code: def extraction_result(intent, slots):
      """Create the items in the output of :meth:`.SnipsNLUEngine.parse` or
      :meth:`.IntentParser.parse` when called with a defined ``top_n`` value
  
      This differs from :func:`.parsing_result` in that the input is omitted.
  
      Example:
  
          >>> intent_result = intent_classification_result("Greeting", 0.95)
          >>> internal_slot = unresolved_slot([6, 10], "Bill", "name",
          ... "greetee")
          >>> slots = [custom_slot(internal_slot, "William")]
          >>> res = extraction_result(intent_result, slots)
          >>> from snips_nlu.common.utils import json_string
          >>> print(json_string(res, indent=4, sort_keys=True))
          {
              "intent": {
                  "intentName": "Greeting",
                  "probability": 0.95
              },
              "slots": [
                  {
                      "entity": "name",
                      "range": {
                          "end": 10,
                          "start": 6
                      },
                      "rawValue": "Bill",
                      "slotName": "greetee",
                      "value": {
                          "kind": "Custom",
                          "value": "William"
                      }
                  }
              ]
          }
      """
      return {RES_INTENT: intent, RES_SLOTS: slots}
  
file_path: snips-nlu/snips_nlu/result.py
imports: from __future__ import unicode_literals
  from snips_nlu.constants import RES_ENTITY, RES_INPUT, RES_INTENT, RES_INTENT_NAME, RES_MATCH_RANGE, RES_PROBA, RES_RAW_VALUE, RES_SLOTS, RES_SLOT_NAME, RES_VALUE, ENTITY_KIND, RESOLVED_VALUE, VALUE
label_desc: Use image node_id 7 for calling a global function with example usage: extraction_result(intent, slots) and returns: dict