class_docstr: A Poisson random variable.
class_docstr_tok: ["A","Poisson","random","variable","."]
docstr: Evaluate the probability density function at x.
  
  Parameters
  ----------
  x : array-like, shape=[n_samples, *support_shape]
      Sample points at which to compute the probability density function.
  point : array-like, shape=[..., *space.shape]
      Point representing a distribution.
  
  Returns
  -------
  pdf_at_x : array-like, shape=[..., n_samples, *support_shape]
      Values of pdf at x for each value of the parameters provided
      by point.
docstr_tok: ["Evaluate","the","probability","density","function","at","x.","Parameters","--","--","--","--","--","x",":","array-like",",","shape=","[","n_samples",",","*","support_shape","]","Sample","points","at","which","to","compute","the","probability","density","function",".","point",":","array-like",",","shape=","[","...",",","*","space.shape","]","Point","representing","a","distribution",".","Returns","--","--","--","-","pdf_at_x",":","array-like",",","shape=","[","...",",","n_samples",",","*","support_shape","]","Values","of","pdf","at","x","for","each","value","of","the","parameters","provided","by","point","."]
code: def pdf(self, x, point):
      """Evaluate the probability density function at x.
  
      Parameters
      ----------
      x : array-like, shape=[n_samples, *support_shape]
          Sample points at which to compute the probability density function.
      point : array-like, shape=[..., *space.shape]
          Point representing a distribution.
  
      Returns
      -------
      pdf_at_x : array-like, shape=[..., n_samples, *support_shape]
          Values of pdf at x for each value of the parameters provided
          by point.
      """
      return gs.from_numpy(
          poisson.pmf(x, point),
      )
  
imports: from scipy.special import factorial
  from scipy.stats import poisson
  import geomstats.backend
  from geomstats.geometry.base import VectorSpaceOpenSet
  from geomstats.geometry.euclidean import Euclidean
  from geomstats.geometry.riemannian_metric import RiemannianMetric
  from geomstats.information_geometry.base import InformationManifoldMixin, ScipyUnivariateRandomVariable
label_desc: Use image node_id 3 for calling the PoissonDistributionsRandomVariable obj's underlying member method code with example usage: obj.pdf(x, point) and returns: gs