def __init__(
        self,
        model: "torch.nn.Module",
        input_shape: tuple[int, ...] = (3, 416, 416),
        optimizer: "torch.optim.Optimizer" | None = None,
        clip_values: "CLIP_VALUES_TYPE" | None = None,
        channels_first: bool = True,
        preprocessing_defences: "Preprocessor" | list["Preprocessor"] | None = None,
        postprocessing_defences: "Postprocessor" | list["Postprocessor"] | None = None,
        preprocessing: "PREPROCESSING_TYPE" = None,
        attack_losses: tuple[str, ...] = (
            "loss_classifier",
            "loss_box_reg",
            "loss_objectness",
            "loss_rpn_box_reg",
        ),
        detector_type: Literal["Faster-RCNN", "YOLO", "DETR"] = "YOLO",
        num_lines: int = 3,
        confidence_threshold: float = 0.3,
        iou_threshold: float = 0.5,
        prune_threshold: float = 0.5,
        epsilon: float = 0.1,
        device_type: str = "gpu",
        verbose: bool = False,
    ):
        """
        Create an ObjectSeeker classifier.

        :param model: Object detection model. The output of the model is `list[dict[str, torch.Tensor]]`,
                      one for each input image. The fields of the dict are as follows:

                      - boxes [N, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and
                        0 <= y1 < y2 <= H.
                      - labels [N]: the labels for each image.
                      - scores [N]: the scores of each prediction.
        :param input_shape: The shape of one input sample.
        :param optimizer: The optimizer for training the classifier.
        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
               maximum values allowed for features. If floats are provided, these will be used as the range of all
               features. If arrays are provided, each value will be considered the bound for a feature, thus
               the shape of clip values needs to match the total number of features.
        :param channels_first: Set channels first or last.
        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
               used for data preprocessing. The first value will be subtracted from the input. The input will then
               be divided by the second one.
        :param attack_losses: Tuple of any combination of strings of loss components: 'loss_classifier', 'loss_box_reg',
                              'loss_objectness', and 'loss_rpn_box_reg'.
        :param detector_type: The type of object detector being used: 'Faster-RCNN' | 'YOLO' | 'DETR'
        :param num_lines: The number of divisions both vertically and horizontally to make masked predictions.
        :param confidence_threshold: The confidence threshold to discard bounding boxes.
        :param iou_threshold: The IoU threshold to discard overlapping bounding boxes.
        :param prune_threshold: The IoA threshold for pruning and duplicated bounding boxes.
        :param epsilon: The maximum distance between bounding boxes when merging using DBSCAN.
        :param device_type: Type of device to be used for model and tensors, if `cpu` run on CPU, if `gpu` run on GPU
                            if available otherwise run on CPU.
        :param verbose: Show progress bars.
        """
        super().__init__(
            model=model,
            clip_values=clip_values,
            channels_first=channels_first,
            preprocessing_defences=preprocessing_defences,
            postprocessing_defences=postprocessing_defences,
            preprocessing=preprocessing,
            device_type=device_type,
            num_lines=num_lines,
            confidence_threshold=confidence_threshold,
            iou_threshold=iou_threshold,
            prune_threshold=prune_threshold,
            epsilon=epsilon,
            verbose=verbose,
        )

        self._input_shape = input_shape
        self._channels_first = channels_first
        self._optimizer = optimizer
        self._attack_losses = attack_losses
        self.detector_type = detector_type

        detector_ctor: type
        if detector_type == "Faster-RCNN":
            detector_ctor = PyTorchFasterRCNN
        elif detector_type == "YOLO":
            detector_ctor = PyTorchYolo
        elif detector_type == "DETR":
            detector_ctor = PyTorchDetectionTransformer
        else:
            detector_ctor = PyTorchObjectDetector

        self.detector = detector_ctor(
            model=model,
            input_shape=input_shape,
            optimizer=optimizer,
            clip_values=clip_values,
            channels_first=channels_first,
            preprocessing_defences=preprocessing_defences,
            postprocessing_defences=postprocessing_defences,
            preprocessing=preprocessing,
            attack_losses=attack_losses,
            device_type=device_type,
        )

        Create an ObjectSeeker classifier.

        :param model: Object detection model. The output of the model is `list[dict[str, torch.Tensor]]`,
                      one for each input image. The fields of the dict are as follows:

                      - boxes [N, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and
                        0 <= y1 < y2 <= H.
                      - labels [N]: the labels for each image.
                      - scores [N]: the scores of each prediction.
        :param input_shape: The shape of one input sample.
        :param optimizer: The optimizer for training the classifier.
        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
               maximum values allowed for features. If floats are provided, these will be used as the range of all
               features. If arrays are provided, each value will be considered the bound for a feature, thus
               the shape of clip values needs to match the total number of features.
        :param channels_first: Set channels first or last.
        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
               used for data preprocessing. The first value will be subtracted from the input. The input will then
               be divided by the second one.
        :param attack_losses: Tuple of any combination of strings of loss components: 'loss_classifier', 'loss_box_reg',
                              'loss_objectness', and 'loss_rpn_box_reg'.
        :param detector_type: The type of object detector being used: 'Faster-RCNN' | 'YOLO' | 'DETR'
        :param num_lines: The number of divisions both vertically and horizontally to make masked predictions.
        :param confidence_threshold: The confidence threshold to discard bounding boxes.
        :param iou_threshold: The IoU threshold to discard overlapping bounding boxes.
        :param prune_threshold: The IoA threshold for pruning and duplicated bounding boxes.
        :param epsilon: The maximum distance between bounding boxes when merging using DBSCAN.
        :param device_type: Type of device to be used for model and tensors, if `cpu` run on CPU, if `gpu` run on GPU
                            if available otherwise run on CPU.
        :param verbose: Show progress bars.
        
art/estimators/certification/object_seeker/pytorch.py