How to extract priors from a Turing model

Assuming the following model:

# Bayesian linear regression.
@model function linear_regression(x, y)
    # Set variance prior.
    σ² ~ truncated(Normal(0, 100); lower=0)

    # Set intercept prior.
    intercept ~ Normal(0, sqrt(3))

    # Set the priors on our coefficients.
    nfeatures = size(x, 2)
    coefficients ~ MvNormal(Zeros(nfeatures), 10.0 * I)

    # Calculate all the mu terms.
    mu = intercept .+ x * coefficients
    return y ~ MvNormal(mu, σ² * I)

Is there a way to extract the priors set in the model? Something like:

(σ²=truncated(Normal(0, 100); lower=0), 
 intercept=Normal(0, sqrt(3)))

I couldn’t find any solutions in the Turing documentation, so thanks for any pointers!