code: def utterance_to_sample(query_data, tagging_scheme, language):
      tokens, tags = [], []
      current_length = 0
      for chunk in query_data:
          chunk_tokens = tokenize(chunk[TEXT], language)
          tokens += [
              Token(
                  t.value,
                  current_length + t.start,
                  current_length + t.end,
              )
              for t in chunk_tokens
          ]
          current_length += len(chunk[TEXT])
          if SLOT_NAME not in chunk:
              tags += negative_tagging(len(chunk_tokens))
          else:
              tags += positive_tagging(
                  tagging_scheme, chunk[SLOT_NAME], len(chunk_tokens)
              )
      return {TOKENS: tokens, TAGS: tags}
  
imports: from __future__ import unicode_literals
  from builtins import range
  from enum import Enum, unique
  from snips_nlu.constants import END, SLOT_NAME, START, TEXT
  from snips_nlu.preprocessing import Token, tokenize
  from snips_nlu.result import unresolved_slot
label_desc: Use image node_id 13 for calling a global function with example usage: utterance_to_sample(query_data, tagging_scheme, language) and returns: dict