class_docstr: Create a binomial distribution object.
  
  Parameters
  ----------
  n : scalar
      Non-negative interger of Bernoulli trials to stop.
  prob : Tensor or scalar, default None
      Probability of sampling `1`.
  logit : Tensor or scalar, default None
      The log-odds of sampling `1`.
code: def broadcast_to(self, batch_shape):
      new_instance = self.__new__(type(self))
      if "prob" in self.__dict__:
          new_instance.prob = np.broadcast_to(self.prob, batch_shape)
      else:
          new_instance.logit = np.broadcast_to(self.logit, batch_shape)
      new_instance.n = self.n
      super(Binomial, 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/binomial.py
imports: from .distribution import Distribution
  from .utils import prob2logit, logit2prob, cached_property, sample_n_shape_converter
  from .utils import gammaln
  from .constraint import Interval, Real, NonNegativeInteger
  from ....None import np, npx
label_desc: Use image node_id 6 for calling the Binomial obj's underlying member method code with example usage: obj.broadcast_to(batch_shape) and returns: new_instance