code: def preprocess_feature(cls, feature, parameters):
      is_not_empty = 1 - np.isclose(feature, MISSING_VALUE)
      if parameters.feature_type == identify_types.BINARY:
          # Binary features are always 1 unless they are 0
          return ((feature != 0) * is_not_empty).astype(np.float32)
      if parameters.boxcox_lambda is not None:
          feature = stats.boxcox(
              np.maximum(
                  feature + parameters.boxcox_shift, BOX_COX_MARGIN
              ),
              parameters.boxcox_lambda,
          )
      # No *= to ensure consistent out-of-place operation.
      if parameters.feature_type == identify_types.PROBABILITY:
          feature = np.clip(feature, 0.01, 0.99)
          feature = special.logit(feature)
      elif parameters.feature_type == identify_types.QUANTILE:
          transformed_feature = np.zeros_like(feature)
          for i in six.moves.range(feature.shape[0]):
              transformed_feature[i] = cls.value_to_quantile(
                  feature[i], parameters.quantiles
              )
          feature = transformed_feature
      elif parameters.feature_type == identify_types.ENUM:
          possible_values = parameters.possible_values
          mapping = {}
          for i, possible_value in enumerate(possible_values):
              mapping[possible_value] = i
          output_feature = np.zeros(
              (len(feature), len(possible_values))
          )
          for i, val in enumerate(feature):
              if abs(val - MISSING_VALUE) < 1e-2:
                  # This check is required by the PT preprocessing but not C2
                  continue
              output_feature[i][mapping[val]] = 1.0
          return output_feature
      elif parameters.feature_type == identify_types.CONTINUOUS_ACTION:
          min_value = parameters.min_value
          max_value = parameters.max_value
          feature = (
              (feature - min_value)
              * ((1 - 1e-6) * 2 / (max_value - min_value))
              - 1
              + 1e-6
          )
      else:
          feature = feature - parameters.mean
          feature /= parameters.stddev
          feature = np.clip(
              feature, MIN_FEATURE_VALUE, MAX_FEATURE_VALUE
          )
      feature *= is_not_empty
      return feature
  
file_path: ReAgent/reagent/test/base/utils.py
imports: import collections
  import csv
  import numpy
  import six
  from reagent.preprocessing import identify_types
  from reagent.preprocessing.normalization import BOX_COX_MARGIN, MAX_FEATURE_VALUE, MIN_FEATURE_VALUE, MISSING_VALUE, NormalizationParameters
  from scipy import special, stats
label_desc: Use image node_id 2 for calling the NumpyFeatureProcessor obj's underlying member method code with example usage: obj.preprocess_feature(cls, feature, parameters) and returns: feature, unknown, output_feature