class_docstr: Class for the manifold of exponential distributions.
  
  This is the parameter space of exponential distributions
  i.e. the half-line of positive reals.
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docstr: Compute pdf associated to point.
  
  Compute the probability density function of the exponential
  distribution with parameters provided by point.
  
  Parameters
  ----------
  point : array-like, shape=[..., 1]
      Point representing an exponential distribution (scale).
  
  Returns
  -------
  pdf : function
      Probability density function of the exponential distribution with
      scale parameter provided by point.
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code: def point_to_pdf(self, point):
      """Compute pdf associated to point.
  
      Compute the probability density function of the exponential
      distribution with parameters provided by point.
  
      Parameters
      ----------
      point : array-like, shape=[..., 1]
          Point representing an exponential distribution (scale).
  
      Returns
      -------
      pdf : function
          Probability density function of the exponential distribution with
          scale parameter provided by point.
      """
  
      def pdf(x):
          """Generate parameterized function for exponential pdf.
  
          The pdf is parameterized by the scale parameter of the exponential,
          which is equal to 1 / lambda, where lambda is the rate parameter.
  
          Parameters
          ----------
          x : array-like, shape=[n_points,]
              Points at which to compute the probability density function.
  
          Returns
          -------
          pdf_at_x : array-like, shape=[..., n_points]
          """
          x = gs.reshape(gs.array(x), (-1,))
          pdf_at_x = point * gs.exp(-point * x)
          return gs.where(x >= 0, pdf_at_x, 0.0)
  
      return pdf
  
imports: from scipy.stats import expon
  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 7 for calling the ExponentialDistributions obj's underlying member method code with example usage: obj.point_to_pdf(point) and returns: pdf, gs