def preprocess(
        self,
        images: Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]],
        do_resize: Optional[bool] = None,
        size_divisor: Optional[int] = None,
        resample=None,
        do_rescale: Optional[bool] = None,
        return_tensors: Optional[Union[TensorType, str]] = None,
        data_format: ChannelDimension = ChannelDimension.FIRST,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> BatchFeature:
        """
        Preprocess the given images.

        Args:
            images (`PIL.Image.Image` or `TensorType` or `List[np.ndarray]` or `List[TensorType]`):
                Images to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
                passing in images with pixel values between 0 and 1, set `do_normalize=False`.
            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
                Whether to resize the input such that the (height, width) dimensions are a multiple of `size_divisor`.
            size_divisor (`int`, *optional*, defaults to `self.size_divisor`):
                When `do_resize` is `True`, images are resized so their height and width are rounded down to the
                closest multiple of `size_divisor`.
            resample (`PIL.Image` resampling filter, *optional*, defaults to `self.resample`):
                `PIL.Image` resampling filter to use if resizing the image e.g. `PILImageResampling.BILINEAR`. Only has
                an effect if `do_resize` is set to `True`.
            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
                Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.).
            return_tensors (`str` or `TensorType`, *optional*):
                The type of tensors to return. Can be one of:
                    - `None`: Return a list of `np.ndarray`.
                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
                The channel dimension format for the output image. Can be one of:
                    - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                    - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the input image. If unset, the channel dimension format is inferred
                from the input image. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
        """
        do_resize = do_resize if do_resize is not None else self.do_resize
        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
        size_divisor = size_divisor if size_divisor is not None else self.size_divisor
        resample = resample if resample is not None else self.resample

        if do_resize and size_divisor is None:
            raise ValueError("size_divisor is required for resizing")

        images = make_list_of_images(images)

        if not valid_images(images):
            raise ValueError("Invalid image(s)")

        # All transformations expect numpy arrays.
        images = [to_numpy_array(img) for img in images]

        if is_scaled_image(images[0]) and do_rescale:
            logger.warning_once(
                "It looks like you are trying to rescale already rescaled images. If the input"
                " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
            )

        if input_data_format is None:
            # We assume that all images have the same channel dimension format.
            input_data_format = infer_channel_dimension_format(images[0])

        if do_resize:
            images = [
                self.resize(image, size_divisor=size_divisor, resample=resample, input_data_format=input_data_format)
                for image in images
            ]

        if do_rescale:
            images = [self.rescale(image, scale=1 / 255, input_data_format=input_data_format) for image in images]

        images = [
            to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
        ]

        data = {"pixel_values": images}
        return BatchFeature(data=data, tensor_type=return_tensors)

        Preprocess the given images.

        Args:
            images (`PIL.Image.Image` or `TensorType` or `List[np.ndarray]` or `List[TensorType]`):
                Images to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
                passing in images with pixel values between 0 and 1, set `do_normalize=False`.
            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
                Whether to resize the input such that the (height, width) dimensions are a multiple of `size_divisor`.
            size_divisor (`int`, *optional*, defaults to `self.size_divisor`):
                When `do_resize` is `True`, images are resized so their height and width are rounded down to the
                closest multiple of `size_divisor`.
            resample (`PIL.Image` resampling filter, *optional*, defaults to `self.resample`):
                `PIL.Image` resampling filter to use if resizing the image e.g. `PILImageResampling.BILINEAR`. Only has
                an effect if `do_resize` is set to `True`.
            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
                Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.).
            return_tensors (`str` or `TensorType`, *optional*):
                The type of tensors to return. Can be one of:
                    - `None`: Return a list of `np.ndarray`.
                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
                The channel dimension format for the output image. Can be one of:
                    - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                    - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the input image. If unset, the channel dimension format is inferred
                from the input image. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
        
dpkd/transformers/src/transformers/models/glpn/image_processing_glpn.py