def resize(
        self,
        image: np.ndarray,
        size_divisor: int,
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        data_format: Optional[ChannelDimension] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> np.ndarray:
        """
        Resize the image, rounding the (height, width) dimensions down to the closest multiple of size_divisor.

        If the image is of dimension (3, 260, 170) and size_divisor is 32, the image will be resized to (3, 256, 160).

        Args:
            image (`np.ndarray`):
                The image to resize.
            size_divisor (`int`):
                The image is resized so its height and width are rounded down to the closest multiple of
                `size_divisor`.
            resample:
                `PIL.Image` resampling filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
            data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the output image. If `None`, the channel dimension format of the input
                image is used. 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 of the input image. If not set, 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.

        Returns:
            `np.ndarray`: The resized image.
        """
        height, width = get_image_size(image, channel_dim=input_data_format)
        # Rounds the height and width down to the closest multiple of size_divisor
        new_h = height // size_divisor * size_divisor
        new_w = width // size_divisor * size_divisor
        image = resize(
            image,
            (new_h, new_w),
            resample=resample,
            data_format=data_format,
            input_data_format=input_data_format,
            **kwargs,
        )
        return image

        Resize the image, rounding the (height, width) dimensions down to the closest multiple of size_divisor.

        If the image is of dimension (3, 260, 170) and size_divisor is 32, the image will be resized to (3, 256, 160).

        Args:
            image (`np.ndarray`):
                The image to resize.
            size_divisor (`int`):
                The image is resized so its height and width are rounded down to the closest multiple of
                `size_divisor`.
            resample:
                `PIL.Image` resampling filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
            data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the output image. If `None`, the channel dimension format of the input
                image is used. 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 of the input image. If not set, 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.

        Returns:
            `np.ndarray`: The resized image.
        
dpkd/transformers/src/transformers/models/glpn/image_processing_glpn.py