code: def losses_for_tree(self, tree, summation=True):
      self.predict_for_tree(tree, decorate=True, training=True)
      nodes = tree.nonterms()
      losses = [
          dy.pickneglogsoftmax(nt._logits, nt.label) for nt in nodes
      ]
      return dy.esum(losses) if summation else losses, len(nodes)
  
file_path: dynet/examples/treelstm/model.py
imports: import dynet
  import numpy
  import os
label_desc: Use image node_id 3 for calling the TreeLSTMClassifier obj's underlying member method code with example usage: obj.losses_for_tree(tree, summation) and returns: unknown,  len