code: def run(self, mol_graph, mol_line_graph):
      n_nodes = mol_graph.num_nodes()
  
      mol_graph.apply_edges(
          func=lambda edges: {"src_x": edges.src["x"]},
      )
      mol_line_graph.ndata.update(mol_graph.edata)
  
      e_repr = mol_line_graph.ndata
      bond_features = e_repr["x"]
      source_features = e_repr["src_x"]
  
      features = torch.cat([source_features, bond_features], 1)
      msg_input = self.W_i(features)
      mol_line_graph.ndata.update(
          {
              "msg_input": msg_input,
              "msg": F.relu(msg_input),
              "accum_msg": torch.zeros_like(msg_input),
          }
      )
      mol_graph.ndata.update(
          {
              "m": bond_features.new(n_nodes, self.hidden_size).zero_(),
              "h": bond_features.new(n_nodes, self.hidden_size).zero_(),
          }
      )
  
      for i in range(self.depth - 1):
          mol_line_graph.update_all(
              DGLF.copy_u("msg", "msg"), DGLF.sum("msg", "accum_msg")
          )
          mol_line_graph.apply_nodes(self.loopy_bp_updater)
  
      mol_graph.edata.update(mol_line_graph.ndata)
      mol_graph.update_all(
          DGLF.copy_e("msg", "msg"), DGLF.sum("msg", "m")
      )
      mol_graph.apply_nodes(self.gather_updater)
  
      return mol_graph
  
imports: import dgl
  import dgl.function
  import rdkit.Chem
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl import line_graph, mean_nodes
  from .chemutils import get_mol
label_desc: Use image node_id 3 for calling the DGLMPN obj's underlying member method code with example usage: obj.run(mol_graph, mol_line_graph) and returns: mol_graph