class_docstr: Class for space of one dimensional functions with norm 1.
  
  The tangent space is given by functions that have
  zero inner-product with the base point
  
  Parameters
  ----------
  domain : array-like, shape=[n_samples,]
      Grid points on the domain.
  
  References
  ----------
  .. [Sea2016] Srivastava, Anuj, and Eric P. Klassen.
      Functional and shape data analysis.
      Vol. 1. New York: Springer, 2016.
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docstr: Project a point to the infinite dimensional hypersphere.
  
  Parameters
  ----------
  point : array-like, shape=[..., n_samples]
      Discrete evaluation of a function.
  
  Returns
  -------
  projected_point : array-like, shape=[..., n_samples]
      Point projected to the hypersphere.
docstr_tok: ["Project","a","point","to","the","infinite","dimensional","hypersphere",".","Parameters","--","--","--","--","--","point",":","array-like",",","shape=","[","...",",","n_samples","]","Discrete","evaluation","of","a","function",".","Returns","--","--","--","-","projected_point",":","array-like",",","shape=","[","...",",","n_samples","]","Point","projected","to","the","hypersphere","."]
code: def projection(self, point):
      """Project a point to the infinite dimensional hypersphere.
  
      Parameters
      ----------
      point : array-like, shape=[..., n_samples]
          Discrete evaluation of a function.
  
      Returns
      -------
      projected_point : array-like, shape=[..., n_samples]
          Point projected to the hypersphere.
      """
      norm = self.metric.norm(point)
      return gs.einsum("...,...i->...i", 1.0 / norm, point)
  
file_path: geomstats/geomstats/geometry/functions.py
imports: import math
  import geomstats.algebra_utils
  import geomstats.backend
  from geomstats.geometry.manifold import Manifold
  from geomstats.geometry.riemannian_metric import RiemannianMetric
label_desc: Use image node_id 3 for calling the HilbertSphere obj's underlying member method code with example usage: obj.projection(point) and returns: gs