def test_poisson(self):
        # Check over two calls to see if the random state is correctly updated.
        rng_R = random_state_type()
        # Use non-default parameters, and larger dimensions because of
        # the integer nature of the result
        post_r, out = poisson(rng_R, lam=5, size=(11, 8))

        f = compile.function(
                [compile.In(rng_R,
                    value=numpy.random.RandomState(utt.fetch_seed()),
                    update=post_r, mutable=True)],
                [out], accept_inplace=True)

        numpy_rng = numpy.random.RandomState(utt.fetch_seed())
        val0 = f()
        val1 = f()
        numpy_val0 = numpy_rng.poisson(5, size=(11, 8))
        numpy_val1 = numpy_rng.poisson(5, size=(11, 8))
        print(val0)
        print(numpy_val0)
        print(val1)
        print(numpy_val1)
        self.assertTrue(numpy.allclose(val0, numpy_val0))
        self.assertTrue(numpy.allclose(val1, numpy_val1))
Test that raw_random.poisson generates the same
dataset/ETHPy150Open rizar/attention-lvcsr/libs/Theano/theano/tensor/tests/test_raw_random.py/T_random_function.test_poisson