What is the point of assigning a random variable to a named python variable?

Hi, I was playing with this post on Bayesian MMM.

coords = {"date": date, "fourier_mode": np.arange(2 * n_order)}

with pm.Model(coords=coords) as base_model:
    # --- coords ---
    base_model.add_coord(name="dat", values=date, mutable=True)
    base_model.add_coord(name="fourier_mode", values=np.arange(2 * n_order), mutable=False)

    # --- data containers ---
    z_scaled_ = pm.MutableData(name="z_scaled", value=z_scaled, dims="date")

    # --- priors ---
    ## intercept
    a = pm.Normal(name="a", mu=0, sigma=4)
    ## trend coefficient
    b_trend = pm.Normal(name="b_trend", mu=0, sigma=2)
    ## seasonality coefficient
    b_fourier = pm.Laplace(name="b_fourier", mu=0, b=2, dims="fourier_mode")
    ## regressor coefficient
    b_z = pm.HalfNormal(name="b_z", sigma=2)
    ## standard deviation of the normal likelihood
    sigma = pm.HalfNormal(name="sigma", sigma=0.5)
    # degrees of freedom of the t distribution
    nu = pm.Gamma(name="nu", alpha=25, beta=2)
    # --- model parametrization (coeff * data) ---
    trend = pm.Deterministic(name="trend", var=a + b_trend * t, dims="date")
    seasonality = pm.Deterministic(
        name="seasonality", var=pm.math.dot(fourier_features, b_fourier), dims="date"
    z_effect = pm.Deterministic(name="z_effect", var=b_z * z_scaled_, dims="date")
    # expected value of the outcome
    mu = pm.Deterministic(name="mu", var=trend + seasonality + z_effect, dims="date")

    # --- likelihood ---
    pm.StudentT(name="likelihood", nu=nu, mu=mu, sigma=sigma, observed=y_scaled, dims="date")

    # --- prior samples ---
    base_model_prior_predictive = pm.sample_prior_predictive()


What I was struggling with is that once I don’t assign pm.Deterministic(name="mu", var=trend + seasonality + z_effect, dims="date") to mu the code raises error in sampling step

with base_model:
    base_model_trace = pm.sample(
        idata_kwargs={"log_likelihood": True},
    base_model_posterior_predictive = pm.sample_posterior_predictive(

the above code throws the ValueError: Random variables detected in the logp graph: {a, b_fourier, b_z, b_trend}.

Is there anyone can help me with why this happens?

When you don’t assign mu you must be passing another mu variable to your likelihood variable, which probably belongs to an earlier model in the local scope.