code: def sample_neighbors(nodes, G, ratio, all_nodes):
      # This fuction takes a set of nodes, a NetworkX graph G and neighbor sampling ratio.
      # It will return sampled neighbors (unioned with input nodes) and edges between
      neighbors = set()
      edges = []
      for node in nodes:
          neighbors_list = list(nx.neighbors(G, node))
  
          # We only sample the (ratio * number of neighbors) neighbors
          num = int(len(neighbors_list) * ratio)
          if num > 0:
              # Random shuffle the neighbors
              random.shuffle(neighbors_list)
              neighbors_list = neighbors_list[:num]
              for neighbor in neighbors_list:
                  # Add neighbors
                  neighbors.add(neighbor)
                  edges.append((neighbor, node))
      return neighbors, neighbors.union(all_nodes), edges
  
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 2 for calling a global function with example usage: sample_neighbors(nodes, G, ratio, all_nodes) and returns: neighbors,  neighbors,  edges