docstr: The training procedure which computes the prediction loss given the
  ground truth tree
docstr_tok: ["The","training","procedure","which","computes","the","prediction","loss","given","the","ground","truth","tree"]
code: def forward(self, mol_trees, tree_vec):
      """
      The training procedure which computes the prediction loss given the
      ground truth tree
      """
      mol_tree_batch = batch(mol_trees)
      mol_tree_batch_lg = line_graph(
          mol_tree_batch, backtracking=False, shared=True
      )
      n_trees = len(mol_trees)
  
      return self.run(
          mol_tree_batch, mol_tree_batch_lg, n_trees, tree_vec
      )
  
file_path: dgl/examples/pytorch/jtnn/jtnn/jtnn_dec.py
imports: import dgl.function
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl import batch, dfs_labeled_edges_generator, line_graph
  from .chemutils import enum_assemble_nx, get_mol
  from .mol_tree_nx import DGLMolTree
  from .nnutils import cuda, GRUUpdate, tocpu
label_desc: Use image node_id 2 for calling the DGLJTNNDecoder obj's underlying member method code with example usage: obj.forward(mol_trees, tree_vec) and returns: self