Avoid hardcoding specific device types like 'cuda' in AI code. Instead, use device-agnostic approaches such as device type variables or accelerator detection functions. This ensures code runs efficiently across different hardware accelerators (CUDA, ROCm, XPU, etc.) without modification. Examples: ```python # Instead of this: x = torch.randn(100, 100, device='cuda') # Do this: x = torch.randn(100, 100, device=GPU_TYPE) # Or for more dynamic detection: device = torch.accelerator.current_accelerator().type if torch.accelerator.current_accelerator() else "cpu" x = torch.randn(100, 100, device=device) ``` This approach helps maintain compatibility with various AI hardware acceleration platforms and simplifies testing across multiple device types. For common test cases, prefer using constants like `GPU_TYPE` defined in testing utilities, and for production code, use the accelerator API to detect available devices at runtime.