def normalize(
        self,
        image: np.ndarray,
        mean: Union[float, Iterable[float]],
        std: Union[float, Iterable[float]],
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> np.ndarray:
        """
        Normalize an image. image = (image - image_mean) / image_std.

        Args:
            image (`np.ndarray`):
                Image to normalize.
            mean (`float` or `Iterable[float]`):
                Image mean to use for normalization.
            std (`float` or `Iterable[float]`):
                Image standard deviation to use for normalization.
            data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format for the output image. If unset, the channel dimension format of the input
                image is used. 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.
            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.

        Returns:
            `np.ndarray`: The normalized image.
        """
        return normalize(
            image, mean=mean, std=std, data_format=data_format, input_data_format=input_data_format, **kwargs
        )

        Normalize an image. image = (image - image_mean) / image_std.

        Args:
            image (`np.ndarray`):
                Image to normalize.
            mean (`float` or `Iterable[float]`):
                Image mean to use for normalization.
            std (`float` or `Iterable[float]`):
                Image standard deviation to use for normalization.
            data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format for the output image. If unset, the channel dimension format of the input
                image is used. 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.
            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.

        Returns:
            `np.ndarray`: The normalized image.
        
dpkd/transformers/src/transformers/image_processing_utils.py