Fixed the test for the optimization that removes switches with constant

theano/tensor/tests/test_opt.py

@@ -2284,7 +2284,9 @@ class test_local_remove_switch_const_cond(unittest.TestCase):
                 z = theano.tensor.switch(0, x, y)
                 f = theano.function([x,y], z, mode=self.mode)
                 assert len([node.op for node in f.maker.env.toposort() if
-                            isinstance(node.op,theano.tensor.Elemwise) ]) == 0
+                            ( isinstance(node.op,theano.tensor.Elemwise)
+                           and isinstance(node.op.scalar_op,
+                                          theano.scalar.basic.Switch))]) == 0
                 vx = numpy.array([[1,2,3],[ 4, 5, 6]], dtype=dtype1)
                 vy = numpy.array([[7,8,9],[10,11,12]], dtype=dtype2)
                 assert numpy.all(f(vx,vy) == vy)
@@ -2298,7 +2300,9 @@ class test_local_remove_switch_const_cond(unittest.TestCase):
                 z = theano.tensor.switch(1, x, y)
                 f = theano.function([x,y], z, mode=self.mode)
                 assert len([node.op for node in f.maker.env.toposort() if
-                            isinstance(node.op,theano.tensor.Elemwise) ]) == 0
+                            ( isinstance(node.op,theano.tensor.Elemwise)
+                           and isinstance(node.op.scalar_op,
+                                          theano.scalar.basic.Switch))]) == 0
                 vx = numpy.array([[1,2,3],[ 4, 5, 6]], dtype=dtype1)
                 vy = numpy.array([[7,8,9],[10,11,12]], dtype=dtype2)
                 assert numpy.all(f(vx,vy) == vx)