code: def test_process_unlabeled_1(self):
      vectors_cache_dir = ".cache"
      if os.path.exists(vectors_cache_dir):
          shutil.rmtree(vectors_cache_dir)
  
      data_cache_path = os.path.join(
          test_dir_path, "test_datasets", "cacheddata.pth"
      )
      if os.path.exists(data_cache_path):
          os.remove(data_cache_path)
  
      vec_dir = os.path.abspath(
          os.path.join(test_dir_path, "test_datasets")
      )
      filename = "fasttext_sample.vec.zip"
      url_base = urljoin("file:", pathname2url(vec_dir)) + os.path.sep
      ft = FastText(
          filename, url_base=url_base, cache=vectors_cache_dir
      )
  
      train, valid, test = process(
          path=os.path.join(test_dir_path, "test_datasets"),
          train="test_train.csv",
          validation="test_valid.csv",
          test="test_test.csv",
          id_attr="id",
          ignore_columns=("left_id", "right_id"),
          embeddings=ft,
          embeddings_cache_path="",
          pca=True,
      )
  
      model_save_path = "sif_model.pth"
      model = MatchingModel(attr_summarizer="sif")
      model.run_train(
          train,
          valid,
          epochs=1,
          batch_size=8,
          best_save_path=model_save_path,
          pos_neg_ratio=3,
      )
  
      test_unlabeled = process_unlabeled(
          path=os.path.join(
              test_dir_path, "test_datasets", "test_test.csv"
          ),
          trained_model=model,
          ignore_columns=("left_id", "right_id"),
      )
  
      self.assertEqual(
          test_unlabeled.all_text_fields, test.all_text_fields
      )
  
      if os.path.exists(model_save_path):
          os.remove(model_save_path)
  
      if os.path.exists(data_cache_path):
          os.remove(data_cache_path)
  
      if os.path.exists(vectors_cache_dir):
          shutil.rmtree(vectors_cache_dir)
  
imports: import io
  import os
  import shutil
  import unittest
  from test import test_dir_path
  from nose.tools import *
  from deepmatcher.data.field import FastText
  from deepmatcher.data.process import _check_header, _make_fields, process, process_unlabeled
  from torchtext.utils import unicode_csv_reader
  from deepmatcher import MatchingModel
  from urllib.parse import urljoin
  from urllib.request import pathname2url
label_desc: Use image node_id 1 for calling the ProcessUnlabeledTestCases obj's underlying member method code with example usage: obj.test_process_unlabeled_1() without return types