The customGNN was designed with the below features,
- Dynamic Feature Processing: This version specifically processes dynamic features index [18, 19] (and, depending on the specific component node, index [20], [21], [22]) for action space parameters within component nodes.
- Maintaining Original Features: For both component and net nodes, static features remain unchanged. The entire node features matrix is initialized to hold original features, and only the dynamic features of component nodes are processed through the GAT layer.
- Component and Net Node Handling: Component node features are selectively updated based on the action space parameters. Net nodes and static features of component nodes are left unchanged, fulfilling the requirement to keep net nodes and other static component node features static during the training process.

I need you to rovide me the complete code for RL PPO agent in continuous action space for tuning the selected dynamic feature values(with actor and critic) to be properly synchronize with the existing custom model 'class CustomGNN(torch.nn.Module)',

import torch
from torch_geometric.nn import GATConv

class CustomGNN(torch.nn.Module):
    def init(self, total_node_count, num_feature_dim, num_output_dim):
        super(CustomGNN, self).init()
        self.total_node_count = total_node_count
        self.num_feature_dim = num_feature_dim
        self.num_output_dim = num_output_dim
        
        # GAT layer for component nodes, customized for dynamic features
        self.gat_component = GATConv(in_channels=2,  # Only taking the dynamic features for GAT processing
                                     out_channels=num_output_dim,
                                     heads=8,
                                     dropout=0.6)
    
    def forward(self, node_features, edge_index):
        # Dynamically determine component and net node indices based on their features
        component_indices = (node_features[:, 0] == 0).nonzero(as_tuple=True)[0]
        
        # Initialize a tensor for all nodes to hold original features
        all_node_features = torch.zeros((self.total_node_count, self.num_feature_dim))
        
        # For component nodes, selectively process only the dynamic features (indices [18] [19])
        dynamic_feature_indices = torch.tensor([18, 19], dtype=torch.long)
        
        for idx in component_indices:
            # Identify if node is part of synchronized tuning sets and pick correct dynamic indices
            node_idx_value = node_features[idx, 7:18].nonzero(as_tuple=True)[0].item() + 7
            dynamic_index = [node_idx_value - 7 + 18, node_idx_value - 7 + 19] if node_idx_value < 15 else [node_idx_value]
            
            dynamic_features = node_features[idx, dynamic_feature_indices].reshape(1, -1)
            processed_dynamic_features = self.gat_component(dynamic_features, edge_index)[0]
            
            # Insert processed dynamic features back, keep static features unchanged
            all_node_features[idx, dynamic_feature_indices] = processed_dynamic_features

        # Copy net node features directly, as they remain unchanged
        net_indices = (node_features[:, 0] == 1).nonzero(as_tuple=True)[0]
        all_node_features[net_indices] = node_features[net_indices]
        
        return all_node_features