class_docstr: The Twitch Gamer networks introduced in the
  `"Multi-scale Attributed Node Embedding"
  <https://arxiv.org/abs/1909.13021>`_ paper.
  Nodes represent gamers on Twitch and edges are followerships between them.
  Node features represent embeddings of games played by the Twitch users.
  The task is to predict whether a user streams mature content.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      name (str): The name of the dataset (:obj:`"DE"`, :obj:`"EN"`,
          :obj:`"ES"`, :obj:`"FR"`, :obj:`"PT"`, :obj:`"RU"`).
      transform (callable, optional): A function/transform that takes in an
          :obj:`torch_geometric.data.Data` object and returns a transformed
          version. The data object will be transformed before every access.
          (default: :obj:`None`)
      pre_transform (callable, optional): A function/transform that takes in
          an :obj:`torch_geometric.data.Data` object and returns a
          transformed version. The data object will be transformed before
          being saved to disk. (default: :obj:`None`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
  
  **STATS:**
  
  .. list-table::
      :widths: 10 10 10 10 10
      :header-rows: 1
  
      * - Name
        - #nodes
        - #edges
        - #features
        - #classes
      * - DE
        - 9,498
        - 315,774
        - 128
        - 2
      * - EN
        - 7,126
        - 77,774
        - 128
        - 2
      * - ES
        - 4,648
        - 123,412
        - 128
        - 2
      * - FR
        - 6,551
        - 231,883
        - 128
        - 2
      * - PT
        - 1,912
        - 64,510
        - 128
        - 2
      * - RU
        - 4,385
        - 78,993
        - 128
        - 2
code: def process(self) -> None:
      data = np.load(self.raw_paths[0], "r", allow_pickle=True)
      x = torch.from_numpy(data["features"]).to(torch.float)
      y = torch.from_numpy(data["target"]).to(torch.long)
  
      edge_index = torch.from_numpy(data["edges"]).to(torch.long)
      edge_index = edge_index.t().contiguous()
  
      data = Data(x=x, y=y, edge_index=edge_index)
  
      if self.pre_transform is not None:
          data = self.pre_transform(data)
  
      self.save([data], self.processed_paths[0])
  
file_path: pytorch_geometric/torch_geometric/datasets/twitch.py
imports: import os.path
  from typing import Callable, Optional
  import numpy
  import torch
  from torch_geometric.data import Data, InMemoryDataset, download_url
label_desc: Use image node_id 7 for calling the Twitch obj's underlying member method code with example usage: obj.process() without return types