How to use the gradio.preprocessing_utils.decode_base64_to_image function in gradio

def rebuild_flagged(self, dir, msg):
        """
        Default rebuild method to decode a base64 image
        """
        out = msg['data']['output']
        im = preprocessing_utils.decode_base64_to_image(out)
        timestamp = datetime.datetime.now()
        filename = f'output_{timestamp.strftime("%Y-%m-%d-%H-%M-%S")}.png'
        im.save(f'{dir}/{filename}', 'PNG')
        return filename

def preprocess(self, inp):
        """
        Default preprocessing method for is to convert the picture to black and white and resize to be 48x48
        """
        im = preprocessing_utils.decode_base64_to_image(inp)
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            im = im.convert(self.image_mode)

        im = preprocessing_utils.resize_and_crop(im, (self.image_width, self.image_height))
        im = np.array(im).flatten()
        im = im * self.scale + self.shift
        if self.num_channels is None:
            array = im.reshape(1, self.image_width, self.image_height)
        else:
            array = im.reshape(1, self.image_width, self.image_height, self.num_channels)
        return array

def rebuild_flagged(self, dir, msg):
        """
        Default rebuild method to decode a base64 image
        """
        inp = msg['data']['input']
        im = preprocessing_utils.decode_base64_to_image(inp)
        timestamp = datetime.datetime.now()
        filename = f'input_{timestamp.strftime("%Y-%m-%d-%H-%M-%S")}.png'
        im.save(f'{dir}/{filename}', 'PNG')
        return filename

def preprocess(self, inp):
        """
        Default preprocessing method for the SketchPad is to convert the sketch to black and white and resize 28x28
        """
        im_transparent = preprocessing_utils.decode_base64_to_image(inp)
        im = Image.new("RGBA", im_transparent.size, "WHITE")  # Create a white background for the alpha channel
        im.paste(im_transparent, (0, 0), im_transparent)
        im = im.convert('L')
        if self.invert_colors:
            im = ImageOps.invert(im)
        im = im.resize((self.image_width, self.image_height))
        if self.flatten:
            array = np.array(im).flatten().reshape(1, self.image_width * self.image_height)
        else:
            array = np.array(im).flatten().reshape(1, self.image_width, self.image_height)
        array = array * self.scale + self.shift
        array = array.astype(self.dtype)
        return array