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 log_prob(self, value):
      if self._validate_args:
          self._validate_samples(value)
      lgamma = gammaln()
      alpha = self.alpha
      return (
          (np.log(value) * (alpha - 1.0)).sum(-1)
          + lgamma(alpha.sum(-1))
          - lgamma(alpha).sum(-1)
      )
  
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 4 for calling the Dirichlet obj's underlying member method code with example usage: obj.log_prob(value) and returns: unknown