code: def default_normalizer(feats, min_value=None, max_value=None):
      normalization_types = [
          NormalizationParameters(
              feature_type="BINARY",
              boxcox_lambda=None,
              boxcox_shift=None,
              mean=0,
              stddev=1,
              possible_values=None,
              quantiles=None,
              min_value=min_value,
              max_value=max_value,
          ),
          NormalizationParameters(
              feature_type="PROBABILITY",
              boxcox_lambda=None,
              boxcox_shift=None,
              mean=0,
              stddev=1,
              possible_values=None,
              quantiles=None,
              min_value=min_value,
              max_value=max_value,
          ),
          NormalizationParameters(
              feature_type="CONTINUOUS",
              boxcox_lambda=None,
              boxcox_shift=None,
              mean=0,
              stddev=1,
              possible_values=None,
              quantiles=None,
              min_value=min_value,
              max_value=max_value,
          ),
          NormalizationParameters(
              feature_type="BOXCOX",
              boxcox_lambda=1,
              boxcox_shift=1,
              mean=0,
              stddev=1,
              possible_values=None,
              quantiles=None,
              min_value=min_value,
              max_value=max_value,
          ),
          NormalizationParameters(
              feature_type="QUANTILE",
              boxcox_lambda=None,
              boxcox_shift=None,
              mean=0,
              stddev=1,
              possible_values=None,
              quantiles=[0, 1],
              min_value=min_value,
              max_value=max_value,
          ),
          NormalizationParameters(
              feature_type="ENUM",
              boxcox_lambda=None,
              boxcox_shift=None,
              mean=0,
              stddev=1,
              possible_values=[0, 1],
              quantiles=None,
              min_value=min_value,
              max_value=max_value,
          ),
      ]
      normalization = collections.OrderedDict(
          [
              (
                  feats[i],
                  normalization_types[i % len(normalization_types)],
              )
              for i in range(len(feats))
          ]
      )
      return normalization
  
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 1 for calling a global function with example usage: default_normalizer(feats, min_value, max_value) and returns: normalization