At the moment my model gives me an error:

`TypeError: tanh(): argument 'input' (position 1) must be Tensor, not tuple`

If there is a solution?

How to implement this model in PyTorch?

The model is following:

```
class RNN(nn.Module):
def __init__(self):
super(RNN, self).__init__()
self.lstm1 = nn.LSTM(input_size=87, hidden_size=256)
self.lstm2 = nn.LSTM(input_size=256, hidden_size=128)
self.lstm3 = nn.LSTM(input_size=128, hidden_size=64)
self.lstm4 = nn.LSTM(input_size=64, hidden_size=32)
self.fc1 = nn.Linear(in_features=32, out_features=128)
self.fc2 = nn.Linear(in_features=128, out_features=64)
self.fc3 = nn.Linear(in_features=64, out_features=32)
self.fc4 = nn.Linear(in_features=32, out_features=3)
def forward(self, x):
x = torch.tanh(self.lstm1(x))
x = torch.tanh(self.lstm2(x))
x = torch.tanh(self.lstm3(x))
x = torch.tanh(self.lstm4(x))
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
x = self.fc4(x)
return x
```