code: def parsed_entity(
      entity_kind, entity_value, entity_resolved_value, entity_range
  ):
      """Create the items in the output of
          :meth:`snips_nlu.entity_parser.EntityParser.parse`
  
      Example:
          >>> resolved_value = dict(age=28, role="datascientist")
          >>> range = dict(start=0, end=6)
          >>> ent = parsed_entity("snipster", "adrien", resolved_value, range)
          >>> import json
          >>> print(json.dumps(ent, indent=4, sort_keys=True))
          {
              "entity_kind": "snipster",
              "range": {
                  "end": 6,
                  "start": 0
              },
              "resolved_value": {
                  "age": 28,
                  "role": "datascientist"
              },
              "value": "adrien"
          }
      """
      return {
          VALUE: entity_value,
          RESOLVED_VALUE: entity_resolved_value,
          ENTITY_KIND: entity_kind,
          RES_MATCH_RANGE: entity_range,
      }
  
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 10 for calling a global function with example usage: parsed_entity(entity_kind, entity_value, entity_resolved_value, entity_range) and returns: dict