class_docstr: A subset of the DBLP computer science bibliography website,
  as collected in the
  [Fu et al. (2020)](https://arxiv.org/abs/2002.01680) paper.
  
  **Arguments**
  
  - `normalize_x`: if True, normalize the features.
  - `dtype`: numpy dtype of graph data.
class_docstr_tok: ["A","subset","of","the","DBLP","computer","science","bibliography","website",",","as","collected","in","the","[","Fu","et","al",".","(","2020",")","]","(","https",":","\/\/arxiv.org\/abs\/2002.01680",")","paper",".","*","*","Arguments","*","*","-","`","normalize_x","`",":","if","True",",","normalize","the","features",".","-","`","dtype","`",":","numpy","dtype","of","graph","data","."]
code: def read(self):
      f = np.load(osp.join(self.path, "dblp.npz"))
  
      x = sp.csr_matrix(
          (f["attr_data"], f["attr_indices"], f["attr_indptr"]),
          f["attr_shape"],
      ).toarray()
      x[x > 0] = 1
  
      if self.normalize_x:
          print("Pre-processing node features")
          x = _preprocess_features(x)
  
      a = sp.csr_matrix(
          (f["adj_data"], f["adj_indices"], f["adj_indptr"]),
          f["adj_shape"],
      )  # .tocoo()
  
      y = f["labels"]
      y = label_to_one_hot(y, np.unique(y))
  
      return [
          Graph(
              x=x.astype(self.dtype),
              a=a.astype(self.dtype),
              y=y.astype(self.dtype),
          )
      ]
  
file_path: spektral/spektral/datasets/dblp.py
imports: import errno
  import os
  import os.path
  import ssl
  import sys
  import urllib
  import numpy
  import scipy.sparse
  from spektral.data import Dataset, Graph
  from spektral.datasets.citation import _preprocess_features
  from spektral.datasets.utils import DATASET_FOLDER
  from spektral.utils import label_to_one_hot
label_desc: Use image node_id 4 for calling the DBLP obj's underlying member method code with example usage: obj.read() and returns: list