Which is better code1 or code 2. 1: def adjust_values(img: np.ndarray) -> tuple[np.ndarray, int, int]:
    """Adjust the values of a grayscale image."""
    try:
        # Calculate modal white value in the range [250, 256)
        hist_white, bins_white = np.histogram(img[img >= 250], bins=6, range=(250, 256))
        modal_white = int(bins_white[np.argmax(hist_white)])

        # Calculate modal black value in the range [0, 61)
        hist_black, bins_black = np.histogram(img[img < 61], bins=61, range=(0, 61))
        modal_black = int(bins_black[np.argmax(hist_black)])

        # Convert image to float for processing
        adjusted = img.astype(np.float64)

        # Create a mask for extreme original values (0 and 255)
        mask = (img == 0) | (img == 255)

        # Exclude extreme values from the white adjustment
        white_adjust_mask = ~mask

        # Scale image if modal white is not at maximum intensity
        if modal_white != 255:
            adjusted[white_adjust_mask] *= 255.0 / modal_white
            adjusted = np.clip(adjusted, 0, 255)

        # After white value adjustment, identify new extreme values
        new_extremes = (adjusted == 0) | (adjusted == 255)

        # Combine masks to exclude all extreme values from black adjustment
        total_extremes = mask | new_extremes
        black_adjust_mask = ~total_extremes

        # Adjust image based on modal black value
        if modal_black:
            adjusted[black_adjust_mask] = (
                adjusted[black_adjust_mask] - modal_black
            ) * (255.0 / (255 - modal_black))
            adjusted = np.clip(adjusted, 0, 255)

            gamma = 1.0 - (modal_black / 255.0)
            adjusted[black_adjust_mask] = (
                np.power(adjusted[black_adjust_mask] / 255.0, gamma) * 255
            )

        # Convert back to unsigned 8-bit integer
        adjusted = adjusted.astype(np.uint8)

        return adjusted, modal_black, modal_white

    except ValueError as e:
        log_error(f"Error adjusting values for image: {e}")
        return img, 0, 255 2: def adjust_values(img: np.ndarray) -> tuple[np.ndarray, int, int]:
    """Adjust the values of a grayscale image with minimal rounding errors."""
    try:
        # Calculate modal white value in the range [250, 256)
        hist_white, bins_white = np.histogram(img[img >= 250], bins=6, range=(250, 256))
        modal_white = int(bins_white[np.argmax(hist_white)])

        # Calculate modal black value in the range [0, 61)
        hist_black, bins_black = np.histogram(img[img < 61], bins=61, range=(0, 61))
        modal_black = int(bins_black[np.argmax(hist_black)])

        # Convert image to uint16 for processing to prevent overflow
        adjusted = img.astype(np.uint16)

        # Create masks for processing
        mask = (img == 0) | (img == 255)
        white_adjust_mask = ~mask

        # Scale image if modal white is not at maximum intensity
        if modal_white != 255:
            scaling_factor = 255 / modal_white
            adjusted[white_adjust_mask] = (adjusted[white_adjust_mask] * scaling_factor).astype(np.uint16)
            adjusted = np.clip(adjusted, 0, 255)

        # Exclude extreme values from black adjustment
        new_extremes = (adjusted == 0) | (adjusted == 255)
        total_extremes = mask | new_extremes
        black_adjust_mask = ~total_extremes

        # Adjust image based on modal black value
        if modal_black:
            adjusted[black_adjust_mask] = ((adjusted[black_adjust_mask] - modal_black) * (255 / (255 - modal_black))).astype(np.uint16)
            adjusted = np.clip(adjusted, 0, 255)

            gamma = 1.0 - (modal_black / 255.0)
            gamma_corrected = np.power(adjusted[black_adjust_mask] / 255.0, gamma) * 255
            adjusted[black_adjust_mask] = np.clip(np.rint(gamma_corrected), 0, 255).astype(np.uint16)
        
        # Convert back to uint8 with rounding
        adjusted = np.rint(adjusted).astype(np.uint8)

        return adjusted, modal_black, modal_white

    except ValueError as e:
        log_error(f"Error adjusting values for image: {e}")
        return img, 0, 255