I am having problems with this:
# hidden features
n_hidden = 256
# input activation factor 
n_fac = 42 
# batch size
bs = 512

class Model007(nn.Module):
    def __init__(self, vocab_size, n_fac):
        super().__init__()
        self.l1 = nn.Embedding(vocab_size, n_fac)
        self.l2 = nn.Linear(n_fac, n_hidden)
        
        
    def forward(self, c1, c2, c3):
        print("c1", c1)
        print("c2", c2)
        print("c3", c3)
        
        in1 = torch.relu(self.l2(self.l1(c1)))
        in2 = torch.relu(self.l2(self.l1(c2)))
        in3 = torch.relu(self.l2(self.l1(c3)))
                
        return in1 + in2 + in3
    
x1 = np.array([13,3,28,24,33,2,3,62,47,58])
x2 = np.array([15,21,30,27,17,3,3,54,54,44])
x3 = np.array([32,2,27,19,2,3,32,3,60,47])
x = np.stack([x1,x2,x3], axis=1)
y = np.array([3,28,24,33,2,3,62,47,58,54])
# converting to tensor
X = torch.from_numpy(x).cuda()
Y = torch.from_numpy(y).cuda()
print(X)
print(Y)
print("...")



ds = utils.TensorDataset(X, Y) 
dl = utils.DataLoader(ds, batch_size=2, shuffle=False) 
print(ds.tensors)
it = iter(dl)

# mb mini bach, yt is target
mb, yt = next(it) 
print(mb)

m = Model007(vocab_size, n_fac).cuda()

y_hat = m(mb)

print(y_hat)

The error I am getting is like this:
TypeError: forward() missing 2 required positional arguments: 'c2' and 'c3'