class_docstr: Create a multinomial distribution object.
  
  Parameters
  ----------
  num_events : int
      number of events.
  prob : Tensor
      probability of each event.
  logit : Tensor
      unnormalized probability of each event.
  total_count : int
      number of trials.
class_docstr_tok: ["Create","a","multinomial","distribution","object",".","Parameters","--","--","--","--","--","num_events",":","int","number","of","events",".","prob",":","Tensor","probability","of","each","event",".","logit",":","Tensor","unnormalized","probability","of","each","event",".","total_count",":","int","number","of","trials","."]
code: def mean(self):
      return self.prob * self.total_count
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/multinomial.py
imports: from numbers import Number
  from .distribution import Distribution
  from .one_hot_categorical import OneHotCategorical
  from .utils import cached_property, logit2prob, prob2logit, gammaln
  from .constraint import Simplex, Real, IntegerInterval
label_desc: Use image node_id 2 for calling the Multinomial obj's underlying member method code with example usage: obj.mean() and returns: unknown