code: def get_self_loop_attr(
      edge_index: Tensor,
      edge_attr: Optional[Tensor] = None,
      num_nodes: Optional[int] = None,
  ) -> Tensor:
      r"""Returns the edge features or weights of self-loops
      :math:`(i, i)` of every node :math:`i \in \mathcal{V}` in the
      graph given by :attr:`edge_index`. Edge features of missing self-loops not
      present in :attr:`edge_index` will be filled with zeros. If
      :attr:`edge_attr` is not given, it will be the vector of ones.
  
      .. note::
          This operation is analogous to getting the diagonal elements of the
          dense adjacency matrix.
  
      Args:
          edge_index (LongTensor): The edge indices.
          edge_attr (Tensor, optional): Edge weights or multi-dimensional edge
              features. (default: :obj:`None`)
          num_nodes (int, optional): The number of nodes, *i.e.*
              :obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`)
  
      :rtype: :class:`Tensor`
  
      Examples:
          >>> edge_index = torch.tensor([[0, 1, 0],
          ...                            [1, 0, 0]])
          >>> edge_weight = torch.tensor([0.2, 0.3, 0.5])
          >>> get_self_loop_attr(edge_index, edge_weight)
          tensor([0.5000, 0.0000])
  
          >>> get_self_loop_attr(edge_index, edge_weight, num_nodes=4)
          tensor([0.5000, 0.0000, 0.0000, 0.0000])
      """
      loop_mask = edge_index[0] == edge_index[1]
      loop_index = edge_index[0][loop_mask]
  
      if edge_attr is not None:
          loop_attr = edge_attr[loop_mask]
      else:  # A vector of ones:
          loop_attr = torch.ones(
              loop_index.numel(), device=edge_index.device
          )
  
      num_nodes = maybe_num_nodes(edge_index, num_nodes)
      full_loop_attr = loop_attr.new_zeros(
          (num_nodes,) + loop_attr.size()[1:]
      )
      full_loop_attr[loop_index] = loop_attr
  
      return full_loop_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 39 for calling a global function with example usage: get_self_loop_attr(edge_index, edge_attr, num_nodes) and returns: full_loop_attr