docstr: Sample n times with probability of success p for each trial and
  return the number of successes.
  
  If the size argument is ambiguous on the number of dimensions,
  ndim may be a plain integer to supplement the missing information.
docstr_tok: ["Sample","n","times","with","probability","of","success","p","for","each","trial","and","return","the","number","of","successes",".","If","the","size","argument","is","ambiguous","on","the","number","of","dimensions",",","ndim","may","be","a","plain","integer","to","supplement","the","missing","information","."]
code: def binomial(
      self, size=None, n=1, p=0.5, ndim=None, dtype="int64", prob=None
  ):
      """
      Sample n times with probability of success p for each trial and
      return the number of successes.
  
      If the size argument is ambiguous on the number of dimensions,
      ndim may be a plain integer to supplement the missing information.
  
      """
      if prob is not None:
          p = prob
          print(
              "DEPRECATION WARNING: the parameter prob to the binomal fct have been renamed to p to have the same name as numpy.",
              file=sys.stderr,
          )
      return self.gen(binomial, size, n, p, ndim=ndim, dtype=dtype)
  
imports: from __future__ import absolute_import, print_function, division
  import sys
  from copy import copy
  import numpy
  from six import string_types
  from six.moves import reduce, xrange
  import theano
  from theano import tensor
  from theano.tensor import opt
  from theano import gof
  from theano.compile import optdb
label_desc: Use image node_id 1 for calling the RandomStreamsBase obj's underlying member method code with example usage: obj.binomial(size, n, p, ndim, dtype, prob) and returns: self