I put my LSTM model code below and I am not quite sure what my model actually looks like or how I would change it.

For example. What would my model look like with these params:

n_features = 5
n_layers = 3 
n_hidden=64 
n_classes=3

I would have said:

1. Layer is Input layer with 5 Neurons
2. LSTM with 64 Neurons
3. LSTM with 64 Neurons
4. LSTM with 64 Neurons
5. Linear Output with 3 Neurons

Now if this is correct, could I write the same model with the 3 LSTM Layers seperately?

class Model_LSTM(nn.Module):

    def __init__(self, n_features, n_classes, n_hidden, n_layers):            
        super().__init__()
        self.lstm = nn.LSTM(
            input_size=n_features,
            hidden_size=n_hidden,
            num_layers=n_layers,
            batch_first=True,
            dropout=0.75
        )

        # HE-Initialisierung
        weight = torch.zeros(n_layers,n_hidden)
        nn.init.kaiming_uniform_(weight)
        self.weight = nn.Parameter(weight)


        self.classifier = nn.Linear(n_hidden, n_classes)

    def init_hidden(self):       
        hidden_state = torch.zeros(self.lstm.num_layers,batch_size,self.lstm.hidden_size)
        cell_state = torch.zeros(self.lstm.num_layers,batch_size,self.lstm.hidden_size)
        return (hidden_state, cell_state)

    def forward(self, x):
        self.hidden = self.init_hidden()
        _, (hidden, _) = self.lstm(x)                  
        out=hidden[-1]                                  
        return self.classifier(out)