code: def preprocess(cls, features, parameters):
      result = {}
      for feature_name in features:
          result[feature_name] = cls.preprocess_feature(
              features[feature_name], parameters[feature_name]
          )
      return result
  
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 3 for calling the NumpyFeatureProcessor obj's underlying member method code with example usage: obj.preprocess(cls, features, parameters) and returns: result