code: def random_integers(
      random_state, 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.
  
      If size is None, the output shape will be determined by the shapes
      of low and high.
  
      """
      low = tensor.as_tensor_variable(low)
      high = tensor.as_tensor_variable(high)
      ndim, size, bcast = _infer_ndim_bcast(ndim, size, low, high)
      op = RandomFunction(
          random_integers_helper,
          tensor.TensorType(dtype=dtype, broadcastable=bcast),
      )
      return op(random_state, size, low, high)
  
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 7 for calling a global function with example usage: random_integers(random_state, size, low, high, ndim, dtype) and returns: op