code: def sweep(
      run_config: DictConfig | ListConfig,
      device: int = 0,
      seed: int = 42,
      convert_openvino: bool = False,
  ) -> dict[str, str | float]:
      """Go over all the values mentioned in `grid_search` parameter of the benchmarking config.
  
      Args:
          run_config: (DictConfig | ListConfig, optional): Configuration for current run.
          device (int, optional): Name of the device on which the model is trained. Defaults to 0 "cpu".
          convert_openvino (bool, optional): Whether to convert the model to openvino format. Defaults to False.
  
      Returns:
          dict[str, str | float]: Dictionary containing the metrics gathered from the sweep.
      """
      seed_everything(seed, workers=True)
      # This assumes that `model_name` is always present in the sweep config.
      model_config = get_configurable_parameters(
          model_name=run_config.model_name
      )
      model_config.project.seed = seed
  
      model_config = cast(DictConfig, model_config)  # placate mypy
      for param in run_config.keys():
          # grid search keys are always assumed to be strings
          param = cast(str, param)  # placate mypy
          set_in_nested_config(model_config, param.split("."), run_config[param])  # type: ignore
  
      # convert image size to tuple in case it was updated by run config
      model_config = update_input_size_config(model_config)
  
      # Set device in config. 0 - cpu, [0], [1].. - gpu id
      if device != 0:
          model_config.trainer.devices = [device - 1]
          model_config.trainer.accelerator = "gpu"
      else:
          model_config.trainer.accelerator = "cpu"
  
      # Remove legacy flags
      for legacy_device in [
          "num_processes",
          "gpus",
          "ipus",
          "tpu_cores",
      ]:
          if legacy_device in model_config.trainer:
              model_config.trainer[legacy_device] = None
  
      if run_config.model_name in ["patchcore", "cflow"]:
          convert_openvino = (
              False  # `torch.cdist` is not supported by onnx version 11
          )
          # TODO Remove this line when issue #40 is fixed https://github.com/openvinotoolkit/anomalib/issues/40
          if model_config.model.input_size != (224, 224):
              return {}  # go to next run
  
      # Run benchmarking for current config
      model_metrics = get_single_model_metrics(
          model_config=model_config, openvino_metrics=convert_openvino
      )
      output = (
          f"One sweep run complete for model {model_config.model.name}"
      )
      output += (
          f" On category {model_config.dataset.category}"
          if model_config.dataset.category is not None
          else ""
      )
      output += str(model_metrics)
      logger.info(output)
  
      # Append configuration of current run to the collected metrics
      for key, value in run_config.items():
          # Skip adding model name to the dataframe
          if key != "model_name":
              model_metrics[key] = value
  
      # Add device name to list
      model_metrics["device"] = "gpu" if device > 0 else "cpu"
      model_metrics["model_name"] = run_config.model_name
  
      return model_metrics
  
imports: from __future__ import annotations
  import functools
  import io
  import logging
  import math
  import multiprocessing
  import sys
  import time
  import warnings
  from argparse import ArgumentParser
  from concurrent.futures import ProcessPoolExecutor, as_completed
  from datetime import datetime
  from pathlib import Path
  from tempfile import TemporaryDirectory
  from typing import cast
  import torch
  from omegaconf import DictConfig, ListConfig, OmegaConf
  from pytorch_lightning import Trainer, seed_everything
  from utils import upload_to_comet, upload_to_wandb, write_metrics
  from anomalib.config import get_configurable_parameters, update_input_size_config
  from anomalib.data import get_datamodule
  from anomalib.deploy import export
  from anomalib.deploy.export import ExportMode
  from anomalib.models import get_model
  from anomalib.utils.loggers import configure_logger
  from anomalib.utils.sweep import get_openvino_throughput, get_run_config, get_sweep_callbacks, get_torch_throughput, set_in_nested_config
label_desc: Use image node_id 7 for calling a global function with example usage: sweep(run_config, device, seed, convert_openvino) and returns: model_metrics, dict