code: def _linear_decorelate_color(t):
    
    # check that inner dimension is 3?
    t_flat = tf.reshape(t, [-1, 3])
    color_correlation_normalized = color_correlation_svd_sqrt / max_norm_svd_sqrt
    t_flat = tf.matmul(t_flat, color_correlation_normalized.T)
    t = tf.reshape(t_flat, tf.shape(t))
    return t
docs: Multiply input by sqrt of emperical (ImageNet) color correlation matrix.
    
    If you interpret t's innermost dimension as describing colors in a
    decorrelated version of the color space (which is a very natural way to
    describe colors -- see discussion in Feature Visualization article) the way
    to map back to normal colors is multiply the square root of your color
    correlations.