The latent distribution is as close as possible to the prior. The second goal acts as a regularizer and ensures that we can decode meaningful graphs even when we sample latent representations from the prior p(Z). This second goal is critically important if we want to generate new graphs after training: we can generate new graphs by sampling from the prior and feeding these latent embeddings to the decoder, and this process will only work if this second goal is satisfied.