def METHOD_NAME():
    """
    Runs the YOLOv5 quantization example.
    """
    # Step 1: Initialize model from the PyTorch Hub.
    repo_path = str(ROOT.joinpath("yolov5"))
    model = torch.hub.load(repo_path, "yolov5m", pretrained=True, source="local")
    model.eval()
    # Step 2: Converts PyTorch model to the OpenVINO model.
    ov_model = convert_torch_to_openvino(model)
    # Step 3: Create calibration dataset.
    data_source = create_data_source()
    # Step 4: Apply quantization algorithm.
    # Define the transformation method. This method should take a data item returned
    # per iteration through the `data_source` object and transform it into the model's
    # expected input that can be used for the model inference.
    def transform_fn(data_item):
        images, *_ = data_item
        images = images.float()
        images = images / 255
        images = images.cpu().detach().numpy()
        return images
    # Wrap framework-specific data source into the `nncf.Dataset` object.
    calibration_dataset = nncf.Dataset(data_source, transform_fn)
    quantized_model = nncf.quantize(ov_model, calibration_dataset, preset=nncf.QuantizationPreset.MIXED)
    # Step 5: Save the quantized model.
    ir_qmodel_xml = MODEL_DIR / "yolov5m_quantized.xml"
    ir_qmodel_bin = MODEL_DIR / "yolov5m_quantized.bin"
    ov.serialize(quantized_model, str(ir_qmodel_xml), str(ir_qmodel_bin))
    # Step 6: Compare the accuracy of the original and quantized models.
    print("Checking the accuracy of the original model:")
    metrics = validation_fn(
        data=DATASET_CONFIG,
        weights=MODEL_DIR.joinpath("yolov5m.xml"),  # Already supports.
        batch_size=1,
        workers=1,
        plots=False,
        device="cpu",
        iou_thres=0.65,
    )
    print(f"mAP@.5 = {metrics[0][2]}")
    print(f"mAP@.5:.95 = {metrics[0][3]}")
    print("Checking the accuracy of the quantized model:")
    metrics = validation_fn(
        data=DATASET_CONFIG,
        weights=ir_qmodel_xml,  # Already supports.
        batch_size=1,
        workers=1,
        plots=False,
        device="cpu",
        iou_thres=0.65,
    )
    print(f"mAP@.5 = {metrics[0][2]}")
    print(f"mAP@.5:.95 = {metrics[0][3]}")
    # Step 7: Compare Performance of the original and quantized models
    # benchmark_app -m yolov5m_quantization/yolov5m.xml -d CPU -api async
    # benchmark_app -m yolov5m_quantization/yolov5m_quantized.xml -d CPU -api async