code: def predict_for_tree(self, tree, decorate=True, training=True):
      h, _ = self.builder.expr_for_tree(tree, decorate, training)
      if training:
          for node in tree.nonterms_iter():
              e = (
                  dy.dropout(node._e, self.dropout_rate)
                  if self.use_dropout
                  else node._e
              )
              node._logits = self.W_ * e
      else:
          tree._logits = self.W_ * h
          return tree._logits.npvalue()
  
file_path: dynet/examples/treelstm/model.py
imports: import dynet
  import numpy
  import os
label_desc: Use image node_id 2 for calling the TreeLSTMClassifier obj's underlying member method code with example usage: obj.predict_for_tree(tree, decorate, training) and returns: tree