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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _mix_color(slice1, slice2, alpha_channels, color_space):
 """Mixing them as red and green channels"""


    if slice1.shape != slice2.shape:
        raise ValueError('size mismatch between cropped slices and checkers!!!')

    alpha_channels = np.array(alpha_channels)
    if len(alpha_channels) != 2:
        raise ValueError('Alphas must be two value tuples.')

    slice1, slice2 = scale_images_0to1(slice1, slice2)

    # masking background
    combined_distr = np.concatenate((slice1.flatten(), slice2.flatten()))
    image_eps = np.percentile(combined_distr, 5)
    background = np.logical_or(slice1 <= image_eps, slice2 <= image_eps)

    if color_space.lower() in ['rgb']:

        red = alpha_channels[0] * slice1
        grn = alpha_channels[1] * slice2
        blu = np.zeros_like(slice1)

        # foreground = np.logical_not(background)
        # blu[foreground] = 1.0

        mixed = np.stack((red, grn, blu), axis=2)

    elif color_space.lower() in ['hsv']:

        raise NotImplementedError(
            'This method (color_space="hsv") is yet to fully conceptualized and implemented.')

        # TODO other ideas: hue/saturation/intensity value driven by difference in intensity?
        hue = alpha_channels[0] * slice1
        sat = alpha_channels[1] * slice2
        val = np.ones_like(slice1)

        hue[background] = 1.0
        sat[background] = 0.0
        val[background] = 0.0

        mixed = np.stack((hue, sat, val), axis=2)
        # converting to RGB
        mixed = mpl.colors.hsv_to_rgb(mixed)

    # ensuring all values are clipped to [0, 1]
    mixed[mixed <= 0.0] = 0.0
    mixed[mixed >= 1.0] = 1.0

    return mixed