Random intercept and slope model with correlation and complex within individual variability in Winbugs

I am trying to implement a random intercept and slope with complex within error variability in WinBUGS. I was getting "expected multivariate node". Here is a sample code with the error message. Any help would be much appreciated. Please let me know if there is need for further clarification.

model { 
  for (i in 1:N) {
    y[i,1:2] ~ dnorm(mu[i,1:2],prec[i,1:2])
    mu[i,1] <- alpha[1] + beta[1]*(wave[i]-1) + b[id[i],1] + u[id[i],1]*(wave[i]-1)
    mu[i,2] <- alpha[2] + beta[2]*(wave[i]-1) + b[id[i],2] + u[id[i],2]*(wave[i]-1)
    prec[i,1] <- 1 / exp(delta1[1] + delta2[1] * (wave[i]-1) + b[id[i], 3])
    prec[i,2] <- 1 / exp(delta1[2] + delta2[2] * (wave[i]-1) + b[id[i], 4])
 
  }
 
     alpha[1] ~ dnorm(0,0.000001)
     alpha[2] ~ dnorm(0,0.000001)
     beta[1]  ~ dnorm(0,0.000001)
     beta[2]  ~ dnorm(0,0.000001)
     delta1[1]  ~ dnorm(0,0.000001)
     delta1[2]  ~ dnorm(0,0.000001)
     delta2[1]  ~ dnorm(0,0.000001)
     delta2[2]  ~ dnorm(0,0.000001)
 
for (i in 1:Nid) { 
    b[i,1:4] ~ dmnorm(m1[1:4], prec1[1:4,1:4])
    u[i,1:2] ~ dmnorm(m2[1:2], prec2[1:2,1:2])
 }

# Priors for random terms
 prec1[1:4,1:4] ~ dwish(R1[1:4,1:4], 4)
 sigma1[1:4,1:4] <- inverse(prec1[1:4,1:4])
 R1[1,1] <- 0.00001 
 R1[2,2] <- 0.00001
 R1[3,3] <- 0.00001   
 R1[4,4] <- 0.00001    
 R1[1,2] <- 0    
 R1[1,3] <- 0 
 R1[1,4] <- 0   
 R1[2,3] <- 0  
 R1[2,4] <- 0  
 R1[3,4] <- 0  
 S1[1:4,1:4] <- inverse(prec1[1:4,1:4])
 
 prec2[1:2,1:2] ~ dwish(R1[1:2,1:2], 2)
 sigma2[1:2,1:2] <- inverse(prec2[1:2,1:2])
  R2[1,1] <- 0.0001   
  R2[2,2] <- 0.0001   
  R2[1,2] <- 0    
  R2[2,1] <- 0 
  S2[1:2,1:2] <- inverse(prec2[1:2,1:2])
 

}
}}

I think the problem is actually in the first line of the first for() loop:

    y[i,1:2] ~ dnorm(mu[i,1:2],prec[i,1:2])

You're saying here that the first two columns of the ith row of y have a distribution. The distribution would have to have as many dimensions as there are values, so WinBUGS is looking for a bivariate node here (~dmnorm(mu[i,1:2], prec[1:2,1:2])). Alternatively, you could separate them:

y[i,1] ~ dnorm(mu[i,1], prec[i,1])
y[i,2] ~ dnorm(mu[i,2], prec[i,2])