Array function coding error for multlevel logit model with multivariate priors

I need to put a period to this thread. Below I am posting a Stan code chunk for running a multilevel logit model with multivariate priors, hopefully without any issue. Note that I silence the lines containing array, since I haven’t figured it out yet and will be working on it. Will update once I have solid answers.
stanDiscoursePost20220402.R (3.4 KB)
Attached please also find an updated R script file. Thank you all for help!

``````data {
int<lower=0> N;                     // num individuals
int<lower=1> K;                     // num ind predictors
int<lower=1> J;                     // num groups
int<lower=1> L;                     // num group predictors
// array[N] int<lower=1, upper=J> jj;  // group for individual
int<lower=1, upper=J> jj[N];  // group for individual
matrix[N, K] x;                     // individual predictors
row_vector[L] u[J];           // group predictors
// vector[N] y;                        // outcomes
int<lower=0, upper=1> y[N];
// vector<lower=0, upper=1>[N] y;
}

parameters {
corr_matrix[K] Omega;        // prior correlation
vector<lower=0>[K] tau;      // prior scale
matrix[L, K] gamma;          // group coeffs
// array[J] vector[K] beta;     // indiv coeffs by group
vector[K] beta[J];
real<lower=0> sigma;         // prediction error scale
}

model {
tau ~ cauchy(0, 2.5);
Omega ~ lkj_corr(2);
to_vector(gamma) ~ normal(0, 5);
{
// array[J] row_vector[K] u_gamma;
row_vector[K] u_gamma[J];
for (j in 1:J) {
u_gamma[j] = u[j] * gamma;
}