This is a wrapper used to process multiple different kinds of context information collected in x = (x_0, x_1, …). For each kind of context information x_i, an individual embedding network is provided in enets = (enet_0, enet_1, …). The embedded output of the forward method is the concatenation of the individual embeddings enet_i(x_i).
In the GW use case, this wrapper can be used to embed the high-dimensional signal input into a lower dimensional feature vector with a large embedding network, while applying an identity embedding to the time shifts.