A little confused about estimations for a model with definition variables

I am trying to fit an LGC model with individually-varying time points. I've added definition variables as labels, and the model itself works well (the script is pasted below). I am just a little confused about the estimations. As my understanding, in my case, I should have full information maximum likelihood (FIML) rather than ML since individual contributions to the likelihood depends on the individual-specific time; but I am not sure if I can get the FIML through my codes. Do I have to use mxFitFunctionML() or some other statement? Thank you in advance.

library(OpenMx)
lg.math.age.omx <- mxModel('LGC with individually varying time points',
type='RAM', mxData(observed = data, type='raw'),
manifestVars=c('y1','y2','y3','y4'),
latentVars=c('eta_1','eta_2'),
# residual variance paths
mxPath(from=c('y1','y2','y3','y4'),
arrows=2, free=TRUE, values=40, labels='th'),
# latent variable variances and covariance
mxPath(from=c('eta1','eta2'), arrows=2, connect='unique.pairs',
free=TRUE, values=c(1,0,1),
labels=c('psi11','psi21','psi22')),
# regression weights
mxPath(from='eta1', to=c('y1','y2','y3','y4'),
arrows=1, free=FALSE, values=1),
mxPath(from='eta2', to=c('y1','y2','y3','y4'), arrows=1, free=FALSE,
labels=c('data.t1','data.t2','data.t3','data.t4')),
# means and intercepts
mxPath(from='one', to=c('eta1','eta2'),
arrows=1, free=TRUE, values=c(40, 4), labels=c('alpha1','alpha2'))
) # close model
lg.math.age. t <- mxRun(lg.math.age.omx)
summary(lg.math.age. t)