code: def __init__(
      self, n_classes, w2i, word_embed, params, model_meta_file=None
  ):
      self.params = params.copy()
      self.dropout_rate = self.params["dropout_rate"]
      self.use_dropout = self.dropout_rate > 0
      if model_meta_file is not None:
          saved_params = np.load(model_meta_file).item()
          self.params.update(saved_params)
      self.pc_param = dy.ParameterCollection()
      self.pc_embed = dy.ParameterCollection()
      self.builder = TreeLSTMBuilder(
          self.pc_param,
          self.pc_embed,
          w2i,
          self.params["wembed_size"],
          self.params["hidden_size"],
          word_embed,
      )
      self.W_ = self.pc_param.add_parameters(
          (n_classes, self.params["hidden_size"])
      )
      if model_meta_file is not None:
          self._load_param_embed(model_meta_file)
  
file_path: dynet/examples/treelstm/model.py
imports: import dynet
  import numpy
  import os
label_desc: Use image node_id 1 to create a new TreeLSTMClassifier object from inherited base classes: object with example: obj = TreeLSTMClassifier(n_classes, w2i, word_embed, params, model_meta_file)