code: def exec_train(self, max_turns=1000):
      time_stamp = time.time()
      total_time = []
      best_acc = 0
      n_endure, endure_upper = 0, 10
      model_meta_file = None
      for i in range(max_turns):
          self.train.reset()
          time_start = time.time()
          for j, trees in enumerate(
              self.train.batches(batch_size=self.params["batch_size"]),
              1,
          ):
              dy.renew_cg()
              loss = self.model.losses_for_tree_batch(trees)
              loss += self.model.regularization_loss(
                  coef=self.params["regularization_strength"]
              )
              loss_value = loss.value()
              loss.backward()
              self.trainer_param.update()
              self.trainer_embed.update()
  
              if j % 50 == 0:
                  self.trainer_param.status()
                  print(loss_value)
          time_epoch = time.time() - time_start
          total_time.append(time_epoch)
          print("epoch {} time {}".format(i, time_epoch))
          self.trainer_param.learning_rate *= self.params[
              "learning_rate_decay"
          ]
          self.trainer_embed.learning_rate *= self.params[
              "learning_rate_decay"
          ]
  
          acc = acc_eval(self.dev, self.model)
          best_acc, updated = max(acc, best_acc), acc > best_acc
          print("dev_acc=%.4f best_dev_acc=%.4f" % (acc, best_acc))
  
          if updated:
              self.model.delete(model_meta_file)
              model_meta_file = self.model.save(
                  self.params["save_dir"],
                  str(time_stamp) + "_" + str(i),
              )
              n_endure = 0
          else:
              n_endure += 1
              if n_endure > endure_upper:
                  break
      self._print_time_statistics(total_time)
      return best_acc, model_meta_file
  
file_path: dynet/examples/treelstm/scheduler.py
imports: import time
  import dynet
  import numpy
  from utils import acc_eval
label_desc: Use image node_id 2 for calling the Scheduler obj's underlying member method code with example usage: obj.exec_train(max_turns) and returns: best_acc,  model_meta_file