code: def test_analyze_trees(self, wordvec_pretrain_file):
      test_str = "(ROOT (S (NP (PRP I)) (VP (VBP wan) (S (VP (TO na) (VP (VB lick) (NP (NP (NNP Sh'reyan) (POS 's)) (NNS antennae))))))))  (ROOT (S (NP (DT This) (NN interface)) (VP (VBZ sucks))))"
  
      test_tree = tree_reader.read_trees(test_str)
      assert len(test_tree) == 2
  
      args = [
          "--pattn_num_layers",
          "0",
          "--lattn_d_proj",
          "0",
          "--hidden_size",
          "20",
          "--delta_embedding_dim",
          "10",
      ]
      tr = build_trainer(wordvec_pretrain_file, *args)
  
      results = tr.model.analyze_trees(test_tree)
      assert len(results) == 2
      assert len(results[0].predictions) == 1
      assert results[0].predictions[0].tree == test_tree[0]
      assert results[0].state is not None
      assert isinstance(results[0].state.score, torch.Tensor)
      assert results[0].state.score.shape == torch.Size([])
      assert len(results[0].constituents) == 9
      assert results[0].constituents[-1].value == test_tree[0]
      # the way the results are built, the next-to-last entry
      # should be the thing just below the root
      assert (
          results[0].constituents[-2].value == test_tree[0].children[0]
      )
  
      assert len(results[1].predictions) == 1
      assert results[1].predictions[0].tree == test_tree[1]
      assert results[1].state is not None
      assert isinstance(results[1].state.score, torch.Tensor)
      assert results[1].state.score.shape == torch.Size([])
      assert len(results[1].constituents) == 4
      assert results[1].constituents[-1].value == test_tree[1]
      assert (
          results[1].constituents[-2].value == test_tree[1].children[0]
      )
  
imports: from collections import defaultdict
  import logging
  import pathlib
  import tempfile
  import pytest
  import torch
  from torch import optim
  from stanza import Pipeline
  from stanza.models import constituency_parser
  from stanza.models.common import pretrain
  from stanza.models.common.bert_embedding import load_bert, load_tokenizer
  from stanza.models.common.foundation_cache import FoundationCache
  from stanza.models.common.utils import set_random_seed
  from stanza.models.constituency import lstm_model
  from stanza.models.constituency import trainer
  from stanza.models.constituency import tree_reader
  from stanza.tests import *
label_desc: Use image node_id 18 for calling the TestTrainer obj's underlying member method code with example usage: obj.test_analyze_trees(wordvec_pretrain_file) without return types