code: def test_preprocessing_network(self) -> None:
      feature_value_map = read_data()
  
      normalization_parameters = {}
      name_preprocessed_blob_map = {}
  
      for feature_name, feature_values in feature_value_map.items():
          normalization_parameters[
              feature_name
          ] = normalization.identify_parameter(
              feature_name,
              feature_values,
              feature_type=self._feature_type_override(feature_name),
          )
          feature_values[
              0
          ] = MISSING_VALUE  # Set one entry to MISSING_VALUE to test that
  
          preprocessor = Preprocessor(
              {feature_name: normalization_parameters[feature_name]},
              False,
          )
          feature_values_matrix = torch.from_numpy(
              np.expand_dims(feature_values, -1)
          )
          normalized_feature_values = preprocessor(
              feature_values_matrix,
              (feature_values_matrix != MISSING_VALUE),
          )
          name_preprocessed_blob_map[
              feature_name
          ] = normalized_feature_values.numpy()
  
      test_features = NumpyFeatureProcessor.preprocess(
          feature_value_map, normalization_parameters
      )
  
      for feature_name in feature_value_map:
          normalized_features = name_preprocessed_blob_map[feature_name]
          if feature_name != ENUM_FEATURE_ID:
              normalized_features = np.squeeze(normalized_features, -1)
  
          tolerance = 0.01
          if feature_name == BOXCOX_FEATURE_ID:
              # At the limit, boxcox has some numerical instability
              tolerance = 0.5
          non_matching = np.where(
              np.logical_not(
                  np.isclose(
                      normalized_features.flatten(),
                      test_features[feature_name].flatten(),
                      rtol=tolerance,
                      atol=tolerance,
                  )
              )
          )
          self.assertTrue(
              np.all(
                  np.isclose(
                      normalized_features.flatten(),
                      test_features[feature_name].flatten(),
                      rtol=tolerance,
                      atol=tolerance,
                  )
              ),
              "{} does not match: {} \n!=\n {}".format(
                  feature_name,
                  normalized_features.flatten()[non_matching],
                  test_features[feature_name].flatten()[non_matching],
              ),
          )
  
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 6 for calling the TestPreprocessing obj's underlying member method code with example usage: obj.test_preprocessing_network() without return types