code: def get_sample_graph_list(row, colptr, nodes, sample_sizes):
      graph_list = []
      for size in sample_sizes:
          (
              neighbors,
              neighbor_counts,
          ) = paddle.geometric.sample_neighbors(
              row, colptr, nodes, sample_size=size
          )
          (
              edge_src,
              edge_dst,
              sample_index,
          ) = paddle.geometric.reindex_graph(
              nodes, neighbors, neighbor_counts
          )
          nodes = sample_index
      edge_index = paddle.concat(
          [edge_src.reshape([-1, 1]), edge_dst.reshape([-1, 1])], axis=1
      )
      return edge_index, nodes
  
file_path: PGL/examples/gnn_depoly/node_classification_with_sampling/train.py
imports: import os
  import sys
  import argparse
  import numpy
  import paddle
  import pgl
  import paddle.nn
  from paddle.io import Dataset, DataLoader
  from pgl.utils.logger import log
  from paddle.optimizer import Adam
  from paddle.framework import core
  from dataset import ShardedDataset
  from models import GraphSage, GAT
label_desc: Use image node_id 3 for calling a global function with example usage: get_sample_graph_list(row, colptr, nodes, sample_sizes) and returns: edge_index,  nodes