code: def compute_loop_attr(  # noqa: F811
      edge_index: Tensor,
      edge_attr: Tensor,
      num_nodes: int,
      is_sparse: bool,
      fill_value: Optional[Union[float, Tensor, str]] = None,
  ) -> Tensor:
      if fill_value is None:
          size = (num_nodes,) + edge_attr.size()[1:]
          return edge_attr.new_ones(size)
  
      elif isinstance(fill_value, (int, float)):
          size = (num_nodes,) + edge_attr.size()[1:]
          return edge_attr.new_full(size, fill_value)
  
      elif isinstance(fill_value, Tensor):
          size = (num_nodes,) + edge_attr.size()[1:]
          loop_attr = fill_value.to(edge_attr.device, edge_attr.dtype)
          if edge_attr.dim() != loop_attr.dim():
              loop_attr = loop_attr.unsqueeze(0)
          return loop_attr.expand(size).contiguous()
  
      elif isinstance(fill_value, str):
          col = edge_index[0] if is_sparse else edge_index[1]
          return scatter(edge_attr, col, 0, num_nodes, fill_value)
  
      raise AttributeError("No valid 'fill_value' provided")
  
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 43 for calling a global function with example usage: compute_loop_attr(edge_index, edge_attr, num_nodes, is_sparse, fill_value) and returns: edge_attr, edge_attr, loop_attr, scatter