text: from typing import Optional, Callable, List
  
  import os
  import os.path as osp
  from itertools import product
  
  import torch
  import numpy as np
  import scipy.sparse as sp
  
  from torch_geometric.data import (InMemoryDataset, HeteroData, download_url,
                                    extract_zip)
  
  
  class LastFM(InMemoryDataset):
      r"""A subset of the last.fm music website keeping track of users' listining
      information from various sources, as collected in the
      `"MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph
      Embedding" <https://arxiv.org/abs/2002.01680>`_ paper.
      last.fm is a heterogeneous graph containing three types of entities - users
      (1,892 nodes), artists (17,632 nodes), and artist tags (1,088 nodes).
      This dataset can be used for link prediction, and no labels or features are
      provided.
  
      Args:
          root (string): Root directory where the dataset should be saved.
          transform (callable, optional): A function/transform that takes in an
              :obj:`torch_geometric.data.HeteroData` 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.HeteroData` object and returns a
              transformed version. The data object will be transformed before
              being saved to disk. (default: :obj:`None`)
      """
  
      url = 'https://www.dropbox.com/s/jvlbs09pz6zwcka/LastFM_processed.zip?dl=1'
  
      def __init__(self, root: str, transform: Optional[Callable] = None,
                   pre_transform: Optional[Callable] = None):
          super().__init__(root, transform, pre_transform)
          self.data, self.slices = torch.load(self.processed_paths[0])
  
      @property
      def raw_file_names(self) -> List[str]:
          return [
              'adjM.npz', 'node_types.npy', 'train_val_test_neg_user_artist.npz',
              'train_val_test_pos_user_artist.npz'
          ]
  
      @property
      def processed_file_names(self) -> str:
          return 'data.pt'
  
      def download(self):
          path = download_url(self.url, self.raw_dir)
          extract_zip(path, self.raw_dir)
          os.remove(path)
  
      def process(self):
          data = HeteroData()
  
          node_type_idx = np.load(osp.join(self.raw_dir, 'node_types.npy'))
          node_type_idx = torch.from_numpy(node_type_idx).to(torch.long)
  
          node_types = ['user', 'artist', 'tag']
          for i, node_type in enumerate(node_types):
              data[node_type].num_nodes = int((node_type_idx == i).sum())
  
          pos_split = np.load(
              osp.join(self.raw_dir, 'train_val_test_pos_user_artist.npz'))
          neg_split = np.load(
              osp.join(self.raw_dir, 'train_val_test_neg_user_artist.npz'))
  
          for name in ['train', 'val', 'test']:
              if name != 'train':
                  edge_index = pos_split[f'{name}_pos_user_artist']
                  edge_index = torch.from_numpy(edge_index)
                  edge_index = edge_index.t().to(torch.long).contiguous()
                  data['user', 'artist'][f'{name}_pos_edge_index'] = edge_index
  
              edge_index = neg_split[f'{name}_neg_user_artist']
              edge_index = torch.from_numpy(edge_index)
              edge_index = edge_index.t().to(torch.long).contiguous()
              data['user', 'artist'][f'{name}_neg_edge_index'] = edge_index
  
          s = {}
          N_u = data['user'].num_nodes
          N_a = data['artist'].num_nodes
          N_t = data['tag'].num_nodes
          s['user'] = (0, N_u)
          s['artist'] = (N_u, N_u + N_a)
          s['tag'] = (N_u + N_a, N_u + N_a + N_t)
  
          A = sp.load_npz(osp.join(self.raw_dir, 'adjM.npz'))
          for src, dst in product(node_types, node_types):
              A_sub = A[s[src][0]:s[src][1], s[dst][0]:s[dst][1]].tocoo()
              if A_sub.nnz > 0:
                  row = torch.from_numpy(A_sub.row).to(torch.long)
                  col = torch.from_numpy(A_sub.col).to(torch.long)
                  data[src, dst].edge_index = torch.stack([row, col], dim=0)
  
          if self.pre_transform is not None:
              data = self.pre_transform(data)
  
          torch.save(self.collate([data]), self.processed_paths[0])
  
      def __repr__(self) -> str:
          return f'{self.__class__.__name__}()'
  