# Prepare mask for distinguishing between stable and unstable nodes
        stability_compute_mask = torch.zeros(processed_features.size(0), 1, device=processed_features.device)
        stability_compute_mask[unstable_nodes] = 1

- Mask Usage: While the computation directly processes the features of unstable nodes (without using the mask for multiplication, as previously discussed), the concept of masking is pivotal for identifying the nodes of interest.

- Selective Stability Computation: Only nodes marked as requiring stability computation (i.e., the unstable nodes) undergo the stability_head processing. This ensures computation is efficient and focused.

Does the mask is necessary for the compuation, if it is neccessary how to implement it appropriately in within the below code

class Actor(torch.nn.Module):
    def __init__(self, gnn_model):
        super(Actor, self).__init__()
        self.gnn = gnn_model
        self.stability_head = nn.Sequential(
            nn.Linear(24, 64),  # Assuming 24 features from GNN’s processed features
            nn.ReLU(),
            nn.Linear(64, 1)    # Outputs a single stability value per node
        )
        
        # Define bounds for action space scaling
        self.bounds_low = torch.tensor([0.18e-6, 0.18e-6, 0.18e-6, 0.18e-6, 0.18e-6, 
                                        0.5e-6, 0.5e-6, 0.5e-6, 0.5e-6, 0.5e-6, 
                                        15e-6, 0.1e-12, 0.8], dtype=torch.float32)
                                        
        self.bounds_high = torch.tensor([0.2e-6, 0.2e-6, 0.2e-6, 0.2e-6, 0.2e-6, 
                                         50e-6, 50e-6, 50e-6, 50e-6, 50e-6, 
                                         30e-6, 10e-12, 1.4], dtype=torch.float32)
        
    def forward(self, state):
        node_features_tensor, _, edge_index = state
        
        # Processed features from the GNN
        processed_features = self.gnn(node_features_tensor, edge_index)
        # Example assertion (add after GNN processing)
        #assert torch.all(node_features_tensor[:, 0] == processed_features[:, 0]), "Node order mismatch!"
        
        # Initialize stability predictions - assume all nodes as stable
        stability_pred = torch.ones(processed_features.size(0), device=processed_features.device)
        
        # Define the indices of nodes that actually require stability prediction
        # Given [0, 3, 5, 7, 10, 13, 15, 16] represent tunable/unstable nodes
        unstable_nodes_idx = torch.tensor([0, 3, 5, 7, 10, 13, 15, 16], dtype=torch.long, device=processed_features.device)
        
        # Fetch features for nodes requiring stability computation
        features_for_stability = processed_features[unstable_nodes_idx]
        
        # Compute stability predictions for these nodes
        stability_pred_unstable = self.stability_head(features_for_stability).squeeze(-1)
        
        # Update the overall stability predictions
        stability_pred[unstable_nodes_idx] = stability_pred_unstable
        
        # For action_indices handling, adapt based on the dynamic nature of processed_features
        action_indices = [(10, 19), (16, 19), (5, 19), (3, 19), (0, 19), (10, 18),
                          (16, 18), (5, 18), (3, 18), (0, 18), (17, 20), (18, 21), (19, 22)]
                          
        # Convert action_indices to tensor format for easier handling
        action_indices_tensor = torch.tensor(action_indices, dtype=torch.long).t()
        selected_features = processed_features[action_indices_tensor[0], action_indices_tensor[1]]
        
        # Normalize and scale the GNN outputs to the action bounds for usable actions
        normalized_gnn_output = torch.tanh(selected_features)
        action_means = self.bounds_low + (self.bounds_high - self.bounds_low) * ((normalized_gnn_output + 1) / 2)
        
        # Define action variances (assuming a simple fixed form here for demonstration)
        action_log_std = torch.zeros_like(action_means)
        action_std = torch.exp(action_log_std)

        return action_means, action_std, stability_pred