def prepare_image(self, image):
        from decaf.util import transform  # soft dep
        _JEFFNET_FLIP = True

        # first, extract the 256x256 center.
        image = transform.scale_and_extract(transform.as_rgb(image), 256)
        # convert to [0,255] float32
        image = image.astype(np.float32) * 255.
        if _JEFFNET_FLIP:
            # Flip the image if necessary, maintaining the c_contiguous order
            image = image[::-1, :].copy()
        # subtract the mean
        image -= self.net_._data_mean
        return image
Returns image of shape `(256, 256, 3)`, as expected by
dataset/ETHPy150Open dnouri/nolearn/nolearn/decaf.py/ConvNetFeatures.prepare_image