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 from the binomial distribution.
  
  Sample from the binomial distribution with parameter provided by point.
  
  Parameters
  ----------
  point : array-like, shape=[..., dim]
      Point representing a binomial distribution.
  n_samples : int
      Number of points to sample with for each parameter in point.
      Optional, default: 1.
  
  Returns
  -------
  samples : array-like, shape=[..., n_samples, dim]
      Sample from binomial distributions.
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code: def sample(self, point, n_samples=1):
      """Sample from the binomial distribution.
  
      Sample from the binomial distribution with parameter provided by point.
  
      Parameters
      ----------
      point : array-like, shape=[..., dim]
          Point representing a binomial distribution.
      n_samples : int
          Number of points to sample with for each parameter in point.
          Optional, default: 1.
  
      Returns
      -------
      samples : array-like, shape=[..., n_samples, dim]
          Sample from binomial distributions.
      """
      return self._scp_rv.rvs(point, n_samples)
  
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 6 for calling the BinomialDistributions obj's underlying member method code with example usage: obj.sample(point, n_samples) and returns: self