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 __init__(
      self,
      num_events,
      prob=None,
      logit=None,
      total_count=1,
      validate_args=None,
  ):
      if not isinstance(total_count, Number):
          raise ValueError("Expect `total_conut` to be scalar value")
      self.total_count = total_count
      if (prob is None) == (logit is None):
          raise ValueError(
              "Either `prob` or `logit` must be specified, but not both. "
              + "Received prob={}, logit={}".format(prob, logit)
          )
      if prob is not None:
          self.prob = prob
      else:
          self.logit = logit
      self._categorical = OneHotCategorical(
          num_events, prob, logit, validate_args
      )
      super(Multinomial, self).__init__(
          event_dim=1, validate_args=validate_args
      )
  
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 1 to create a new Multinomial object from inherited base classes: Distribution with example: obj = Multinomial(num_events, prob, logit, total_count, validate_args)