Is it possible to declare multiple MvNormal variables with different covariance matrices in a vectorized way?

Questions v5
10

Does this mean it accepts a list of covariance matrices for cov?

with pm.Model() as model: 
        nu = pm.Truncated("nu", pm.Exponential.dist(lam=1/10), lower=2)
        sd_dist = pm.Exponential.dist(lam=2, shape=2)
        chol, corr, stds = pm.LKJCholeskyCov('chol_cov', n=2, eta=1, sd_dist=sd_dist, compute_corr=True)
        
        z = pm.Normal('z', mu=0, sigma=1, shape=(df.shape[0], 2))
        t = pm.Deterministic('t',  chol.dot(z.T).T * np.sqrt(nu/(nu-2)))

        y = pm.MvNormal('y', mu=t, cov=df['sigma_e'].tolist(), observed=df[['y1', 'y2']])

        idata = pm.sample(draws=5000, return_inferencedata=True, target_accept=0.99, chains=4, cores=4, nuts_sampler='numpyro', var_names=['nu', 'chol_cov', 't', 'y'])