class_docstr: The Deezer User Network datasets introduced in the
  `"GEMSEC: Graph Embedding with Self Clustering"
  <https://arxiv.org/abs/1802.03997>`_ paper.
  Nodes represent Deezer user and edges are mutual friendships.
  The task is multi-label multi-class node classification about
  the genres liked by the users.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      name (str): The name of the dataset (:obj:`"HU"`, :obj:`"HR"`,
          :obj:`"RO"`).
      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`)
code: def processed_file_names(self) -> str:
      return "data.pt"
  
file_path: pytorch_geometric/torch_geometric/datasets/gemsec.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 5 for calling the GemsecDeezer obj's underlying member method code with example usage: obj.processed_file_names() and returns: str