Const tensor vs input in AOT FX graph

Is there a way to discern self.consttensor from input in AOT FX graph? For the test example, is there any attribute or method that tells that arg3_1 is the input, and arg2_1 is the self.consttensor. The input always goes in the end of the arg list, but the model could have variable number of inputs

class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.linear = torch.nn.Linear(20, 30)
        self.consttensor = torch.randn(1, 30)
    def forward(self, input):
        out = self.linear(input)
        out = out + self.consttensor
        return out
opcode         name    target              args                 kwargs
-------------  ------  ------------------  -------------------  --------
placeholder    arg0_1  arg0_1              ()                   {}
placeholder    arg1_1  arg1_1              ()                   {}
placeholder    arg2_1  arg2_1              ()                   {}
placeholder    arg3_1  arg3_1              ()                   {}
call_function  t       aten.t.default      (arg0_1,)            {}
call_function  addmm   aten.addmm.default  (arg1_1, arg3_1, t)  {}
call_function  add     aten.add.Tensor     (addmm, arg2_1)      {}
output         output  output              ((add,),)            {}