I am not able to understand exactly what input needs to be given to the LSTM layer. It expects a state computed from before but I do not have these states. How do I proceed with the forward function? For now, this is the model and it gives me an error that says:
AttributeError: 'tuple' object has no attribute 'size'.
class ConvLSTM(nn.Module):
    def __init__(self):
        super().__init__()
        
        self.conv1 = nn.Conv1d(8, 16, kernel_size=8)
        self.conv2 = nn.Conv1d(16, 32, kernel_size=8)
        self.conv3 = nn.Conv1d(32, 64, kernel_size=8)
        
        self.bn1 = nn.BatchNorm1d(64)
        
        self.conv4 = nn.Conv1d(64, 64, kernel_size=8)
        self.conv5 = nn.Conv1d(64, 128, kernel_size=8)
        
        self.bn2 = nn.BatchNorm1d(128)

        self.lstm1 = nn.LSTM(12, 100)
        self.lstm2 = nn.LSTM(100, 128)
        
        self.fc1 = nn.Linear(128, 64)
        self.fc2 = nn.Linear(64, 32)
        self.fc3 = nn.Linear(32, classes)
        
    def exec_conv_block(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        
        x = F.max_pool1d(x, 2)
        x = self.bn1(x)
        
        x = F.relu(self.conv4(x))
        x = F.relu(self.conv5(x))
        
        x = F.max_pool1d(x, 2)
        x = self.bn2(x)
               
        return x
    
    def forward(self, x):
        x = self.exec_conv_block(x)

        x, state = self.lstm1(x)
        x, _ = self.lstm2(x, state)
        
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        
        return x