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    Ultralytics YOLO NAS Predictor for object detection.

    This class extends the `DetectionPredictor` from Ultralytics engine and is responsible for post-processing the
    raw predictions generated by the YOLO NAS models. It applies operations like non-maximum suppression and
    scaling the bounding boxes to fit the original image dimensions.

    Attributes:
        args (Namespace): Namespace containing various configurations for post-processing including confidence threshold,
            IoU threshold, agnostic NMS flag, maximum detections, and class filtering options.
        model (torch.nn.Module): The YOLO NAS model used for inference.
        batch (list): Batch of inputs for processing.

    Examples:
        >>> from ultralytics import NAS
        >>> model = NAS("yolo_nas_s")
        >>> predictor = model.predictor

        Assume that raw_preds, img, orig_imgs are available
        >>> results = predictor.postprocess(raw_preds, img, orig_imgs)

    Notes:
        Typically, this class is not instantiated directly. It is used internally within the `NAS` class.
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        Postprocess NAS model predictions to generate final detection results.

        This method takes raw predictions from a YOLO NAS model, converts bounding box formats, and applies
        post-processing operations to generate the final detection results compatible with Ultralytics
        result visualization and analysis tools.

        Args:
            preds_in (list): Raw predictions from the NAS model, typically containing bounding boxes and class scores.
            img (torch.Tensor): Input image tensor that was fed to the model, with shape (B, C, H, W).
            orig_imgs (list | torch.Tensor | np.ndarray): Original images before preprocessing, used for scaling
                coordinates back to original dimensions.

        Returns:
            (list): List of Results objects containing the processed predictions for each image in the batch.

        Examples:
            >>> predictor = NAS("yolo_nas_s").predictor
            >>> results = predictor.postprocess(raw_preds, img, orig_imgs)
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