Excluding the input of a specific time step, all the input of the remaining time step is set to 0.

That would be for sure one possible approach, but keep in mind, that information in an LSTM, represented by the state vectors, “vanishes” across the time dimension. If you feed the input features only once, say at the first time step, it’s likely, that they won’t be fully propagated to the later time steps and hence the model would have a bias across the time dimension. Hence, you might want to use the same feature vector as input for every time step.