code: def build_trainer(wordvec_pretrain_file, *args, treebank=TREEBANK):
      # TODO: build a fake embedding some other way?
      train_trees = tree_reader.read_trees(treebank)
      dev_trees = train_trees[-1:]
      silver_trees = []
  
      args = ["--wordvec_pretrain_file", wordvec_pretrain_file] + list(
          args
      )
      args = constituency_parser.parse_args(args)
  
      foundation_cache = FoundationCache()
      # might be None, unless we're testing loading an existing model
      model_load_name = args["load_name"]
  
      model, _, _, _ = trainer.build_trainer(
          args,
          train_trees,
          dev_trees,
          silver_trees,
          foundation_cache,
          model_load_name,
      )
      assert isinstance(model.model, lstm_model.LSTMModel)
      return model
  
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 1 for calling a global function with example usage: build_trainer(wordvec_pretrain_file) and returns: model