Description of my process:
In my graph network(undirected bipartite), i have fixed topology of component nodes(11 nodes) on one side and net node(9 nodes) on another side(derived from the analog circuit netlist). where i have total 20 nodes and each nodes are having 24 node features and the 38 edges were connected between these 11 component nodes and 9 net nodes (this is my state representation the data from the environment for optimization process) and among the node features of each node where the 24th node represents its stability state. my environment need the action space of values for 13 variables from the actor network as input to the simulator circuit netlist. here few nodes [0, 3, 5, 7, 10, 13, 15, 16] are need to be check for its stability and during optimization process these nodes are need to be keep in stable state.

what is the correct values for the input dates to the gnn_model, 'in_channels' and 'out_channels' for the below model instantiated in PPOAgent class,
gnn_model_actor = CustomGNN(in_channels=, out_channels=)
gnn_model_critic = CustomGNN(in_channels=, out_channels=)


MY CODE:
class CustomGNN(torch.nn.Module):
    def __init__(self, in_channels, out_channels):
        super(CustomGNN, self).__init__()
        self.gat1 = GATConv(in_channels, 8, heads=8, dropout=0.2) # dropout=0.6
        self.gat2 = GATConv(64, out_channels, heads=1, concat=False, dropout=0.2)  # 8*8 is 64 # dropout=0.6

    def forward(self, x, edge_index):
        x = F.dropout(x, p=0.2, training=self.training) # p=0.6
        x = F.elu(self.gat1(x, edge_index))
        # After dropout and activation
        #assert torch.equal(X1[:, 0], x[:, 0]), "Order mismatch after dropout/activation."
        x = F.dropout(x, p=0.2, training=self.training) # p=0.6
        x = self.gat2(x, edge_index)
        # Selective feature synchronization based on given mappings
        # Converting given (row, column) pairs to 0-based indexing
        sync_mappings = {
            (10, 18): (7, 18),
            (10, 19): (7, 19),
            (16, 18): (15, 18),
            (16, 19): (15, 19),
            (5, 18): (0, 18),
            (5, 19): (0, 19),            
        }
        
        original_features = x.clone()
        for target, source in sync_mappings.items():
            original_features[target[0], target[1]] = original_features[source[0], source[1]]
        
        # Only retain the transformative updates for the specified 19 features
        dynamic_updates_mask = torch.zeros_like(x, dtype=bool)
        dynamic_indices = [
            (0, 18), (0, 19), (3, 18), (3, 19), (5, 18), (5, 19), (7, 18), (7, 19),
            (10, 18), (10, 19), (13, 18), (13, 19), (15, 18), (15, 19), (16, 18),
            (16, 19), (17, 20), (18, 21), (19, 22)]       

        for row, col in dynamic_indices:
            dynamic_updates_mask[row, col] = True
        
        x = original_features * (~dynamic_updates_mask) + x * dynamic_updates_mask

        return x


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([0.181e-6, 0.181e-6, 0.181e-6, 0.181e-6, 0.181e-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), (13, 19), (3, 19), (10, 18),
                          (16, 18), (5, 18), (13, 18), (3, 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

class Critic(torch.nn.Module):
    def __init__(self, gnn_model):
        super(Critic, self).__init__()
        self.gnn_model = gnn_model
        self.fc = torch.nn.Sequential(
            #torch.nn.Linear(24 * 20, 128),
            torch.nn.Linear(24 * 20, 128),
            torch.nn.ReLU(),
            torch.nn.Linear(128, 1)
        )

    def forward(self, node_features_tensor, edge_index):
        x = self.gnn_model(node_features_tensor, edge_index)
        x = x.view(-1)  # Flatten
        return self.fc(x)


# Initialize Hyperparameters
in_channels = 24  # Number of input features
out_channels = 24  # Number of output actions or features

class PPOAgent:
    def init(self, state_dim, action_space, lr_actor, lr_critic, gamma, gae_lambda, epsilon, policy_clip, epochs, entropy_coef):
        # Create separate instances if necessary
        gnn_model_actor = CustomGNN(in_channels=state_dim, out_channels=action_space)
        gnn_model_critic = CustomGNN(in_channels=state_dim, out_channels=1)
        
        self.actor = Actor(gnn_model_actor)
        self.critic = Critic(gnn_model_critic)
        self.optimizer_actor = optim.Adam(self.actor.parameters(), lr=lr_actor)
        self.optimizer_critic = optim.Adam(self.critic.parameters(), lr=lr_critic)
        self.action_space = action_space