//  [python]
def test_add_relu_fusion(self):
        class M(torch.nn.Module):
            def __init__(self, relu_op):
                super().__init__()
                self.relu_op = relu_op

            def forward(self, a, b, c):
                tmp = torch.add(a, b)
                x = self.relu_op(tmp)
                d = torch.add(a, c)
                return x + d
        a = torch.rand((7, 11))
        a = a * -10
        a = a + 5
        b = torch.rand((7, 11))
        c = torch.rand((7, 11))
        m = torch.jit.script(M(torch.relu))
        orig_res = m(a, b, c)
        torch._C._jit_pass_fuse_add_relu(m.graph)
        buffer = io.BytesIO()
        torch.jit.save(m, buffer)
        buffer.seek(0)
        m = torch.jit.load(buffer)
        new_res = m(a, b, c)
        FileCheck().check_not("aten::relu(") \
            .check("aten::_add_relu(") \
            .run(m.graph)
        torch.testing.assert_close(orig_res, new_res)

        # add, relu_
        a = torch.rand((7, 11))
        a = a * -10
        a = a + 5
        b = torch.rand((7, 11))
        c = torch.rand((7, 11))
        m = torch.jit.script(M(torch.relu_))
        orig_res = m(a, b, c)
        torch._C._jit_pass_fuse_add_relu(m.graph)
        buffer = io.BytesIO()
        torch.jit.save(m, buffer)
        buffer.seek(0)
        m = torch.jit.load(buffer)
        new_res = m(a, b, c)
        FileCheck().check_not("aten::relu_(") \
            .check("aten::_add_relu(") \
            .run(m.graph)
        torch.testing.assert_close(orig_res, new_res)

        class Madd_(torch.nn.Module):
            def __init__(self, relu_op):
                super().__init__()
                self.relu_op = relu_op

            def forward(self, a, b):
                x = a.add_(b)
                x = self.relu_op(x)
                return x

        # add_, relu_
        a = torch.rand((7, 11))
        a = a * -10
        a = a + 5
        b = torch.rand((7, 11))
        # Because in place add_ will overwrite a
        a_copy = a.clone()
        m = torch.jit.script(Madd_(torch.relu_))
        orig_res = m(a, b)
        torch._C._jit_pass_fuse_add_relu(m.graph)
        buffer = io.BytesIO()
        torch.jit.save(m, buffer)
        buffer.seek(0)
        m = torch.jit.load(buffer)
        new_res = m(a_copy, b)
        FileCheck().check_not("aten::add_(") \
            .check_not("aten::relu_(") \
            .check("aten::_add_relu_(") \
            .run(m.graph)
        torch.testing.assert_close(orig_res, new_res)
        # Since _add_relu_ does inplace mutation ensure
        # a_copy is modified
        torch.testing.assert_close(orig_res, a_copy)

        class Madd_out(torch.nn.Module):
            def __init__(self, relu_op):
                super().__init__()
                self.relu_op = relu_op

            def forward(self, a, b):
                x = torch.add(a, b, out=a)
                x = self.relu_op(x)
                return x
        a = torch.rand((7, 11))
        a = a * -10
        a = a + 5
        b = torch.rand((7, 11))

        # add_out, relu_
        a = torch.rand((7, 11))
        a = a * -10
        a = a + 5
        b = torch.rand((7, 11))
        # Because in place add_ will overwrite a
        a_copy = a.clone()
        m = torch.jit.script(Madd_out(torch.relu_))
        orig_res = m(a, b)
        torch._C._jit_pass_fuse_add_relu(m.graph)
        buffer = io.BytesIO()
        torch.jit.save(m, buffer)
        buffer.seek(0)
        m = torch.jit.load(buffer)
        new_res = m(a_copy, b)
        FileCheck().check_not("aten::add(") \
            .check_not("aten::relu_(") \
            .check("aten::_add_relu(") \
            .run(m.graph)
        torch.testing.assert_close(orig_res, new_res)
        # Since _add_relu_ with out=a does inplace mutation ensure
        # a_copy is modified
        torch.testing.assert_close(orig_res, a_copy)