code: def load(name, normalized_feature=True):
      if name == "cora":
          dataset = data_loader.CoraDataset()
      elif name == "pubmed":
          dataset = data_loader.CitationDataset(
              "pubmed", symmetry_edges=True
          )
      elif name == "citeseer":
          dataset = data_loader.CitationDataset(
              "citeseer", symmetry_edges=True
          )
      else:
          raise ValueError(name + " dataset doesn't exists")
  
      indegree = dataset.graph.indegree()
      norm = np.maximum(indegree.astype("float32"), 1)
      norm = np.power(norm, -0.5)
      dataset.graph.node_feat["norm"] = np.expand_dims(norm, -1)
      dataset.graph.node_feat["words"] = normalize(
          dataset.graph.node_feat["words"]
      )
      return dataset
  
file_path: PGL/legacy/examples/citation_benchmark/train.py
imports: import pgl
  import model
  from pgl import data_loader
  from pgl.utils.logger import log
  import paddle.fluid
  import numpy
  import time
  import argparse
  from build_model import build_model
  import yaml
  from easydict import EasyDict
  import tqdm
label_desc: Use image node_id 2 for calling a global function with example usage: load(name, normalized_feature) and returns: dataset