code: def random_walk(
      row: Tensor,
      col: Tensor,
      start: Tensor,
      walk_length: int,
      p: float = 1,
      q: float = 1,
      coalesced: bool = True,
      num_nodes: Optional[int] = None,
      return_edge_indices: bool = False,
  ) -> Union[Tensor, Tuple[Tensor, Tensor]]:
      """Samples random walks of length :obj:`walk_length` from all node indices
      in :obj:`start` in the graph given by :obj:`(row, col)` as described in the
      `"node2vec: Scalable Feature Learning for Networks"
      <https://arxiv.org/abs/1607.00653>`_ paper.
      Edge indices :obj:`(row, col)` need to be coalesced/sorted according to
      :obj:`row` (use the :obj:`coalesced` attribute to force).
  
      Args:
          row (LongTensor): Source nodes.
          col (LongTensor): Target nodes.
          start (LongTensor): Nodes from where random walks start.
          walk_length (int): The walk length.
          p (float, optional): Likelihood of immediately revisiting a node in the
              walk. (default: :obj:`1`)
          q (float, optional): Control parameter to interpolate between
              breadth-first strategy and depth-first strategy (default: :obj:`1`)
          coalesced (bool, optional): If set to :obj:`True`, will coalesce/sort
              the graph given by :obj:`(row, col)` according to :obj:`row`.
              (default: :obj:`True`)
          num_nodes (int, optional): The number of nodes. (default: :obj:`None`)
          return_edge_indices (bool, optional): Whether to additionally return
              the indices of edges traversed during the random walk.
              (default: :obj:`False`)
  
      :rtype: :class:`LongTensor`
      """
      if num_nodes is None:
          num_nodes = (
              max(int(row.max()), int(col.max()), int(start.max())) + 1
          )
  
      if coalesced:
          perm = torch.argsort(row * num_nodes + col)
          row, col = row[perm], col[perm]
  
      deg = row.new_zeros(num_nodes)
      deg.scatter_add_(0, row, torch.ones_like(row))
      rowptr = row.new_zeros(num_nodes + 1)
      torch.cumsum(deg, 0, out=rowptr[1:])
  
      node_seq, edge_seq = torch.ops.torch_cluster.random_walk(
          rowptr, col, start, walk_length, p, q
      )
  
      if return_edge_indices:
          return node_seq, edge_seq
  
      return node_seq
  
label_desc: Use image node_id 1 for calling a global function with example usage: random_walk(row, col, start, walk_length, p, q, coalesced, num_nodes, return_edge_indices) and returns: node_seq, node_seq,  edge_seq