def run(
        self,
        task_id: int | None = None,
    ) -> dict[str, Any]:
        """Run the benchmark job.

        This method executes the full benchmarking pipeline including model
        training and testing. It measures execution time for different stages and
        collects performance metrics.

        Args:
            task_id (int | None, optional): ID of the task when running in
                distributed mode. When provided, the job will use the specified
                device. Defaults to ``None``.

        Returns:
            dict[str, Any]: Dictionary containing benchmark results including:
                - Timing information (job, fit and test duration)
                - Model configuration
                - Performance metrics from testing
        """
        job_start_time = time.time()
        devices: str | list[int] = "auto"
        if task_id is not None:
            devices = [task_id]
            logger.info(f"Running job {self.model.__class__.__name__} with device {task_id}")
        with TemporaryDirectory() as temp_dir:
            seed_everything(self.seed)
            engine = Engine(
                accelerator=self.accelerator,
                devices=devices,
                default_root_dir=temp_dir,
            )
            fit_start_time = time.time()
            engine.fit(self.model, self.datamodule)
            test_start_time = time.time()
            test_results = engine.test(self.model, self.datamodule)
        job_end_time = time.time()
        durations = {
            "job_duration": job_end_time - job_start_time,
            "fit_duration": test_start_time - fit_start_time,
            "test_duration": job_end_time - test_start_time,
        }
        # TODO(ashwinvaidya17): Restore throughput
        # https://github.com/open-edge-platform/anomalib/issues/2054
        output = {
            "accelerator": self.accelerator,
            **durations,
            **self.flat_cfg,
            **test_results[0],
        }
        logger.info(f"Completed with result {output}")
        return output
Run the benchmark job.

        This method executes the full benchmarking pipeline including model
        training and testing. It measures execution time for different stages and
        collects performance metrics.

        Args:
            task_id (int | None, optional): ID of the task when running in
                distributed mode. When provided, the job will use the specified
                device. Defaults to ``None``.

        Returns:
            dict[str, Any]: Dictionary containing benchmark results including:
                - Timing information (job, fit and test duration)
                - Model configuration
                - Performance metrics from testing
        
src/anomalib/pipelines/benchmark/job.py