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.
class_docstr_tok: ["Class","for","the","manifold","of","exponential","distributions",".","This","is","the","parameter","space","of","exponential","distributions","i.e",".","the","half-line","of","positive","reals","."]
docstr: Sample parameters of exponential distributions.
  
  The uniform distribution on [0, bound] is used.
  
  Parameters
  ----------
  n_samples : int
      Number of samples.
      Optional, default: 1.
  bound : float
      Right-end ot the segment where exponential parameters are sampled.
      Optional, default: 1.
  
  Returns
  -------
  samples : array-like, shape=[n_samples,]
      Sample of points representing exponential distributions.
docstr_tok: ["Sample","parameters","of","exponential","distributions",".","The","uniform","distribution","on","[","0",",","bound","]","is","used",".","Parameters","--","--","--","--","--","n_samples",":","int","Number","of","samples",".","Optional",",","default",":","1.","bound",":","float","Right-end","ot","the","segment","where","exponential","parameters","are","sampled",".","Optional",",","default",":","1",".","Returns","--","--","--","-","samples",":","array-like",",","shape=","[","n_samples",",","]","Sample","of","points","representing","exponential","distributions","."]
code: def random_point(self, n_samples=1, lower_bound=0.1, upper_bound=1.0):
      """Sample parameters of exponential distributions.
  
      The uniform distribution on [0, bound] is used.
  
      Parameters
      ----------
      n_samples : int
          Number of samples.
          Optional, default: 1.
      bound : float
          Right-end ot the segment where exponential parameters are sampled.
          Optional, default: 1.
  
      Returns
      -------
      samples : array-like, shape=[n_samples,]
          Sample of points representing exponential distributions.
      """
      size = (n_samples, self.dim) if n_samples != 1 else (self.dim,)
      return (upper_bound - lower_bound) * gs.random.rand(
          *size
      ) + lower_bound
  
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 4 for calling the ExponentialDistributions obj's underlying member method code with example usage: obj.random_point(n_samples, lower_bound, upper_bound) and returns: unknown