code: def test_columnvector(self) -> None:
      def format_input2output(test_keys, inp_form):
          test_data = {}
          for ky in test_keys:
              test_data[ky] = inp_form
          test_instance = transforms.ColumnVector(test_keys)
          output_data = test_instance(test_data)
          return output_data
  
      test_values = range(0, 5)
      test_keys = []
      for k in test_values:
          test_keys.append(str(k))
  
      # Possible input formats: tuple, list, torch.Tensor
      for n_len in [1, 3]:
          test_input_forms = [
              (np.ones((n_len, 1)), 0),
              n_len * [1],
              torch.tensor(np.ones((n_len, 1))),
          ]
          for inp_form in test_input_forms:
              output_data = format_input2output(test_keys, inp_form)
              for ky in test_keys:
                  self.assertEqual(output_data[ky].shape[0], n_len)
                  self.assertEqual(output_data[ky].shape[1], 1)
  
      # Input as in row format
      test_data = {}
      for ky in test_keys:
          test_data[ky] = (np.ones((1, 3)), 0)
      test_instance = transforms.ColumnVector(test_keys)
      with self.assertRaisesRegex(
          AssertionError, "Invalid shape for key"
      ):
          output_data = test_instance(test_data)
  
      # Input as unimplemented type (number)
      test_data = {}
      for ky in test_keys:
          test_data[ky] = 1
      test_instance = transforms.ColumnVector(test_keys)
      with self.assertRaisesRegex(NotImplementedError, "value of type"):
          output_data = test_instance(test_data)
  
imports: import unittest
  import numpy
  import numpy.testing
  import six
  import torch
  from reagent.preprocessing import identify_types, normalization, transforms
  from reagent.preprocessing.identify_types import BOXCOX, CONTINUOUS, DO_NOT_PREPROCESS, ENUM
  from reagent.preprocessing.normalization import MISSING_VALUE, NormalizationParameters, sort_features_by_normalization
  from reagent.preprocessing.preprocessor import Preprocessor
  from reagent.test.base.utils import NumpyFeatureProcessor
  from reagent.test.preprocessing.preprocessing_util import BOXCOX_FEATURE_ID, CONTINUOUS_FEATURE_ID, CONTINUOUS_SMALL_FEATURE_ID, ENUM_FEATURE_ID, id_to_type, PROBABILITY_FEATURE_ID, read_data
  from scipy import special
label_desc: Use image node_id 11 for calling the TestPreprocessing obj's underlying member method code with example usage: obj.test_columnvector() and returns: output_data