code: def _test_learn2learning_training_strategies(
      gpus, training_strategy, tmpdir, accelerator=None, strategy=None
  ):
      train_dir = Path(tmpdir / "train")
      train_dir.mkdir()
  
      (train_dir / "a").mkdir()
      pa_1 = train_dir / "a" / "1.png"
      pa_2 = train_dir / "a" / "2.png"
      pb_1 = train_dir / "b" / "1.png"
      pb_2 = train_dir / "b" / "2.png"
      image_size = (96, 96)
      _rand_image(image_size).save(pa_1)
      _rand_image(image_size).save(pa_2)
  
      (train_dir / "b").mkdir()
      _rand_image(image_size).save(pb_1)
      _rand_image(image_size).save(pb_2)
  
      n = 5
  
      dm = ImageClassificationData.from_files(
          train_files=[str(pa_1)] * n
          + [str(pa_2)] * n
          + [str(pb_1)] * n
          + [str(pb_2)] * n,
          train_targets=[0] * n + [1] * n + [2] * n + [3] * n,
          batch_size=1,
          num_workers=0,
          transform_kwargs={"image_size": image_size},
      )
  
      model = ImageClassifier(
          backbone="resnet18",
          training_strategy=training_strategy,
          training_strategy_kwargs={
              "ways": dm.num_classes,
              "shots": 4,
              "meta_batch_size": 4,
          },
      )
  
      trainer = Trainer(fast_dev_run=2, gpus=gpus, strategy=strategy)
  
      trainer.fit(model, datamodule=dm)
  
imports: import os
  from pathlib import Path
  import pytest
  import torch
  from flash import Trainer
  from flash.core.data.io.input import DataKeys
  from flash.core.utilities.imports import _LEARN2LEARN_AVAILABLE, _TOPIC_IMAGE_AVAILABLE
  from flash.image import ImageClassificationData, ImageClassifier
  from flash.image.classification.adapters import TRAINING_STRATEGIES
  from torch.utils.data import DataLoader
  from tests.image.classification.test_data import _rand_image
label_desc: Use image node_id 3 for calling a global function with example usage: _test_learn2learning_training_strategies(gpus, training_strategy, tmpdir, accelerator, strategy) without return types