def METHOD_NAME(self):
    """ Test that we can record complex example inputs such as lists and dicts
    """
    class Sub(torch.nn.Module):
        def __init__(self):
            super().__init__()
            self.linear1 = torch.nn.Linear(5, 5)
            self.linear2 = torch.nn.Linear(5, 5)
        def forward(self, x, list_arg, dict_arg):
            x = self.linear1(x)
            x = self.linear2(x)
            return x
    class M(torch.nn.Module):
        def __init__(self):
            super().__init__()
            self.linear1 = torch.nn.Linear(5, 5)
            self.linear2 = torch.nn.Linear(5, 5)
            self.sub = Sub()
        def forward(self, x):
            x = self.linear1(x)
            x = self.linear2(x)
            x = self.sub(x, [x], {"3": x})
            return x
    example_inputs = (torch.rand(1, 5),)
    m = M().eval()
    fqn_to_example_inputs = get_fqn_to_example_inputs(m, example_inputs)
    assert "sub" in fqn_to_example_inputs
    assert isinstance(fqn_to_example_inputs["sub"][1], list)
    assert isinstance(fqn_to_example_inputs["sub"][2], dict) and \
        "3" in fqn_to_example_inputs["sub"][2]