docstr: Sample a random integer between low and high, both inclusive.
  
  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","a","random","integer","between","low","and","high",",","both","inclusive",".","If","the","size","argument","is","ambiguous","on","the","number","of","dimensions",",","ndim","may","be","a","plain","integer","to","supplement","the","missing","information","."]
code: def random_integers(
      self, size=None, low=0, high=1, ndim=None, dtype="int64"
  ):
      """
      Sample a random integer between low and high, both inclusive.
  
      If the size argument is ambiguous on the number of dimensions,
      ndim may be a plain integer to supplement the missing information.
  
      """
      return self.gen(
          random_integers, size, low, high, 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 4 for calling the RandomStreamsBase obj's underlying member method code with example usage: obj.random_integers(size, low, high, ndim, dtype) and returns: self