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`.
docstr: Get the probability of sampling `1`.
  
  Returns
  -------
  Tensor
      Parameter tensor.
docstr_tok: ["Get","the","probability","of","sampling","`","1","`",".","Returns","--","--","--","-","Tensor","Parameter","tensor","."]
code: def prob(self):
      """Get the probability of sampling `1`.
  
      Returns
      -------
      Tensor
          Parameter tensor.
      """
      # pylint: disable=method-hidden
      return logit2prob(self.logit, True)
  
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 2 for calling the Binomial obj's underlying member method code with example usage: obj.prob() and returns: logit2prob