def METHOD_NAME():
    net = Model()
    net.eval()
    torch.manual_seed(0)
    x = torch.rand(1, 12, 64, 64)
    a = net(x)
    # export torchscript
    mod = torch.jit.trace(net, x)
    mod.save("test_pnnx_fuse_conv2d_batchnorm2d.pt")
    # torchscript to pnnx
    import os
    os.system("../src/pnnx test_pnnx_fuse_conv2d_batchnorm2d.pt inputshape=[1,12,64,64]")
    # pnnx inference
    import test_pnnx_fuse_conv2d_batchnorm2d_pnnx
    b = test_pnnx_fuse_conv2d_batchnorm2d_pnnx.test_inference()
    return torch.allclose(a, b, 1e-4, 1e-4)