code: def load(name, normalized_feature=True):
      if name == "cora":
          dataset = pgl.dataset.CoraDataset()
      elif name == "pubmed":
          dataset = pgl.dataset.CitationDataset(
              "pubmed", symmetry_edges=True
          )
      elif name == "citeseer":
          dataset = pgl.dataset.CitationDataset(
              "citeseer", symmetry_edges=True
          )
      else:
          raise ValueError(name + " dataset doesn't exists")
  
      indegree = dataset.graph.indegree()
      dataset.graph.node_feat["words"] = normalize(
          dataset.graph.node_feat["words"]
      )
  
      dataset.graph.tensor()
      train_index = dataset.train_index
      dataset.train_label = paddle.to_tensor(
          np.expand_dims(dataset.y[train_index], -1)
      )
      dataset.train_index = paddle.to_tensor(
          np.expand_dims(train_index, -1)
      )
  
      val_index = dataset.val_index
      dataset.val_label = paddle.to_tensor(
          np.expand_dims(dataset.y[val_index], -1)
      )
      dataset.val_index = paddle.to_tensor(
          np.expand_dims(val_index, -1)
      )
  
      test_index = dataset.test_index
      dataset.test_label = paddle.to_tensor(
          np.expand_dims(dataset.y[test_index], -1)
      )
      dataset.test_index = paddle.to_tensor(
          np.expand_dims(test_index, -1)
      )
  
      return dataset
  
file_path: PGL/examples/gcn/train.py
imports: import tqdm
  import pgl
  import paddle
  import paddle.nn
  from pgl.utils.logger import log
  import numpy
  import time
  import argparse
  from paddle.optimizer import Adam
label_desc: Use image node_id 2 for calling a global function with example usage: load(name, normalized_feature) and returns: dataset