Enquiry about model.predict() with batch_size and sampling from each layer after model fitting

[XiaoyuHy]

Hi,
I'm writing to enquire about two use cases of deep GP.
1.
When I was using the two-layer deep GP to make predictions, when I pass batch_size to the function model.predict(), it seems that it only returns the predicted mean (f_mean) without f_var. However, as in your example below(https://secondmind-labs.github.io/GPflux/notebooks/gpflux_features.html:

def plot(model, X, Y, ax=None):
    if ax is None:
        fig, ax = plt.subplots()

    x_margin = 1.0
    N_test = 100
    X_test = np.linspace(X.min() - x_margin, X.max() + x_margin, N_test).reshape(-1, 1)
    **out = model(X_test)

    mu = out.f_mean.numpy().squeeze()
    var = out.f_var.numpy().squeeze()**
    X_test = X_test.squeeze()
    lower = mu - 2 * np.sqrt(var)
    upper = mu + 2 * np.sqrt(var)

    ax.set_ylim(Y.min() - 0.5, Y.max() + 0.5)
    ax.plot(X, Y, "kx", alpha=0.5)
    ax.plot(X_test, mu, "C1")

    ax.fill_between(X_test, lower, upper, color="C1", alpha=0.3)
prediction_model = deep_gp.as_prediction_model()

When no batch_size is passed, deep_gp.as_prediction_model() returns both f_mean and f_var. Could you please let me know how I can get both f_mean and f_var when passing batch_size to prediction_model.predict() ?

2. On this webpage, https://secondmind-labs.github.io/GPflux/notebooks/deep_gp_samples.html, you have given an example of making deep GP samples. However, I cannot see the model fitting part on the webpage. If want to make samples from each layer after model fitting , i.e., after model.compile(tf.optimizers.Adam(0.01)) and history = model.fit({"inputs": X, "targets": Y}, epochs=int(1e3), verbose=0), could you please let me know how I can achieve this?

Many thanks.