code: def test_get_raw_table(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 = process(
          path=os.path.join(test_dir_path, "test_datasets"),
          train="sample_table_small.csv",
          id_attr="id",
          embeddings=ft,
          embeddings_cache_path="",
          pca=False,
      )
  
      train_raw = train.get_raw_table()
      ori_train = pd.read_csv(
          os.path.join(
              test_dir_path, "test_datasets", "sample_table_small.csv"
          )
      )
      self.assertEqual(set(train_raw.columns), set(ori_train.columns))
  
      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
  import pandas
  from nose.tools import *
  import torch
  from deepmatcher.data.dataset import *
  from deepmatcher.data.field import FastText, MatchingField
  from deepmatcher.data.process import _make_fields, process
  from torchtext.utils import unicode_csv_reader
  from urllib.parse import urljoin
  from urllib.request import pathname2url
label_desc: Use image node_id 1 for calling the GetRawTableTestCases obj's underlying member method code with example usage: obj.test_get_raw_table() without return types