how to address the node features(node feature tensor) in to the GNN model appropriately for optimizing the requires 13 variables.
feature_vector = device_type + device_onehot + component_onehot + values + region_state with indices [0:23], which is splitted for each features as, [0 + 1:6 + 7:17 + 18:22 + 23], all the feature vectors are already normalized before tensor structure.
node_features.append(feature_vector)
node_features_tensor = torch.FloatTensor(node_features)

I have totally 20 nodes, among those 20 nodes GNN model need to take of only 11 component nodes 'M0', 'M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'C0', 'I0', 'V1' remaining 9 nodes are net nodes which are not take part in the optimization, the net nodes are only used for message passing (in  my fixed topology graph sructure, edges are connected between the component nodes and net nodes).
My main aim is to focus on the 'values' and 'region_state' of nodes 'M0', 'M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'C0', 'I0', 'V1', remaining all features in the node features are fixed values and wont get change throughout all the iteration and the edge features and edge index also fixed in all iteration, because i have fixed graph topology, where we need to tune the node feature, especially 'values' of the nodes 'M0', 'M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'C0', 'I0', 'V1' appropriately and keep the node feature 'region_state' as '1' for all the nodes among all other node features. similarly due to fixed graph topology and only the node features 'values' are going to change and monitor the node feature 'region_state' as '1', so the edge features and the edge index are fixed during all the complete iteration process. I need to know whether the given requirements are completely satisfies within the below given GNN model. And I also need to know, I already coded the computation for rewards which including penalties for the region_state which is deviating from my requirements. do i need to include additional function 'custom_loss' for performing the same which is given below. suggest me the whether the below code is in well structured or not and if any modification needed to upgrade the existing implementation please provide me the complete upgraded code if any changes is needed in existing.


import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GATConv

class CustomGATConv(GATConv):
    def init(self, *args, **kwargs):
        super(CustomGATConv, self).init(*args, **kwargs)

    def forward(self, x, edge_index):
        # Perform standard GATConv forward pass
        return super().forward(x, edge_index)

class GATModelWithConstraints(nn.Module):
    def init(self, node_input_dim, edge_embedding_dim, hidden_dim, output_dim, num_layers, num_heads, constraint_dims):
        super(GATModelWithConstraints, self).init()

        self.value_indices = value_indices
        self.state_index = state_index
        self.mask_weight = 5  # Amplification factor for ‘values’ and ‘region_state’

        self.node_convs = nn.ModuleList([
            CustomGATConv(node_input_dim if i == 0 else hidden_dim * num_heads, hidden_dim, heads=num_heads, concat=True)
            for i in range(num_layers)
        ])
        
        self.edge_convs = nn.Sequential(nn.Linear(edge_embedding_dim, hidden_dim * num_layers), nn.ELU())
        self.combine_features = nn.Linear(hidden_dim * num_heads + hidden_dim * num_layers, output_dim + 1)  # +1 for region state

        self.constraint_fc = nn.Linear(output_dim, constraint_dims)

    def apply_feature_mask(self, features):
        # Create a mask that amplifies ‘values’ and ‘region_state’
        mask = torch.ones(features.size(1))
        mask[self.value_indices] = self.mask_weight  # Amplify ‘values’
        mask[self.state_index] = self.mask_weight   # Amplify ‘region_state’
        
        # Assuming features and mask are on the same device
        masked_features = features * mask
        return masked_features

    def forward(self, node_features, edge_features, edge_index):
        # Apply mask to enhance ‘values’ and ‘region_state’
        node_features = self.apply_feature_mask(node_features)

        x = node_features
        for conv in self.node_convs:
            x = conv(x, edge_index)
            x = F.elu(x)

        e = self.edge_convs(edge_features)
        combined_features = torch.cat([x, e], dim=1)
        output = self.combine_features(combined_features)

        constraints = self.constraint_fc(output)

        return output, constraints

# Custom loss function including region state constraint
def custom_loss(output, targets, region_state_target, alpha=0.5):
    main_output, region_state_pred = output[:, :-1], output[:, -1]
    main_loss = F.mse_loss(main_output, targets)  # Adjust based on your task
    region_state_loss = F.binary_cross_entropy_with_logits(region_state_pred, region_state_target)

    # Combined loss
    total_loss = (1-alpha) * main_loss + alpha * region_state_loss
    return total_loss

def rearrange_output(original_output):
    # Assuming original_output is a tensor with shape [13] for action space values
    desired_order = [1, 3, 5, 7, 9, 0, 2, 4, 6, 8, 11, 10, 12]
    rearranged_output = original_output[:, desired_order]
    return rearranged_output

# training loop where you process your model’s output
optimizer.zero_grad()
output = model(node_features, edge_features, edge_index)
rearranged_action_space = rearrange_output(output)
loss = custom_loss(output, targets, region_state_target, alpha=0.5)
loss.backward()
optimizer.step()