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"]
      )
  
      if paddle.distributed.get_world_size() > 1:
          dataset.graph = pgl.DistGPUGraph(dataset.graph)
      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/citation_benchmark/multi_gpu_train.py
imports: import pgl
  import model
  import paddle
  import paddle.nn
  from pgl.utils.logger import log
  import numpy
  import time
  import argparse
  import yaml
  from easydict import EasyDict
  import tqdm
  from paddle.optimizer import Adam
  import paddle.distributed
label_desc: Use image node_id 2 for calling a global function with example usage: load(name, normalized_feature) and returns: dataset