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
docstr: Transform response JSON to LUIS format.
  
  Args:
      data: input JSON data as a dictionary.
  
  Returns:
      The transformed input data.
docstr_tok: ["Transform","response","JSON","to","LUIS","format",".","Args",":","data",":","input","JSON","data","as","a","dictionary",".","Returns",":","The","transformed","input","data","."]
code: def normalise_response_json(
      self, data: Dict[Text, Any]
  ) -> Dict[Text, Any]:
      """Transform response JSON to LUIS format.
  
      Args:
          data: input JSON data as a dictionary.
  
      Returns:
          The transformed input data.
      """
      top = data.get(INTENT)
  
      return {
          "query": data[TEXT],
          "prediction": {
              "normalizedQuery": data[TEXT],
              "topIntent": top[INTENT_NAME_KEY] if top else None,
              "intents": self._intents(data),
              "entities": self._entities(data),
          },
      }
  
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 3 for calling the LUISEmulator obj's underlying member method code with example usage: obj.normalise_response_json(data) and returns: dict