class_docstr: Emulates the response format of the LUIS Endpoint API v3.0 /predict endpoint.
  
  https://westcentralus.dev.cognitive.microsoft.com/docs/services/luis-endpoint-api-v3-0/
  https://docs.microsoft.com/en-us/azure/cognitive-services/LUIS/luis-concept-data-extraction?tabs=V3
code: def _intents(self, data: Dict[Text, Any]) -> Dict[Text, Any]:
      if data.get(INTENT_RANKING_KEY):
          return {
              intent[INTENT_NAME_KEY]: {
                  "score": intent[PREDICTED_CONFIDENCE_KEY]
              }
              for intent in data[INTENT_RANKING_KEY]
          }
  
      top = data.get(INTENT)
      if not top:
          return {}
  
      return {
          top[INTENT_NAME_KEY]: {"score": top[PREDICTED_CONFIDENCE_KEY]}
      }
  
file_path: rasa/rasa/nlu/emulators/luis.py
imports: from typing import Any, Dict, Text
  from rasa.nlu.emulators.emulator import Emulator
  from rasa.shared.nlu.constants import ENTITIES, ENTITY_ATTRIBUTE_TYPE, ENTITY_ATTRIBUTE_ROLE, ENTITY_ATTRIBUTE_VALUE, ENTITY_ATTRIBUTE_START, ENTITY_ATTRIBUTE_END, EXTRACTOR, INTENT_RANKING_KEY, TEXT, INTENT, INTENT_NAME_KEY, PREDICTED_CONFIDENCE_KEY
label_desc: Use image node_id 1 for calling the LUISEmulator obj's underlying member method code with example usage: obj._intents(data) and returns: dict, unknown, dict