def _run_no_search(
        self,
        input_model_config: ModelConfig,
        input_model_id: str,
        accelerator_spec: "AcceleratorSpec",
        output_dir: Path,
    ):
        """Run all the registered Olive pass flows in no-search mode."""
        output_model_dir = Path(output_dir)
        self._get_search_space_objectives(input_model_config, input_model_id, accelerator_spec)

        # Compute pas configs
        self._compute_no_search_pass_configs(accelerator_spec)

        # run all the passes in the pass flow
        pass_flow = list(self.computed_passes_configs.keys())
        logger.debug("Running %s with no search ...", pass_flow)
        should_prune, signal, model_ids = self._run_passes(input_model_config, input_model_id, accelerator_spec)

        if should_prune:
            failed_pass = pass_flow[len(model_ids)]
            logger.warning("Flow %s is pruned due to failed or invalid config for pass '%s'", pass_flow, failed_pass)
            return Footprint()

        if signal is not None and not self.skip_saving_artifacts:
            results_path = output_model_dir / "metrics.json"
            with open(results_path, "w") as f:
                json.dump(signal.to_json(), f, indent=4)
            logger.info("Saved evaluation results of output model to %s", results_path)

        output_footprints = self.footprints[accelerator_spec].create_footprints_by_model_ids([model_ids[-1]])
        if not self.skip_saving_artifacts:
            output_footprints.to_file(output_dir / "output_footprints.json")
        return output_footprints
Run all the registered Olive pass flows in no-search mode.
olive/engine/engine.py