code: def test_evaluation_store_serialise(
      entity_predictions: List[dict], entity_targets: List[dict]
  ):
      from rasa.shared.nlu.training_data.formats.readerwriter import (
          TrainingDataWriter,
      )
  
      store = EvaluationStore(
          entity_predictions=entity_predictions,
          entity_targets=entity_targets,
      )
  
      targets, predictions = store.serialise()
  
      assert len(targets) == len(predictions)
  
      i_pred = 0
      i_target = 0
      for i, prediction in enumerate(predictions):
          target = targets[i]
          if prediction != "None" and target != "None":
              predicted = entity_predictions[i_pred]
              assert prediction == TrainingDataWriter.generate_entity(
                  predicted.get("text"), predicted
              )
              assert predicted.get("start") == entity_targets[
                  i_target
              ].get("start")
              assert predicted.get("end") == entity_targets[
                  i_target
              ].get("end")
  
          if prediction != "None":
              i_pred += 1
          if target != "None":
              i_target += 1
  
imports: import asyncio
  import sys
  from pathlib import Path
  import textwrap
  from typing import List, Text
  import pytest
  from _pytest.capture import CaptureFixture
  from _pytest.monkeypatch import MonkeyPatch
  import rasa.shared.utils.io
  import rasa.utils.io
  from rasa.core.agent import Agent
  from rasa.shared.core.events import UserUttered
  from rasa.core.test import EvaluationStore, WronglyClassifiedUserUtterance, WronglyPredictedAction
  from rasa.shared.core.trackers import DialogueStateTracker
  from rasa.shared.core.training_data.story_writer.yaml_story_writer import YAMLStoryWriter
  import rasa.model
  import rasa.cli.utils
  from rasa.nlu.test import NO_ENTITY
  import rasa.core
  from rasa.shared.nlu.constants import ENTITY_ATTRIBUTE_VALUE, ENTITY_ATTRIBUTE_START, ENTITY_ATTRIBUTE_END, ENTITY_ATTRIBUTE_TYPE, ENTITY_ATTRIBUTE_TEXT
  from rasa.shared.constants import LATEST_TRAINING_DATA_FORMAT_VERSION
label_desc: Use image node_id 11 for calling a global function with example usage: test_evaluation_store_serialise(entity_predictions, entity_targets) without return types