How to implement knn based on weights - r

Question

I would like to implement the weighted knn algorithm but I don't know how to do it. Everything and that I can use kknn, I suppose that it can also be done with knn. In the function train(caret) there is an option "weights" but I can't find the solution, any suggestion?
I use the following code in R :
library(caret)
library(corrplot)
glass <- read.csv("https://archive.ics.uci.edu/ml/machine-learning-databases/glass/glass.data",
col.names=c("","RI","Na","Mg","Al","Si","K","Ca","Ba","Fe","Type"))
str(glass)
head(glass)
glass_1<- glass[,-7]
glass_2<- glass_1[,-7]
head(glass_2)
glass<- glass_2
standard.features <- scale(glass[,2:8])
data <- cbind(standard.features,glass[9])
anyNA(data)
head(data)
corrplot(cor(data))
data$Type<-factor(data$Type)
inTraining <- createDataPartition(data$Type, p = .7, list = FALSE, times =1 )
training <- data[ inTraining,]
testing <- data[-inTraining,]
prop.table(table(training$Type))
prop.table(table(testing$Type))
dim(training); dim(testing);
summary(data)
fitControl <- trainControl(## 5-fold CV
method = "cv",
number = 5,
## repeated ten times
#repeats = 5)
)
#k_value <- expand.grid(kmax = 3, distance = 2, kernel = "optimal")
k_value <- expand.grid(k = 3)
set.seed(825)
knn_Fit <- train(Type ~ ., data = training, weights = ????,
method = "knn", tuneGrid = k_value,
trControl = fitControl)
## This last option is actually one
## for gbm() that passes through
#verbose = FALSE)
knn_Fit
knn_Fit$finalModel