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 variance(self):
      a = self.alpha
      s = a.sum(-1, keepdims=True)
      return a * (s - a) / ((s + 1) * s**2)
  
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 6 for calling the Dirichlet obj's underlying member method code with example usage: obj.variance() and returns: unknown