code: def add_remaining_self_loops(  # noqa: F811
      edge_index: Tensor,
      edge_attr: Optional[Tensor] = None,
      fill_value: Optional[Union[float, Tensor, str]] = None,
      num_nodes: Optional[int] = None,
  ) -> Tuple[Tensor, Optional[Tensor]]:
      r"""Adds remaining self-loop :math:`(i,i) \in \mathcal{E}` to every node
      :math:`i \in \mathcal{V}` in the graph given by :attr:`edge_index`.
      In case the graph is weighted or has multi-dimensional edge features
      (:obj:`edge_attr != None`), edge features of non-existing self-loops will
      be added according to :obj:`fill_value`.
  
      Args:
          edge_index (LongTensor): The edge indices.
          edge_attr (Tensor, optional): Edge weights or multi-dimensional edge
              features. (default: :obj:`None`)
          fill_value (float or Tensor or str, optional): The way to generate
              edge features of self-loops (in case :obj:`edge_attr != None`).
              If given as :obj:`float` or :class:`torch.Tensor`, edge features of
              self-loops will be directly given by :obj:`fill_value`.
              If given as :obj:`str`, edge features of self-loops are computed by
              aggregating all features of edges that point to the specific node,
              according to a reduce operation. (:obj:`"add"`, :obj:`"mean"`,
              :obj:`"min"`, :obj:`"max"`, :obj:`"mul"`). (default: :obj:`1.`)
          num_nodes (int, optional): The number of nodes, *i.e.*
              :obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`)
  
      :rtype: (:class:`LongTensor`, :class:`Tensor`)
  
      Example:
          >>> edge_index = torch.tensor([[0, 1],
          ...                            [1, 0]])
          >>> edge_weight = torch.tensor([0.5, 0.5])
          >>> add_remaining_self_loops(edge_index, edge_weight)
          (tensor([[0, 1, 0, 1],
                  [1, 0, 0, 1]]),
          tensor([0.5000, 0.5000, 1.0000, 1.0000]))
      """
      N = maybe_num_nodes(edge_index, num_nodes)
      mask = edge_index[0] != edge_index[1]
  
      device = edge_index.device
      if torch.jit.is_scripting():
          loop_index = (
              torch.arange(0, N, device=device).view(1, -1).repeat(2, 1)
          )
      else:
          loop_index = EdgeIndex(
              torch.arange(0, N, device=device)
              .view(1, -1)
              .repeat(2, 1),
              sparse_size=(N, N),
              is_undirected=True,
          )
  
      if edge_attr is not None:
          loop_attr = compute_loop_attr(  #
              edge_index, edge_attr, N, False, fill_value
          )
  
          inv_mask = ~mask
          loop_attr[edge_index[0][inv_mask]] = edge_attr[inv_mask]
  
          edge_attr = torch.cat([edge_attr[mask], loop_attr], dim=0)
  
      is_undirected = False
      if not torch.jit.is_scripting() and isinstance(
          edge_index, EdgeIndex
      ):
          is_undirected = edge_index.is_undirected
  
      edge_index = edge_index[:, mask]
  
      if not torch.jit.is_scripting() and isinstance(
          edge_index, EdgeIndex
      ):
          edge_index._is_undirected = is_undirected
  
      edge_index = torch.cat([edge_index, loop_index], dim=1)
  
      return edge_index, edge_attr
  
imports: import typing
  from typing import Optional, Tuple, Union
  import torch
  from torch import Tensor
  from torch_geometric import EdgeIndex
  from torch_geometric.utils import scatter
  from torch_geometric.utils.num_nodes import maybe_num_nodes
  from torch_geometric.utils.sparse import is_torch_sparse_tensor, to_edge_index, to_torch_coo_tensor, to_torch_csr_tensor
label_desc: Use image node_id 38 for calling a global function with example usage: add_remaining_self_loops(edge_index, edge_attr, fill_value, num_nodes) and returns: edge_index,  edge_attr