code: def get_regularization_factor(dataset):
      import numpy as np
  
      intents = dataset[INTENTS]
      nb_utterances = [
          len(intent[UTTERANCES]) for intent in itervalues(intents)
      ]
      avg_utterances = np.mean(nb_utterances)
      total_utterances = sum(nb_utterances)
      alpha = 1.0 / (4 * (total_utterances + 5 * avg_utterances))
      return alpha
  
imports: from __future__ import division, unicode_literals
  import itertools
  import re
  from builtins import next, range, str
  from copy import deepcopy
  from uuid import uuid4
  from future.utils import iteritems, itervalues
  from snips_nlu.constants import DATA, ENTITY, INTENTS, TEXT, UNKNOWNWORD, UTTERANCES
  from snips_nlu.data_augmentation import augment_utterances
  from snips_nlu.dataset import get_text_from_chunks
  from snips_nlu.entity_parser.builtin_entity_parser import is_builtin_entity
  from snips_nlu.preprocessing import tokenize_light
  from snips_nlu.resources import get_noise
label_desc: Use image node_id 2 for calling a global function with example usage: get_regularization_factor(dataset) and returns: alpha