# Generated from the displayed GPU Lab example; no GPU timings are measured.
# Run: python example.py --device cpu  (default; no CPU/GPU transfers)
# Or:  python example.py --device cuda (compatible NVIDIA GPU and PyTorch required)
# Explicit CPU-only calculations remain on CPU in either mode.
import argparse
import torch

_lab_parser = argparse.ArgumentParser(description="Run this GPU Lab PyTorch example.")
_lab_parser.add_argument("--device", choices=["cpu", "cuda"], default="cpu")
_lab_device = _lab_parser.parse_args().device
if _lab_device == "cuda" and not torch.cuda.is_available():
    _lab_parser.error("CUDA mode needs an available NVIDIA GPU and compatible PyTorch installation.")
if _lab_device == "cpu":
    print("CPU mode checks values and layouts; it does not perform CPU-GPU transfers.")

# The displayed example, with its CUDA device requests selected by --device.
import torch
x = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0], device=_lab_device)
y = x * 2

for _lab_name in ("y_cpu", "y_gpu", "z", "total", "y", "out", "c", "x_gpu", "x"):
    if _lab_name in globals():
        print(_lab_name + ":", globals()[_lab_name].detach().cpu())
        break