Resize ``img`` to ``size``.
    As default, the shape of input image has to be (height, width, channel).

    Args:
        img (numpy.ndarray): Input image.
        size (tuple of int): Output shape. The order is (width, height).
        interpolate (str): Interpolation method.
            This argument is depend on the backend.
            If you want to specify this argument, you should pay much attention to which backend you use now.
            What you can select is below:
             - pil backend: ["nearest", "box", "bilinear", "hamming", "bicubic", "lanczos"].
             - cv2 backend: ["nearest", "bilinear", "bicubic", "lanczos"].
            Default is "bilinear" for both backends.
        channel_first (bool):
            If True, the shape of the output array is (channel, height, width) for RGB image. Default is False.
    Returns:
         numpy.ndarray

def imresize(img, size, interpolate="bilinear", channel_first=False, **kwargs):
    """
    Resize ``img`` to ``size``.
    As default, the shape of input image has to be (height, width, channel).

    Args:
        img (numpy.ndarray): Input image.
        size (tuple of int): Output shape. The order is (width, height).
        interpolate (str): Interpolation method.
            This argument is depend on the backend.
            If you want to specify this argument, you should pay much attention to which backend you use now.
            What you can select is below:
             - pil backend: ["nearest", "box", "bilinear", "hamming", "bicubic", "lanczos"].
             - cv2 backend: ["nearest", "bilinear", "bicubic", "lanczos"].
            Default is "bilinear" for both backends.
        channel_first (bool):
            If True, the shape of the output array is (channel, height, width) for RGB image. Default is False.
    Returns:
         numpy.ndarray
    """

    return backend_manager.module.imresize(img, size, interpolate=interpolate, channel_first=channel_first, **kwargs)