class_docstr: Create a one-hot categorical distribution object.
  
  Parameters
  ----------
  num_events : Int
      Number of events.
  prob : Tensor
      Probabilities of each event.
  logit : Tensor
      The log-odds of each event
class_docstr_tok: ["Create","a","one-hot","categorical","distribution","object",".","Parameters","--","--","--","--","--","num_events",":","Int","Number","of","events",".","prob",":","Tensor","Probabilities","of","each","event",".","logit",":","Tensor","The","log-odds","of","each","event"]
code: def enumerate_support(self):
      value = self._categorical.enumerate_support()
      return npx.one_hot(value, self.num_events)
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/one_hot_categorical.py
imports: from .distribution import Distribution
  from .categorical import Categorical
  from .utils import cached_property
  from .constraint import Simplex, Real
  from ....None import npx
label_desc: Use image node_id 10 for calling the OneHotCategorical obj's underlying member method code with example usage: obj.enumerate_support() and returns: npx