class_docstr: The datasets used in the paper
  
  > [Inductive Representation Learning on Large Graphs](https://arxiv.org/abs/1706.02216)<br>
  > William L. Hamilton et al.
  
  The PPI dataset (originally
  [Stark et al. (2006)](https://www.ncbi.nlm.nih.gov/pubmed/16381927))
  for inductive node classification uses positional gene sets, motif gene sets
  and immunological signatures as features and gene ontology sets as labels.
  
  The Reddit dataset consists of a graph made of Reddit posts in the month of
  September, 2014. The label for each node is the community that a
  post belongs to. The graph is built by sampling 50 large communities and
  two nodes are connected if the same user commented on both. Node features
  are obtained by concatenating the average GloVe CommonCrawl vectors of
  the title and comments, the post's score and the number of comments.
  
  The train, test, and validation splits are given as binary masks and are
  accessible via the `mask_tr`, `mask_va`, and `mask_te` attributes.
  
  **Arguments**
  
  - `name`: name of the dataset to load (`'ppi'`, or `'reddit'`);
code: def read(self):
      npz_file = osp.join(self.path, self.name) + ".npz"
      data = np.load(npz_file)
      x = data["x"]
      a = sp.csr_matrix(
          (data["adj_data"], (data["adj_row"], data["adj_col"])),
          shape=data["adj_shape"],
      )
      y = data["y"]
      self.mask_tr = data["mask_tr"]
      self.mask_va = data["mask_va"]
      self.mask_te = data["mask_te"]
  
      return [Graph(x=x, a=a, y=y)]
  
imports: import json
  import os
  import os.path
  import shutil
  import numpy
  import scipy.sparse
  from networkx.readwrite import json_graph
  from spektral.data import Dataset, Graph
  from spektral.data.dataset import DATASET_FOLDER
  from spektral.datasets.utils import download_file
label_desc: Use image node_id 3 for calling the GraphSage obj's underlying member method code with example usage: obj.read() and returns: list