code: def norm(
      edge_index,
      num_nodes,
      edge_weight=None,
      improved=False,
      dtype=None,
  ):
      if edge_weight is None:
          edge_weight = torch.ones(
              (edge_index.size(1),),
              dtype=dtype,
              device=edge_index.device,
          )
  
      fill_value = 1.0 if not improved else 2.0
      edge_index, edge_weight = add_remaining_self_loops(
          edge_index, edge_weight, fill_value, num_nodes
      )
  
      row, col = edge_index
      deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes)
      deg_inv_sqrt = deg.pow(-0.5)
      deg_inv_sqrt[deg_inv_sqrt == float("inf")] = 0
  
      return (
          edge_index,
          deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col],
      )
  
imports: import torch
  from torch_geometric.utils import add_remaining_self_loops
  from torch_scatter import scatter_add
label_desc: Use image node_id 1 for calling a global function with example usage: norm(edge_index, num_nodes, edge_weight, improved, dtype) and returns: edge_index,  unknown