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 broadcast_to(self, batch_shape):
      new_instance = self.__new__(type(self))
      new_instance._categorical = self._categorical.broadcast_to(
          batch_shape
      )
      new_instance.num_events = self.num_events
      super(OneHotCategorical, new_instance).__init__(
          event_dim=self.event_dim, validate_args=False
      )
      new_instance._validate_args = self._validate_args
      return new_instance
  
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 9 for calling the OneHotCategorical obj's underlying member method code with example usage: obj.broadcast_to(batch_shape) and returns: new_instance