Is there a way to find loop?

I’m trying to migrate pytorch model into our own c++ framework. Currently I’m using torch.jit.trace to get the graph of the model, which is convenient, I am able to get most ops through the node name like ‘aten::add’, ‘aten::conv2d’, but for the following model:

class A(torch.nn.Module):
def init(self):
self.layer1 = torch.nn.Linear(10,20)
self.layer2 = torch.nn.Linear(20,10)

def forward(self,xx):
    # xx=torch.randn(50,3,10)
    for i in range(xx.shape[0]):
        x = xx[i]
        x = self.layer1(x)
        x = self.layer2(x)
    return x

I have trouble finding if the model is using the for loop, and which node(s) is within the for loop. Is there a way to find out the loop part?