Where 𝛾 is a scaling factor to control the influence of constrain term on the unconditioned output. In the first training step, since both the weights and bias parameters of zero MLP are initialized to zero, i.e. both of the 𝒵(• | •) terms in Eq (3) are zero, then 𝐺 -./ = 𝐺 & . In this way, GrCN enjoys the full capability of the pre-trained prior model, and the harmful noise from conditional graph won't influence the hidden states of trainable copy at the beginning, therefore accelerating and stabilizing the training process.