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 mean(self):
      return self.n * self.prob
  
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 4 for calling the Binomial obj's underlying member method code with example usage: obj.mean() and returns: unknown