def resize(
    image: np.ndarray,
    size: Tuple[int, int],
    resample: "PILImageResampling" = None,
    reducing_gap: Optional[int] = None,
    data_format: Optional[ChannelDimension] = None,
    return_numpy: bool = True,
    input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
    """
    Resizes `image` to `(height, width)` specified by `size` using the PIL library.

    Args:
        image (`np.ndarray`):
            The image to resize.
        size (`Tuple[int, int]`):
            The size to use for resizing the image.
        resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`):
            The filter to user for resampling.
        reducing_gap (`int`, *optional*):
            Apply optimization by resizing the image in two steps. The bigger `reducing_gap`, the closer the result to
            the fair resampling. See corresponding Pillow documentation for more details.
        data_format (`ChannelDimension`, *optional*):
            The channel dimension format of the output image. If unset, will use the inferred format from the input.
        return_numpy (`bool`, *optional*, defaults to `True`):
            Whether or not to return the resized image as a numpy array. If False a `PIL.Image.Image` object is
            returned.
        input_data_format (`ChannelDimension`, *optional*):
            The channel dimension format of the input image. If unset, will use the inferred format from the input.

    Returns:
        `np.ndarray`: The resized image.
    """
    requires_backends(resize, ["vision"])

    resample = resample if resample is not None else PILImageResampling.BILINEAR

    if not len(size) == 2:
        raise ValueError("size must have 2 elements")

    # For all transformations, we want to keep the same data format as the input image unless otherwise specified.
    # The resized image from PIL will always have channels last, so find the input format first.
    if input_data_format is None:
        input_data_format = infer_channel_dimension_format(image)
    data_format = input_data_format if data_format is None else data_format

    # To maintain backwards compatibility with the resizing done in previous image feature extractors, we use
    # the pillow library to resize the image and then convert back to numpy
    do_rescale = False
    if not isinstance(image, PIL.Image.Image):
        do_rescale = _rescale_for_pil_conversion(image)
        image = to_pil_image(image, do_rescale=do_rescale, input_data_format=input_data_format)
    height, width = size
    # PIL images are in the format (width, height)
    resized_image = image.resize((width, height), resample=resample, reducing_gap=reducing_gap)

    if return_numpy:
        resized_image = np.array(resized_image)
        # If the input image channel dimension was of size 1, then it is dropped when converting to a PIL image
        # so we need to add it back if necessary.
        resized_image = np.expand_dims(resized_image, axis=-1) if resized_image.ndim == 2 else resized_image
        # The image is always in channels last format after converting from a PIL image
        resized_image = to_channel_dimension_format(
            resized_image, data_format, input_channel_dim=ChannelDimension.LAST
        )
        # If an image was rescaled to be in the range [0, 255] before converting to a PIL image, then we need to
        # rescale it back to the original range.
        resized_image = rescale(resized_image, 1 / 255) if do_rescale else resized_image
    return resized_image

    Resizes `image` to `(height, width)` specified by `size` using the PIL library.

    Args:
        image (`np.ndarray`):
            The image to resize.
        size (`Tuple[int, int]`):
            The size to use for resizing the image.
        resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`):
            The filter to user for resampling.
        reducing_gap (`int`, *optional*):
            Apply optimization by resizing the image in two steps. The bigger `reducing_gap`, the closer the result to
            the fair resampling. See corresponding Pillow documentation for more details.
        data_format (`ChannelDimension`, *optional*):
            The channel dimension format of the output image. If unset, will use the inferred format from the input.
        return_numpy (`bool`, *optional*, defaults to `True`):
            Whether or not to return the resized image as a numpy array. If False a `PIL.Image.Image` object is
            returned.
        input_data_format (`ChannelDimension`, *optional*):
            The channel dimension format of the input image. If unset, will use the inferred format from the input.

    Returns:
        `np.ndarray`: The resized image.
    
dpkd/transformers/src/transformers/image_transforms.py