def __init__(
        self,
        height: int,
        width: int,
        channels_first: bool = False,
        label_type: str = "classification",
        interpolation: int = cv2.INTER_LINEAR,
        clip_values: "CLIP_VALUES_TYPE" | None = None,
        apply_fit: bool = True,
        apply_predict: bool = False,
        verbose: bool = False,
    ):
        """
        Create an instance of ImageResize.

        :param height: The height of the resized image.
        :param width: The width of the resized image.
        :param channels_first: Set channels first or last.
        :param label_type: String defining the label type. Currently supported: `classification`, `object_detection`
        :param interpolation: The desired method to resize the image defined by the `cv2::InterpolationFlags` enum.
        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
               for features.
        :param apply_fit: True if applied during fitting/training.
        :param apply_predict: True if applied during predicting.
        :param verbose: Show progress bars.
        """
        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
        self.height = height
        self.width = width
        self.channels_first = channels_first
        self.label_type = label_type
        self.interpolation = interpolation
        self.clip_values = clip_values
        self.verbose = verbose
        self._check_params()

        Create an instance of ImageResize.

        :param height: The height of the resized image.
        :param width: The width of the resized image.
        :param channels_first: Set channels first or last.
        :param label_type: String defining the label type. Currently supported: `classification`, `object_detection`
        :param interpolation: The desired method to resize the image defined by the `cv2::InterpolationFlags` enum.
        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
               for features.
        :param apply_fit: True if applied during fitting/training.
        :param apply_predict: True if applied during predicting.
        :param verbose: Show progress bars.
        
art/preprocessing/image/image_resize/numpy.py