class_docstr: Mixin for manifolds of probability distributions.
class_docstr_tok: ["Mixin","for","manifolds","of","probability","distributions","."]
docstr: Compute cdf associated to point.
  
  Compute the cumulative density function of the probability
  distribution with parameters provided by point.
  
  Parameters
  ----------
  point : array-like, shape=[..., dim]
      Point representing a probability distribution.
  
  Returns
  -------
  cdf : function
      Cumulative density function of the probability distribution with
      parameters provided by point.
docstr_tok: ["Compute","cdf","associated","to","point",".","Compute","the","cumulative","density","function","of","the","probability","distribution","with","parameters","provided","by","point",".","Parameters","--","--","--","--","--","point",":","array-like",",","shape=","[","...",",","dim","]","Point","representing","a","probability","distribution",".","Returns","--","--","--","-","cdf",":","function","Cumulative","density","function","of","the","probability","distribution","with","parameters","provided","by","point","."]
code: def point_to_cdf(self, point):
      """Compute cdf associated to point.
  
      Compute the cumulative density function of the probability
      distribution with parameters provided by point.
  
      Parameters
      ----------
      point : array-like, shape=[..., dim]
          Point representing a probability distribution.
  
      Returns
      -------
      cdf : function
          Cumulative density function of the probability distribution with
          parameters provided by point.
      """
      raise NotImplementedError(
          "`point_to_cdf` has not yet been implemented."
      )
  
imports: import math
  import geomstats.backend
  from geomstats.vectorization import get_batch_shape
label_desc: Use image node_id 4 for calling the InformationManifoldMixin obj's underlying member method code with example usage: obj.point_to_cdf(point) without return types