape=[..., dim + 1]
          Vector on the tangent space of the hyperbolic space at base point.
      base_point : array-like, shape=[..., dim + 1], optional
          Point in hyperbolic space in extrinsic coordinates.
  
      Returns
      -------
      sq_norm : array-like, shape=[...,]
          Squared norm of the vector.
      """
      return self._space.embedding_space.metric.squared_norm(vector)
  
imports: import math
  import geomstats.algebra_utils
  import geomstats.backend
  from geomstats.geometry._hyperbolic import HyperbolicMetric, _Hyperbolic
  from geomstats.geometry.base import LevelSet
  from geomstats.geometry.minkowski import Minkowski
  from geomstats.vectorization import repeat_out
label_desc: Use image node_id 3 for calling the HyperboloidMetric obj's underlying member method code with example usage: obj.squared_norm(vector, base_point) and returns: self