class_docstr: Entity extractor which uses SpaCy.
class_docstr_tok: ["Entity","extractor","which","uses","SpaCy","."]
code: def _extract_entities(doc: "Doc") -> List[Dict[Text, Any]]:
      entities = [
          {
              "entity": ent.label_,
              "value": ent.text,
              "start": ent.start_char,
              "confidence": None,
              "end": ent.end_char,
          }
          for ent in doc.ents
      ]
      return entities
  
imports: import typing
  from typing import Any, Dict, List, Text, Type
  from rasa.engine.graph import ExecutionContext, GraphComponent
  from rasa.engine.recipes.default_recipe import DefaultV1Recipe
  from rasa.engine.storage.resource import Resource
  from rasa.engine.storage.storage import ModelStorage
  from rasa.shared.nlu.constants import ENTITIES, TEXT
  from rasa.nlu.utils.spacy_utils import SpacyModel, SpacyNLP
  from rasa.nlu.extractors.extractor import EntityExtractorMixin
  from rasa.shared.nlu.training_data.message import Message
label_desc: Use image node_id 7 for calling the SpacyEntityExtractor obj's underlying member method code with example usage: obj._extract_entities(doc) and returns: entities