The techniques used in this work can indeed be extended to handle functions with more complex dependence structures beyond the assumption of independent entries. One approach to address functions with more intricate dependencies is to explore the use of conditional concentration inequalities tailored to specific dependence structures. By incorporating tools from stochastic processes or advanced probability theory, it may be possible to derive concentration results for functions with correlated or structured dependencies. Additionally, considering extensions of the generalized McDiarmid's inequality to handle functions with Markovian properties or other forms of dependence could open up new avenues for applying these concentration inequalities in diverse settings.