MethodError: no method matching translate! in Turing with SMC

Specific Domains Probabilistic programming
1

Here is my full code, based on a Turing tutorial.

using Turing, Turing.RandomMeasures
using LinearAlgebra, Distributions 
using Plots, Random, CSV, DataFrames

share_1 = rand(Uniform(), 100)
share_2 = 1 .- share_1 
shares = hcat(share_1, share_2)

@model function infiniteGMM(x)
    # Hyper-parameters, i.e. concentration parameter and parameters of H.
    N = size(x)[1] 
    D = size(x)[2]

    α = 10.0
    rpm = DirichletProcess(α) # Define random measure, e.g. Dirichlet process
    Hv = [Exponential(10 * el) for el in mean(x, dims=1)] 
    H = Exponential(5)
    z = tzeros(Int, N)
    μ = tzeros(Float64, D, 0)

    for i in 1:N
        # Number of clusters.
        K = maximum(z)
        nk = Vector{Int}(map(k -> sum(z .== k), 1:K))

        # Draw the latent assignment.
        z[i] ~ ChineseRestaurantProcess(rpm, nk)

        # Create a new cluster?
        if z[i] > K
            μ = hcat(μ, ones(D)) 

            # Draw location of new cluster.
            for j in 1:size(μ)[1]
                μ[j, z[i]] ~ H
            end
        end

        # Draw observation.
        x[i, :] ~ Dirichlet(μ[:, z[i]]) 
    end
end

# MCMC sampling 
Random.seed!(2)
iterations = 100
model_fun = infiniteGMM(shares);
chain = sample(model_fun, SMC(), iterations);

I get this error on the final line:

ERROR: MethodError: no method matching translate!!(::Core.SSAValue, ::Tuple{Symbol}, ::Vector{Any}, ::Bool, ::Core.CodeInfo)

I’m very new to Julia and Turing, I don’t know where to start with this one.