def __init__(
        self,
        model: "torchvision.models.detection.FasterRCNN" | None = None,
        input_shape: tuple[int, ...] = (-1, -1, -1),
        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",
        ),
        device_type: str = "gpu",
    ):
        """
        Initialization.

        :param model: Faster R-CNN 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 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.
        """
        import torchvision

        if model is None:  # pragma: no cover
            model = torchvision.models.detection.fasterrcnn_resnet50_fpn(
                pretrained=True, progress=True, num_classes=91, pretrained_backbone=True
            )

        super().__init__(
            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,
        )

        Initialization.

        :param model: Faster R-CNN 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 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.
        
art/estimators/object_detection/pytorch_faster_rcnn.py