I need to know in the below implementation of graph embedding into the Graph neural network with the node features alone is sufficient for the implementation for graph attention network, or do i need adjacency matrics to include in the below implementation.

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 
        # hidden_features = 8
        # self.gat1 = GATConv(input_features, hidden_features, heads=heads, concat=True)
        # self.gat2 = GATConv(hidden_features * heads, output_features, heads=1, concat=False)
        # hidden_features * heads = 8 * 8 = 64
        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 = {            
            (0, 18): (5, 18),
            (0, 19): (5, 19),
            (13, 18): (15, 18),
            (13, 19): (15, 19),
            (8, 18): (12, 18),
            (8, 19): (12, 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), (5, 18), (5, 19), (8, 18), (8, 19), (10, 18), (10, 19),
            (12, 18), (12, 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(23, 64),  # 23 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!"
                      
        # For action_indices handling, adapt based on the dynamic nature of processed_features
        action_indices = [(0, 19), (13, 19), (8, 19), (16, 19), (10, 19), (0, 18),
                          (13, 18), (8, 18), (16, 18), (10, 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

class Critic(torch.nn.Module):
    def __init__(self):
        super(Critic, self).__init__()
        flattened_size = 20 * 23  # Number of nodes times features per node
        self.network = torch.nn.Sequential(
            torch.nn.Linear(flattened_size, 128),
            torch.nn.ReLU(),
            torch.nn.Linear(128, 1)
        )

    def forward(self, state):
        # Directly use state as it is already what we referred to as state[0] in evaluate
        flattened = state.view(-1)  # Flatten to [480]
        return self.network(flattened)