class_docstr: Class for the manifold of multinomial distributions.
  
  This is the set of `n+1`-tuples of positive reals that sum up to one,
  i.e. the `n`-simplex. Each point is the parameter of a multinomial
  distribution, i.e. gives the probabilities of $n$ different outcomes
  in a single experiment.
  
  Attributes
  ----------
  dim : int
      Dimension of the parameter manifold of multinomial distributions.
      The number of outcomes is dim + 1.
  embedding_manifold : Manifold
      Embedding manifold.
docstr: Generate parameters of multinomial distributions.
  
  The Dirichlet distribution on the simplex is used
  to generate parameters.
  
  Parameters
  ----------
  n_samples : int
      Number of samples.
      Optional, default: 1.
  
  Returns
  -------
  samples : array-like, shape=[..., dim + 1]
      Sample of points representing multinomial distributions.
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code: def random_point(self, n_samples=1):
      """Generate parameters of multinomial distributions.
  
      The Dirichlet distribution on the simplex is used
      to generate parameters.
  
      Parameters
      ----------
      n_samples : int
          Number of samples.
          Optional, default: 1.
  
      Returns
      -------
      samples : array-like, shape=[..., dim + 1]
          Sample of points representing multinomial distributions.
      """
      samples = gs.from_numpy(
          dirichlet.rvs(gs.ones(self.dim + 1), size=n_samples)
      )
  
      return samples[0] if n_samples == 1 else samples
  
imports: from scipy.stats import dirichlet, multinomial
  import geomstats.backend
  from geomstats.algebra_utils import from_vector_to_diagonal_matrix
  from geomstats.geometry.base import LevelSet
  from geomstats.geometry.euclidean import Euclidean
  from geomstats.geometry.hypersphere import Hypersphere
  from geomstats.geometry.riemannian_metric import RiemannianMetric
  from geomstats.information_geometry.base import InformationManifoldMixin, ScipyMultivariateRandomVariable
  from geomstats.vectorization import repeat_out
label_desc: Use image node_id 6 for calling the MultinomialDistributions obj's underlying member method code with example usage: obj.random_point(n_samples) and returns: unknown