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 __init__(self, alpha, validate_args=None):
      self.alpha = alpha
      super(Dirichlet, self).__init__(
          event_dim=1, validate_args=validate_args
      )
  
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 1 to create a new Dirichlet object from inherited base classes: ExponentialFamily with example: obj = Dirichlet(alpha, validate_args)