code: def remove_self_loops(  # noqa: F811
      edge_index: Tensor,
      edge_attr: Optional[Tensor] = None,
  ) -> Tuple[Tensor, Optional[Tensor]]:
      r"""Removes every self-loop in the graph given by :attr:`edge_index`, so
      that :math:`(i,i) \not\in \mathcal{E}` for every :math:`i \in \mathcal{V}`.
  
      Args:
          edge_index (LongTensor): The edge indices.
          edge_attr (Tensor, optional): Edge weights or multi-dimensional
              edge features. (default: :obj:`None`)
  
      :rtype: (:class:`LongTensor`, :class:`Tensor`)
  
      Example:
          >>> edge_index = torch.tensor([[0, 1, 0],
          ...                            [1, 0, 0]])
          >>> edge_attr = [[1, 2], [3, 4], [5, 6]]
          >>> edge_attr = torch.tensor(edge_attr)
          >>> remove_self_loops(edge_index, edge_attr)
          (tensor([[0, 1],
                  [1, 0]]),
          tensor([[1, 2],
                  [3, 4]]))
      """
      size: Optional[Tuple[int, int]] = None
      if not typing.TYPE_CHECKING and torch.jit.is_scripting():
          layout: Optional[int] = None
      else:
          layout: Optional[torch.layout] = None
  
      value: Optional[Tensor] = None
      if is_torch_sparse_tensor(edge_index):
          layout = edge_index.layout
          size = (edge_index.size(0), edge_index.size(1))
          edge_index, value = to_edge_index(edge_index)
  
      is_undirected = False
      if not torch.jit.is_scripting() and isinstance(
          edge_index, EdgeIndex
      ):
          is_undirected = edge_index.is_undirected
  
      mask = edge_index[0] != edge_index[1]
      edge_index = edge_index[:, mask]
  
      if not torch.jit.is_scripting() and isinstance(
          edge_index, EdgeIndex
      ):
          edge_index._is_undirected = is_undirected
  
      if layout is not None:
          assert edge_attr is None
          assert value is not None
          value = value[mask]
          if (
              str(layout) == "torch.sparse_coo"
          ):  # str(...) for TorchScript :(
              return (
                  to_torch_coo_tensor(edge_index, value, size, True),
                  None,
              )
          elif str(layout) == "torch.sparse_csr":
              return (
                  to_torch_csr_tensor(edge_index, value, size, True),
                  None,
              )
          raise ValueError(
              f"Unexpected sparse tensor layout (got '{layout}')"
          )
  
      if edge_attr is None:
          return edge_index, None
      else:
          return edge_index, edge_attr[mask]
  
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 5 for calling a global function with example usage: remove_self_loops(edge_index, edge_attr) and returns: edge_index,  None, edge_index,  edge_attr, to_torch_coo_tensor,  None, to_torch_csr_tensor,  None