code: def gcn_norm(  # noqa: F811
      edge_index,
      edge_weight,
      num_nodes,
      improved,
      add_self_loops,
      flow,
      dtype,
  ):
      # type: (Tensor, OptTensor, Optional[int], bool, bool, str, Optional[int]) -> OptPairTensor  # noqa
      pass
  
imports: from typing import Optional
  import torch
  from torch import Tensor
  from torch.nn import Parameter
  from torch_geometric.nn.conv import MessagePassing
  from torch_geometric.nn.dense.linear import Linear
  from torch_geometric.nn.inits import zeros
  from torch_geometric.typing import Adj, OptPairTensor, OptTensor, SparseTensor, torch_sparse
  from torch_geometric.utils import add_remaining_self_loops
  from torch_geometric.utils import add_self_loops
  from torch_geometric.utils import is_torch_sparse_tensor, scatter, spmm, to_edge_index
  from torch_geometric.utils.num_nodes import maybe_num_nodes
  from torch_geometric.utils.sparse import set_sparse_value
label_desc: Use image node_id 1 for calling a global function with example usage: gcn_norm(edge_index, edge_weight, num_nodes, improved, add__loops, flow, dtype) without return types