Description
Lets developers author ONNX functions and models from Python. It helps machine-learning tooling express model logic, generate ONNX graphs, and bridge Python code with interoperable model formats.
Generated models should be tested with representative inputs. A valid ONNX graph can still produce incorrect or unsafe predictions if the model logic is wrong.