Demonstrate the feasibility of combining depyf with openxla backend

depyf_openxla.py

import torch
import torch_xla.core.xla_model as xm

@torch.compile(backend="openxla")
def toy_example(a, b):
    x = a / (torch.abs(a) + 1)
    if b.sum() < 0:
        b = b * -1
    return x * b

def main():
    device = xm.xla_device()
    a = torch.randn(10).to(device=device)*10
    b = torch.randn(10).to(device=device)*10
    for _ in range(4):
        print(toy_example(a, b).to(device="cpu"))

if __name__ == "__main__":
    import depyf
    with depyf.prepare_debug("depyf_debug_dir"):
        main()