class_docstr: Private class for the n-dimensional hypersphere.
  
  Class for the n-dimensional hypersphere embedded in the
  (n+1)-dimensional Euclidean space.
  
  By default, points are parameterized by their extrinsic
  (n+1)-coordinates. For dimensions 1 and 2, this can be changed with the
  `default_coords_type` parameter. For dimensions 1 (the circle),
  the intrinsic coordinates correspond angles in radians, with 0. mapping
  to point [1., 0.]. For dimension 2, the intrinsic coordinates are the
  spherical coordinates from the north pole, i.e. where angles [0., 0.]
  correspond to point [0., 0., 1.].
  
  Parameters
  ----------
  dim : int
      Dimension of the hypersphere.
  
  default_coords_type : str, {'extrinsic', 'intrinsic'}
      Type of representation for dimensions 1 and 2.
docstr: Sample in the hypersphere from the uniform distribution.
  
  Parameters
  ----------
  n_samples : int
      Number of samples.
      Optional, default: 1.
  bound : unused
  
  Returns
  -------
  samples : array-like, shape=[..., dim + 1]
      Points sampled on the hypersphere.
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code: def random_point(self, n_samples=1, bound=1.0):
      """Sample in the hypersphere from the uniform distribution.
  
      Parameters
      ----------
      n_samples : int
          Number of samples.
          Optional, default: 1.
      bound : unused
  
      Returns
      -------
      samples : array-like, shape=[..., dim + 1]
          Points sampled on the hypersphere.
      """
      return self.random_uniform(n_samples)
  
imports: import logging
  import math
  from itertools import product
  from scipy.stats import beta
  import geomstats.algebra_utils
  import geomstats.backend
  from geomstats.geometry.base import LevelSet
  from geomstats.geometry.euclidean import Euclidean
  from geomstats.geometry.riemannian_metric import RiemannianMetric
  from geomstats.vectorization import get_batch_shape, repeat_out
label_desc: Use image node_id 17 for calling the _Hypersphere obj's underlying member method code with example usage: obj.random_point(n_samples, bound) and returns: self