code: def load_data(normalize=True, symmetry=True):
      """
      data from https://github.com/matenure/FastGCN/issues/8
      reddit_adj.npz: https://drive.google.com/open?id=174vb0Ws7Vxk_QTUtxqTgDHSQ4El4qDHt
      reddit.npz: https://drive.google.com/open?id=19SphVl_Oe8SJ1r87Hr5a6znx3nJu1F2J
      """
      data_dir = os.path.dirname(os.path.abspath(__file__))
      data = np.load(os.path.join(data_dir, "data/reddit.npz"))
      adj = sp.load_npz(os.path.join(data_dir, "data/reddit_adj.npz"))
      if symmetry:
          adj = adj + adj.T
      adj = adj.tocoo()
      src = adj.row
      dst = adj.col
  
      num_class = 41
  
      train_label = data["y_train"]
      val_label = data["y_val"]
      test_label = data["y_test"]
  
      train_index = data["train_index"]
      val_index = data["val_index"]
      test_index = data["test_index"]
  
      feature = data["feats"].astype("float32")
  
      if normalize:
          scaler = StandardScaler()
          scaler.fit(feature[train_index])
          feature = scaler.transform(feature)
  
      log.info("Feature shape %s" % (repr(feature.shape)))
      graph = pgl.graph.Graph(
          num_nodes=feature.shape[0],
          edges=list(zip(src, dst)),
          node_feat={"feat": feature.astype("float32")},
      )
  
      return {
          "graph": graph,
          "train_index": train_index,
          "train_label": train_label,
          "val_label": val_label,
          "val_index": val_index,
          "test_index": test_index,
          "test_label": test_label,
          "num_class": 41,
      }
  
file_path: PGL/legacy/examples/graphsage/train_multi.py
imports: import os
  import argparse
  import time
  import sys
  import traceback
  import numpy
  import scipy.sparse
  from sklearn.preprocessing import StandardScaler
  import pgl
  from pgl.utils.logger import log
  from pgl.utils import paddle_helper
  import paddle
  import paddle.fluid
  import reader
  from model import graphsage_mean, graphsage_meanpool, graphsage_maxpool, graphsage_lstm
label_desc: Use image node_id 1 for calling a global function with example usage: load_data(normalize, symmetry) and returns: dict