How do I create a joint model that shares the parameters of a Knowledge Graph Embedding (KGE) model, TuckER (given below), and GloVe (assume a co-occurrence matrix along with the dimensions is already available) in PyTorch? In other words, the joint model must obey the criterion of the CMTF Framework and the weights from the two embeddings must be tied during training. The problem here is that the KGE expects a triple (subject, relation, object) whereas the GloVe expects a co-occurrence matrix. Additionally, their loss functions are also computed differently.

```
class TuckER(torch.nn.Module):
def __init__(self, d, d1, d2, **kwargs):
super(TuckER, self).__init__()
self.E = torch.nn.Embedding(len(d.entities), d1)
self.R = torch.nn.Embedding(len(d.relations), d2)
self.W = torch.nn.Parameter(torch.tensor(np.random.uniform(-1, 1, (d2, d1, d1)),
dtype=torch.float, device="cuda", requires_grad=True))
self.input_dropout = torch.nn.Dropout(kwargs["input_dropout"])
self.hidden_dropout1 = torch.nn.Dropout(kwargs["hidden_dropout1"])
self.hidden_dropout2 = torch.nn.Dropout(kwargs["hidden_dropout2"])
self.loss = torch.nn.BCELoss()
self.bn0 = torch.nn.BatchNorm1d(d1)
self.bn1 = torch.nn.BatchNorm1d(d1)
def init(self):
xavier_normal_(self.E.weight.data)
xavier_normal_(self.R.weight.data)
def forward(self, e1_idx, r_idx):
e1 = self.E(e1_idx)
x = self.bn0(e1)
x = self.input_dropout(x)
x = x.view(-1, 1, e1.size(1))
r = self.R(r_idx)
W_mat = torch.mm(r, self.W.view(r.size(1), -1))
W_mat = W_mat.view(-1, e1.size(1), e1.size(1))
W_mat = self.hidden_dropout1(W_mat)
x = torch.bmm(x, W_mat)
x = x.view(-1, e1.size(1))
x = self.bn1(x)
x = self.hidden_dropout2(x)
x = torch.mm(x, self.E.weight.transpose(1,0))
pred = torch.sigmoid(x)
return pred
```

I know how to jointly train two pre-trained models by loading the state dicts, taking an instance, running them on the two models, and then applying a feedforward layer on top. But I seem to be not able to figure this scenario out. Can you please suggest how I can achieve this?