class_docstr: Class for the manifold of binomial distributions.
  
  This is the parameter space of binomial distributions
  i.e. the half-line of positive reals.
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docstr: Sample parameters of binomial distributions.
  
  The uniform distribution on [0, 1] is used.
  
  Parameters
  ----------
  n_samples : int
      Number of samples.
      Optional, default: 1.
  
  Returns
  -------
  samples : array-like, shape=[..., dim]
      Sample of points representing binomial distributions.
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code: def random_point(self, n_samples=1):
      """Sample parameters of binomial distributions.
  
      The uniform distribution on [0, 1] is used.
  
      Parameters
      ----------
      n_samples : int
          Number of samples.
          Optional, default: 1.
  
      Returns
      -------
      samples : array-like, shape=[..., dim]
          Sample of points representing binomial distributions.
      """
      size = (n_samples, self.dim) if n_samples != 1 else (self.dim,)
      return gs.random.rand(*size)
  
imports: from scipy.special import factorial
  from scipy.stats import binom
  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 BinomialDistributions obj's underlying member method code with example usage: obj.random_point(n_samples) and returns: gs