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 log_prob(self, value):
      if self._validate_args:
          self._validate_samples(value)
      lgamma = gammaln()
      binomal_coef = (
          lgamma(self.n + 1)
          - lgamma(1 + value)
          - lgamma(self.n - value + 1)
      )
      # log(prob) may have numerical issue.
      unnormalized_log_prob = value * np.log(self.prob) + (
          self.n - value
      ) * np.log1p(-self.prob)
      return binomal_coef + unnormalized_log_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 7 for calling the Binomial obj's underlying member method code with example usage: obj.log_prob(value) and returns: unknown