Check whether the below program properly includes the below requirements,
##Preprocessing Input Data:
1. Provide the GNN (GAT) model with information about all nodes and their features as input.
2. Identify and mark the nodes that need to be optimized and specify the features within those nodes that require tuning.

##Selective Tuning Logic:
1. Implement logic within the GNN (GAT) model to selectively tune the specified nodes and features while keeping the rest of the nodes and features fixed.
2. This logic can involve conditioning the optimization process on certain node or feature attributes and adjusting model parameters accordingly.

##Output Selection:
1. Design the output layer of the GNN model to produce outputs for all nodes and features but selectively extract and use only the tuned features from the selected nodes.

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

class GNN(nn.Module):
    def __init__(self, num_node_features, num_edge_features, num_out_features):
        super(GNN, self).__init__()
        # Define GAT layers for processing node features
        self.conv1 = GATConv(num_node_features, 64, heads=8)
        self.conv2 = GATConv(64 * 8, num_out_features, heads=1)

    def forward(self, node_features_tensor, edge_feature_tensor, edge_index):
        # Preprocess input data
        node_features_tensor, tuned_indices = preprocess_input(node_features_tensor)
        
        # Forward pass through GNN layers
        x = F.elu(self.conv1(node_features_tensor, edge_index))
        x = self.conv2(x, edge_index)
        
        # Select only tuned features from the output
        tuned_output = x[:, tuned_indices]
        
        return tuned_output

def preprocess_input(node_features_tensor):
    # Identify component nodes
    component_nodes = torch.where(node_features_tensor[:, 0] == 0)[0]

    # Define the indices of features to be tuned for each respective node
    tuning_indices = {
        'M0': [18, 19],
        'M1': [18, 19],
        'M2': [18, 19],
        'M3': [18, 19],
        'M4': [18, 19],
        'M5': [18, 19],
        'M6': [18, 19],
        'M7': [18, 19],
        'C0': [20],
        'I0': [21],
        'V1': [22]
    }

    # Apply the tuning logic to the node features tensor
    for node_name, indices in tuning_indices.items():
        node_index = torch.where(node_features_tensor[:, 7] == 1)[0][0]  # Assuming index 7 corresponds to the node name
        if node_index in component_nodes:
            for idx in indices:
                node_features_tensor[node_index, idx] = 1  # Marking tuned features
    
    # Synchronous tuning for specified pairs of components
    synchronous_pairs = [('M0', 'M1'), ('M2', 'M3'), ('M4', 'M7')]
    for pair in synchronous_pairs:
        index1 = torch.where(node_features_tensor[:, 7] == 1)[0][0]  # Assuming index 7 corresponds to the node name
        index2 = torch.where(node_features_tensor[:, 8] == 1)[0][0]  # Assuming index 8 corresponds to the node name
        if index1 in component_nodes and index2 in component_nodes:
            for idx in tuning_indices[pair[0]]:
                node_features_tensor[index1, idx] = node_features_tensor[index2, idx]

    return node_features_tensor, tuning_indices


# Initialize GNN model
num_node_features = 24  # Example number of node features
num_edge_features = 8   # Example number of edge features
num_out_features = 16   # Example number of output features
gnn_model = GNN(num_node_features, num_edge_features, num_out_features)

# Forward pass through the GNN model
output = gnn_model(node_features_tensor, edge_feature_tensor, edge_index)