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 __init__(self, n=1, prob=None, logit=None, validate_args=None):
      if (n < 0) or (n % 1 != 0):
          raise ValueError(
              "Expect `n` to be non-negative integer, received n={}".format(
                  n
              )
          )
      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.n = n
      super(Binomial, self).__init__(
          event_dim=0, validate_args=validate_args
      )
  
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 1 to create a new Binomial object from inherited base classes: Distribution with example: obj = Binomial(n, prob, logit, validate_args)