from __future__ import annotations
import torch
from torch import device
class GraphModule(torch.nn.Module):
    def forward(self, L_a_: "f32[10]", L_b_: "f32[10]"):
        l_a_ = L_a_
        l_b_ = L_b_
        
         # File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:14 in forward, code: x = a / (torch.abs(a) + 1)
        abs_1: "f32[10]" = torch.abs(l_a_)
        add: "f32[10]" = abs_1 + 1;  abs_1 = None
        x: "f32[10]" = l_a_ / add;  l_a_ = add = None
        
         # File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:15 in forward, code: if b.sum() < 0:
        sum_1: "f32[]" = l_b_.sum();  l_b_ = None
        lt: "b8[]" = sum_1 < 0;  sum_1 = None
        return (lt, x)
        