multilevel data (cross-classified) - multiple imputations mice and miceadds

#1
I have some observations (Level 1) cross classified in two grouping variables (A and B at Levels 2a and 2B, respectively) in a Poisson model with an offset term:
DV ~ L1o1+L1o2+L1o3+ L2a1+L2a2+L2b1+L2b2+L2b3+
L2b2:L2b3+L2b3:L2a1+ L2b2: L2a1+

L2b2:L2b3:L2a1+

offset(exp)+(1|A)+(1|B),

family= poisson,

control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=2e5)))

I have the following predictors:
Level 1:
· L1o1
· L1o2
· L1o3
Level 2a:
· L2a1
· L2a2
Level 2b:
· L2b1
· L2b2
· L2b3
Interactions:
- cross-level interactions: L2b1:L1o1, L2b2:L1o1, L2b1:L2b2:L1o1
- a level-2 interaction, e.g., L2b1:L2b2

I was wondering if I can get some help in creating the predictor matrix and devising the interactions to be able to correctly run the analysis for the multiple imputation cross classified model, as I am not sure how to choose the appropriate methods, particularly for the interactions. Thank you so much!