class_docstr: Tangent bundle of a space.
class_docstr_tok: ["Tangent","bundle","of","a","space","."]
docstr: Sample random points on the manifold according to some distribution.
  
  If the manifold is compact, preferably a uniform distribution will be used.
  
  Parameters
  ----------
  n_samples : int
      Number of samples.
      Optional, default: 1.
  bound : float
      Bound of the interval in which to sample for non compact manifolds.
      Optional, default: 1.
  
  Returns
  -------
  samples : array-like, shape=[..., *point_shape]
      Points sampled on the manifold.
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code: def random_point(self, n_samples=1, bound=1.0):
      """Sample random points on the manifold according to some distribution.
  
      If the manifold is compact, preferably a uniform distribution will be used.
  
      Parameters
      ----------
      n_samples : int
          Number of samples.
          Optional, default: 1.
      bound : float
          Bound of the interval in which to sample for non compact manifolds.
          Optional, default: 1.
  
      Returns
      -------
      samples : array-like, shape=[..., *point_shape]
          Points sampled on the manifold.
      """
      space_point = self.space.random_point(n_samples, bound)
      space_tangent_vec = self.space.random_tangent_vec(space_point)
      return self._stack(space_point, space_tangent_vec)
  
imports: from joblib import Parallel, delayed
  import geomstats.backend
  from geomstats.geometry.base import Manifold
  from geomstats.geometry.riemannian_metric import RiemannianMetric
  from geomstats.vectorization import check_is_batch
label_desc: Use image node_id 6 for calling the TangentBundle obj's underlying member method code with example usage: obj.random_point(n_samples, bound) and returns: self