In this below code, my intention is to tune only the selective features in the selected nodes, and keep the unselected nodes and its corresponding features as well as the unselective features in the selected nodes are kept unchange.

x = self.gat2(x, edge_index)
print("x.size():", x.size())

total size of 'x' is 
x.size(): torch.Size([20, 24])
totally 20 nodes and each nodes having 24 features

among the 20 nodes we are selected only 11 nodes based on the index values corresponding to the index number [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] is '1.0000' respectively for each selected node (to identify the first selected node, it has index value '1.0000' at the index number '7', similarly for each selected nodes 8 - 17).

the selective features are different for each selected nodes
for the first eight selected nodes [7, 8, 9, 10, 11, 12, 13, 14] the selective features need to tune is at the index number of [18, 19]
for the selected node [15] the selective features need to tune is at the index number of [20]
for the selected node [16] the selective features need to tune is at the index number of [21]
for the selected node [17] the selective features need to tune is at the index number of [22]

after tuning process done by the algorithm, we made to keep the values present index [18, 19] as same for the paired selected nodes [(7, 8), (9, 10), (11, 14)], so we implemented the synchronization after tuning using averaging the values between the respective pairs. and i need to keep all the static nodes and dynamix nodes total 20 nodes and its corresponding 24 features in to the GNN model with masking where needed for selective feature tuning.

now please identify where the implementation logic is not followed in the below given custom GNN 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=0.6)
        self.gat2 = GATConv(8 * 8, out_channels, heads=1, concat=False, dropout=0.6)

        self.component_nodes_indices = torch.tensor([7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], dtype=torch.long)
        
        # Define masks for tuning selective features
        self.m_features_mask = torch.zeros(24, dtype=torch.bool)
        self.m_features_mask[[18, 19]] = True  
        self.c_features_mask = torch.zeros(24, dtype=torch.bool)
        self.c_features_mask[20] = True
        self.i_features_mask = torch.zeros(24, dtype=torch.bool)
        self.i_features_mask[21] = True
        self.v_features_mask = torch.zeros(24, dtype=torch.bool)
        self.v_features_mask[22] = True

    def forward(self, x, edge_index):
        x = F.dropout(x, p=0.6, training=self.training)
        x = F.elu(self.gat1(x, edge_index))
        x = F.dropout(x, p=0.6, training=self.training)
        x = self.gat2(x, edge_index)
        print("x.size():", x.size())
        #print("x", x)

        # Synchronize updates for defined node pairs before updating dynamic features
        # Averaging the values for the synchronous pairs for features at indices [18] and [19]
        # Ensure the original_features tensor is prepared for this operation to not alter unrelated features.
        original_features = x.clone()

        # Define synchronous node pairs and their associated feature indices
        sync_pairs = [(7, 8), (9, 10), (11, 14)]  # Indices in self.component_nodes_indices
        features_to_sync = [18, 19]

        # Perform synchronization
        for pair in sync_pairs:
            indices = [self.component_nodes_indices[pair[0]], self.component_nodes_indices[pair[1]]]
            assert max(indices) < x.size(0), f"One of the indices {max(indices)} is out of bounds for tensor with {x.size(0)} nodes."
            
            # Calculate the mean of the paired node features for selected features
            avg_features = original_features[indices, :][:, features_to_sync].mean(dim=0)
            
            # Update the original tensor with averaged features for both nodes in the pair for specified features
            original_features[indices, features_to_sync] = avg_features

        # Apply mask and update dynamic features (if there’s any additional logic for individual component node updates)
        dynamic_updates = torch.zeros_like(x)
        
        # Update logic as previous, but now considering synchronization is already handled
        # Note: With the current use-case, dynamic updates remain as initially set.
        # This placeholder exists for cases where further dynamic processing is applied after synchronization.

        # Ensuring static features are kept as is from original_features and only dynamic are updated
        return original_features * (1 - dynamic_updates) + x * dynamic_updates