code: def get_openvino_throughput(
      model_path: str | Path, test_dataset: Dataset
  ) -> float:
      """Runs the generated OpenVINO model on a dummy dataset to get throughput.
  
      Args:
          model_path (str, Path): Path to folder containing the OpenVINO models. It then searches `model.xml` in folder.
          test_dataset (Dataset): The test dataset used as a reference for the mock dataset.
  
      Returns:
          float: Inference throughput
      """
      model_path = Path(model_path)
  
      inferencer = OpenVINOInferencer(
          path=model_path / "weights" / "openvino" / "model.xml",
          metadata=model_path
          / "weights"
          / "openvino"
          / "metadata.json",
      )
      start_time = time.time()
      for image_path in test_dataset.samples.image_path:
          inferencer.predict(image_path)
  
      # get throughput
      inference_time = time.time() - start_time
      throughput = len(test_dataset) / inference_time
  
      return throughput
  
file_path: anomalib/src/anomalib/utils/sweep/helpers/inference.py
imports: from __future__ import annotations
  import time
  from pathlib import Path
  import torch
  from torch.utils.data import Dataset
  from anomalib.deploy import OpenVINOInferencer, TorchInferencer
label_desc: Use image node_id 2 for calling a global function with example usage: get_openvino_throughput(model_path, test_dataset) and returns: throughput