class_docstr: Class for the Fisher information metric on multinomial distributions.
  
  The Fisher information metric on the $n$-simplex of multinomial
  distributions parameters can be obtained as the pullback metric of the
  $n$-sphere using the componentwise square root.
  
  References
  ----------
  .. [K2003] R. E. Kass. The Geometry of Asymptotic Inference. Statistical
      Science, 4(3): 188 - 234, 1989.
docstr: Send point of the simplex to the sphere.
  
  The map takes the square root of each component.
  
  Parameters
  ----------
  point : array-like, shape=[..., dim + 1]
      Point on the simplex.
  
  Returns
  -------
  point_sphere : array-like, shape=[..., dim + 1]
      Point on the sphere.
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code: def simplex_to_sphere(point):
      """Send point of the simplex to the sphere.
  
      The map takes the square root of each component.
  
      Parameters
      ----------
      point : array-like, shape=[..., dim + 1]
          Point on the simplex.
  
      Returns
      -------
      point_sphere : array-like, shape=[..., dim + 1]
          Point on the sphere.
      """
      return point ** (1 / 2)
  
imports: from scipy.stats import dirichlet, multinomial
  import geomstats.backend
  from geomstats.algebra_utils import from_vector_to_diagonal_matrix
  from geomstats.geometry.base import LevelSet
  from geomstats.geometry.euclidean import Euclidean
  from geomstats.geometry.hypersphere import Hypersphere
  from geomstats.geometry.riemannian_metric import RiemannianMetric
  from geomstats.information_geometry.base import InformationManifoldMixin, ScipyMultivariateRandomVariable
  from geomstats.vectorization import repeat_out
label_desc: Use image node_id 3 for calling the MultinomialMetric obj's underlying member method code with example usage: obj.simplex_to_sphere(point) and returns: unknown