code: def test_binomial_vector(self):
      rng_R = random_state_type()
      n = tensor.lvector()
      prob = tensor.vector()
      post_r, out = binomial(rng_R, n=n, p=prob)
      assert out.ndim == 1
      f = compile.function(
          [rng_R, n, prob], [post_r, out], accept_inplace=True
      )
  
      n_val = [1, 2, 3]
      prob_val = np.asarray([0.1, 0.2, 0.3], dtype=config.floatX)
      rng = np.random.RandomState(utt.fetch_seed())
      numpy_rng = np.random.RandomState(utt.fetch_seed())
  
      # Arguments of size (3,)
      rng0, val0 = f(rng, n_val, prob_val)
      numpy_val0 = numpy_rng.binomial(n=n_val, p=prob_val)
      assert np.all(val0 == numpy_val0)
  
      # arguments of size (2,)
      rng1, val1 = f(rng0, n_val[:-1], prob_val[:-1])
      numpy_val1 = numpy_rng.binomial(n=n_val[:-1], p=prob_val[:-1])
      assert np.all(val1 == numpy_val1)
  
      # Specifying the size explicitly
      g = compile.function(
          [rng_R, n, prob],
          binomial(rng_R, n=n, p=prob, size=(3,)),
          accept_inplace=True,
      )
      rng2, val2 = g(rng1, n_val, prob_val)
      numpy_val2 = numpy_rng.binomial(n=n_val, p=prob_val, size=(3,))
      assert np.all(val2 == numpy_val2)
      self.assertRaises(ValueError, g, rng2, n_val[:-1], prob_val[:-1])
  
imports: from __future__ import absolute_import, print_function, division
  import numpy
  import pickle
  from theano.tests import unittest_tools
  from theano.tensor.raw_random import *
  from theano.tensor import raw_random, ivector, dvector, iscalar, dcol, dtensor3
  from theano import tensor
  from theano import compile, config, gof
label_desc: Use image node_id 27 for calling the T_random_function obj's underlying member method code with example usage: obj.test_binomial_vector() without return types