class_docstr: Mixin for manifolds of probability distributions.
class_docstr_tok: ["Mixin","for","manifolds","of","probability","distributions","."]
docstr: Compute pdf associated to point.
  
  Compute the probability density function of the probability
  distribution with parameters provided by point.
  
  Parameters
  ----------
  point : array-like, shape=[..., dim]
      Point representing a probability distribution.
  
  Returns
  -------
  pdf : function
      Probability density function of the probability distribution with
      parameters provided by point.
docstr_tok: ["Compute","pdf","associated","to","point",".","Compute","the","probability","density","function","of","the","probability","distribution","with","parameters","provided","by","point",".","Parameters","--","--","--","--","--","point",":","array-like",",","shape=","[","...",",","dim","]","Point","representing","a","probability","distribution",".","Returns","--","--","--","-","pdf",":","function","Probability","density","function","of","the","probability","distribution","with","parameters","provided","by","point","."]
code: def point_to_pdf(self, point):
      """Compute pdf associated to point.
  
      Compute the probability density function of the probability
      distribution with parameters provided by point.
  
      Parameters
      ----------
      point : array-like, shape=[..., dim]
          Point representing a probability distribution.
  
      Returns
      -------
      pdf : function
          Probability density function of the probability distribution with
          parameters provided by point.
      """
      raise NotImplementedError(
          "`point_to_pdf` has not yet been implemented."
      )
  
imports: import math
  import geomstats.backend
  from geomstats.vectorization import get_batch_shape
label_desc: Use image node_id 3 for calling the InformationManifoldMixin obj's underlying member method code with example usage: obj.point_to_pdf(point) without return types