docstr: Runs a test of the trainer for a few iterations.
  
  Checks some basic properties of the saved model, but doesn't
  check for the accuracy of the results
docstr_tok: ["Runs","a","test","of","the","trainer","for","a","few","iterations",".","Checks","some","basic","properties","of","the","saved","model",",","but","doesn't","check","for","the","accuracy","of","the","results"]
code: def run_train_test(
      self,
      wordvec_pretrain_file,
      tmpdirname,
      num_epochs=5,
      extra_args=None,
      use_silver=False,
      exists_ok=False,
      foundation_cache=None,
  ):
      """
      Runs a test of the trainer for a few iterations.
  
      Checks some basic properties of the saved model, but doesn't
      check for the accuracy of the results
      """
      if extra_args is None:
          extra_args = []
      extra_args += ["--epochs", "%d" % num_epochs]
  
      train_treebank_file, eval_treebank_file = self.write_treebanks(
          tmpdirname
      )
      if use_silver:
          extra_args += ["--silver_file", str(eval_treebank_file)]
      args = self.training_args(
          wordvec_pretrain_file,
          tmpdirname,
          train_treebank_file,
          eval_treebank_file,
          *extra_args,
      )
  
      each_name = args["save_each_name"]
      if not exists_ok:
          assert not os.path.exists(args["save_name"])
      retag_pipeline = Pipeline(
          lang="en",
          processors="tokenize, pos",
          tokenize_pretokenized=True,
          dir=TEST_MODELS_DIR,
          foundation_cache=foundation_cache,
      )
      trained_model = trainer.train(
          args, None, each_name, [retag_pipeline]
      )
      # check that hooks are in the model if expected
      for p in trained_model.model.parameters():
          if p.requires_grad:
              if args["grad_clipping"] is not None:
                  assert len(p._backward_hooks) == 1
              else:
                  assert p._backward_hooks is None
  
      # check that the model can be loaded back
      assert os.path.exists(args["save_name"])
      tr = trainer.Trainer.load(
          args["save_name"],
          load_optimizer=True,
          foundation_cache=retag_pipeline.foundation_cache,
      )
      assert tr.optimizer is not None
      assert tr.scheduler is not None
      assert tr.epochs_trained >= 1
      for p in tr.model.parameters():
          if p.requires_grad:
              assert p._backward_hooks is None
  
      tr = trainer.Trainer.load(
          args["checkpoint_save_name"],
          load_optimizer=True,
          foundation_cache=retag_pipeline.foundation_cache,
      )
      assert tr.optimizer is not None
      assert tr.scheduler is not None
      assert tr.epochs_trained == num_epochs
  
      for i in range(1, num_epochs + 1):
          model_name = each_name % i
          assert os.path.exists(model_name)
          tr = trainer.Trainer.load(
              model_name,
              load_optimizer=True,
              foundation_cache=retag_pipeline.foundation_cache,
          )
          assert tr.epochs_trained == i
          assert tr.batches_trained == (4 * i if use_silver else 2 * i)
  
      return args, trained_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 9 for calling the TestTrainer obj's underlying member method code with example usage: obj.run_train_test(wordvec_pretrain_file, tmpdirname, num_epochs, extra_args, use_silver, exists_ok, foundation_cache) and returns: args,  trained_model