class_docstr: General GNN layer, with edge features
      
class_docstr_tok: ["General","GNN","layer",",","with","edge","features"]
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
      if is_undirected(edge_index, num_nodes=num_nodes):
          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
          norm = deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col]
      else:
          if cfg.gnn.flow == "source_to_target":
              deg = scatter_add(
                  edge_weight, row, dim=0, dim_size=num_nodes
              )
          else:
              deg = scatter_add(
                  edge_weight, col, dim=0, dim_size=num_nodes
              )
          deg_inv_sqrt = deg.pow(-1.0)
          deg_inv_sqrt[deg_inv_sqrt == float("inf")] = 0
          norm = (
              deg_inv_sqrt[row]
              if cfg.gnn.flow == "source_to_target"
              else deg_inv_sqrt[col]
          ) * edge_weight
  
      return edge_index, norm
  
imports: import torch
  import torch.nn
  from torch.nn import Parameter
  from torch_geometric.nn.conv import MessagePassing
  from torch_geometric.nn.inits import glorot, zeros
  from torch_geometric.utils import add_remaining_self_loops, is_undirected
  from torch_scatter import scatter_add
  from graphgym.config import cfg
label_desc: Use image node_id 3 for calling the GeneralEdgeConvLayerV2 obj's underlying member method code with example usage: obj.norm(edge_index, num_nodes, edge_weight, improved, dtype) and returns: edge_index,  norm