MLP_forward_pass.py

def forward(self, users, items):

    user_emb = self.embeddings_user(users)
    item_emb = self.embeddings_item(items)
    emb_vector = torch.cat([user_emb,item_emb], dim=1)
    emb_vector = self.mlp(emb_vector)
    preds = torch.sigmoid(self.out(emb_vector))

    return preds