Multivariate time series forecasting lstm

Assume I have 200 samples of time series data. The input has three features and I want to forecast one step ahead in time. So assume my series look like the following: The first three rows:

1 2 3
4 5 6
7 8 9

Now I will convert the problem to supervised learning problem for one step prediction:

Input        Output
1 2 3        4 5 6
4 5 6        7 8 9

Now I would like to forecast the value of all three features in the output not only one of them. Now I would like to know what should be the dimension of the input to Keras?: I think it should be (100,1,3) where 100 can be replaced by the batch size. Can someone verify this. The main question is what should I change in lstm configuration for it to understand that I need multivariate setting for the output like for example should I have dense(3) to specify I need 3 outputs. I would be appreciate if someone can help.