Code problem

[hongxiaDu]

Hi, thanks your work! I have some confusion about the code. I want to know what self.word_context means, and why concat with x1(pair_h = torch.cat((q1, x1), dim=-1)， q1 = self.word_context.weight[0:].view(1, -1).repeat(x1.shape[0],1).view(x1.shape[0], self.out_features))? It doesn't seem to be reflected in the formula.

When AGGR(edge) aggregates features of hyperedges to nodes, pair_h = torch.cat((q1, y1), dim=-1) , q1 are hyperedge features, y1 are node features. So, I guess whether q1 is the hyperedge feature when nodes features aggregate to hyperedges features? If the guess is correct? But why self.word_context = nn.Embedding(1, self.out_features), instead of self.word_context = nn.Embedding(n_hyperedge, self.out_features). Don't we need to distinguish features of hyperedges?