Is an Averaged neural network (avNNet) the average from all iterations?

I have fitted an Averaged neural network in R with Caret. See code below. Does the term Averaged mean that the average is based on the outcomes of 1000 neural networks? (since there are 1000 iterations in this case)

Thanks.

library(AppliedPredictiveModeling)
data(solubility)

### Create a control funciton that will be used across models. We
### create the fold assignments explictily instead of relying on the
### random number seed being set to identical values.

library(caret)
set.seed(100)
indx <- createFolds(solTrainY, returnTrain = TRUE)
ctrl <- trainControl(method = "cv", index = indx)

################################################################################
### Section 7.1 Neural Networks

### Optional: parallel processing can be used via the 'do' packages,
### such as doMC, doMPI etc. We used doMC (not on Windows) to speed
### up the computations.

### WARNING: Be aware of how much memory is needed to parallel
### process. It can very quickly overwhelm the availible hardware. We
### estimate the memory usuage (VSIZE = total memory size) to be 
### 2677M/core.

library(doMC)
registerDoMC(10)


library(caret)

nnetGrid <- expand.grid(decay = c(0, 0.01, .1), 
                        size = c(1, 3, 5, 7, 9, 11, 13), 
                        bag = FALSE)

set.seed(100)
nnetTune <- train(x = solTrainXtrans, y = solTrainY,
                  method = "avNNet",
                  tuneGrid = nnetGrid,
                  trControl = ctrl,
                  preProc = c("center", "scale"),
                  linout = TRUE,
                  trace = FALSE,
                  MaxNWts = 13 * (ncol(solTrainXtrans) + 1) + 13 + 1,
                  maxit = 1000,
                  allowParallel = FALSE)
nnetTune

plot(nnetTune)

testResults <- data.frame(obs = solTestY,
                          NNet = predict(nnetTune, solTestXtrans))

################################################################################

See also:

https://scientistcafe.com/post/nnet.html

avNNet is a model where the same neural network model is fit using different random number seeds. All the resulting models are used for prediction. For regression, the output from each network are averaged. For classification, the model scores are first averaged, then translated to predicted classes. Source.

The number of models fit is controlled by the argument repeats which is passed down to the model in caret via ...

repeats - the number of neural networks with different random number seeds. At default this is set to 5. So five models will be averaged. In caret's definition of the model I do not see this changing.

If the bag argument is set to TRUE model fitting and aggregation is performed by bootstrap aggregation, which in my opinion is almost guaranteed to provide better predictive performance if the number of models is high enough.