docstr: Train with the punct augmentation set to zero
  
  Distinguishs cases where training works w/ or w/o augmentation
docstr_tok: ["Train","with","the","punct","augmentation","set","to","zero","Distinguishs","cases","where","training","works","w\/","or","w\/o","augmentation"]
code: def test_train_zero_augment(self, tmp_path, wordvec_pretrain_file):
      """
      Train with the punct augmentation set to zero
  
      Distinguishs cases where training works w/ or w/o augmentation
      """
      extra_args = ["--augment_nopunct", "0.0"]
      trainer = self.run_training(
          tmp_path,
          wordvec_pretrain_file,
          TRAIN_DATA,
          DEV_DATA,
          extra_args=extra_args,
      )
  
imports: import os
  import pytest
  import torch
  from stanza.models import tagger
  from stanza.models.common import pretrain
  from stanza.models.pos.trainer import Trainer
  from stanza.tests import TEST_WORKING_DIR, TEST_MODELS_DIR
  from stanza.utils.training.common import choose_pos_charlm, build_charlm_args
label_desc: Use image node_id 7 for calling the TestTagger obj's underlying member method code with example usage: obj.test_train_zero_augment(tmp_path, wordvec_pretrain_file) without return types