code: def pil_images_equal(img_a, img_b):
      diff = ImageChops.difference(img_a, img_b)
  
      return not diff.getbbox()
  
imports: import copy
  import unittest
  from avalanche.benchmarks.datasets.dataset_utils import default_dataset_location
  from avalanche.benchmarks.utils.data import AvalancheDataset
  from avalanche.benchmarks.utils.dataset_traversal_utils import single_flat_dataset
  from avalanche.benchmarks.utils.detection_dataset import DetectionDataset
  from avalanche.benchmarks.classic.cmnist import SplitMNIST
  from avalanche.benchmarks.utils.transform_groups import TransformGroups
  from avalanche.benchmarks.utils.transforms import MultiParamCompose, MultiParamTransformCallable, TupleTransform, flat_transforms_recursive
  import torch
  from PIL import ImageChops
  from torch import Tensor
  from torch.utils.data import DataLoader, ConcatDataset
  from torchvision.datasets import MNIST
  from torchvision.transforms import ToTensor, Compose, CenterCrop, Normalize, Lambda, RandomHorizontalFlip
  from torchvision.transforms.functional import to_tensor
  from PIL.Image import Image
  from tests.unit_tests_utils import get_fast_detection_datasets
label_desc: Use image node_id 1 for calling a global function with example usage: pil_images_equal(img_a, img_b) and returns: unknown