If i do this, does the “Model” and the “Encoder” have same weights?
Or will they become two separate networks?
class Encoder(nn.Module):
    def __init__(self):
        super(Encoder, self).__init__()
        self.fc1 = nn.Linear(784, 32)

    def forward(self, x):
        return F.sigmoid(self.fc1(x))

class Decoder(nn.Module):
    def __init__(self):
        super(Decoder, self).__init__()
        self.fc1 = nn.Linear(32, 784)
        
    def forward(self, x):
        return F.sigmoid(self.fc1(x))

class AutoEncoder(nn.Module):
    def __init__(self):
        super(AutoEncoder, self).__init__()
        self.fc1 = Encoder()
        self.fc2 = Decoder()

    def forward(self, x):
        return self.fc2(self.fc1(x))

Model = AutoEncoder()
Encoder = Encoder()
Decoder = Decoder()