class_docstr: Create a Dirichlet distribution object.
  
  Parameters
  ----------
  alpha : Tensor or scalar
     Shape parameter of the distribution
class_docstr_tok: ["Create","a","Dirichlet","distribution","object",".","Parameters","--","--","--","--","--","alpha",":","Tensor","or","scalar","Shape","parameter","of","the","distribution"]
code: def sample_n(self, size=None):
      alpha = self.alpha
      if size is None:
          return self.sample()
      gamma_samples = np.random.gamma(
          alpha, 1, sample_n_shape_converter(size)
      )
      s = gamma_samples.sum(-1, keepdims=True)
      return _clip_float_eps(gamma_samples / s)
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/dirichlet.py
imports: from .exp_family import ExponentialFamily
  from .constraint import Positive, Simplex
  from .utils import gammaln, digamma, sample_n_shape_converter, _clip_float_eps
  from ....None import np
label_desc: Use image node_id 3 for calling the Dirichlet obj's underlying member method code with example usage: obj.sample_n(size) and returns: _clip_float_eps, self