code: def relable(nodes, labeled_nodes, edges_list):
      # Relable the nodes, labeled_nodes and edges_list
      relabled_edges_list = []
      sorted_nodes = sorted(nodes)
      node_mapping = {node: i for i, node in enumerate(sorted_nodes)}
      for orig_edges in edges_list:
          relabeled_edges = []
          for edge in orig_edges:
              relabeled_edges.append(
                  (node_mapping[edge[0]], node_mapping[edge[1]])
              )
          relabled_edges_list.append(relabeled_edges)
      relabeled_labeled_nodes = [
          node_mapping[node] for node in labeled_nodes
      ]
      relabeled_nodes = [node_mapping[node] for node in nodes]
      return (
          relabled_edges_list,
          relabeled_nodes,
          relabeled_labeled_nodes,
      )
  
imports: import copy
  import torch
  import random
  import argparse
  import numpy
  import networkx
  import torch.nn
  import torch.nn.functional
  from torch_geometric.nn import SAGEConv
  from torch.utils.data import DataLoader
  from torch_geometric.datasets import Planetoid
  from torch.nn import Sequential, Linear, ReLU
  from deepsnap.dataset import GraphDataset
  from deepsnap.graph import Graph
  from model import *
label_desc: Use image node_id 5 for calling a global function with example usage: relable(nodes, labeled_nodes, edges_list) and returns: relabled_edges_list,  relabeled_nodes,  relabeled_labeled_nodes