code: def test_data_multilabel(request):
      return [
          # Multilabel input data of shape (N, C) and (N, C)
          (
              torch.randint(0, 2, size=(10, 4)).long(),
              torch.randint(0, 2, size=(10, 4)).long(),
              1,
          ),
          (
              torch.randint(0, 2, size=(10, 7)).long(),
              torch.randint(0, 2, size=(10, 7)).long(),
              1,
          ),
          # updated batches
          (
              torch.randint(0, 2, size=(50, 4)).long(),
              torch.randint(0, 2, size=(50, 4)).long(),
              16,
          ),
          (
              torch.randint(0, 2, size=(50, 7)).long(),
              torch.randint(0, 2, size=(50, 7)).long(),
              16,
          ),
          # Multilabel input data of shape (N, H, W)
          (
              torch.randint(0, 2, size=(10, 5, 10)).long(),
              torch.randint(0, 2, size=(10, 5, 10)).long(),
              1,
          ),
          (
              torch.randint(0, 2, size=(10, 4, 10)).long(),
              torch.randint(0, 2, size=(10, 4, 10)).long(),
              1,
          ),
          # updated batches
          (
              torch.randint(0, 2, size=(50, 5, 10)).long(),
              torch.randint(0, 2, size=(50, 5, 10)).long(),
              16,
          ),
          (
              torch.randint(0, 2, size=(50, 4, 10)).long(),
              torch.randint(0, 2, size=(50, 4, 10)).long(),
              16,
          ),
          # Multilabel input data of shape (N, C, H, W, ...) and (N, C, H, W, ...)
          (
              torch.randint(0, 2, size=(4, 5, 12, 10)).long(),
              torch.randint(0, 2, size=(4, 5, 12, 10)).long(),
              1,
          ),
          (
              torch.randint(0, 2, size=(4, 10, 12, 8)).long(),
              torch.randint(0, 2, size=(4, 10, 12, 8)).long(),
              1,
          ),
          # updated batches
          (
              torch.randint(0, 2, size=(50, 5, 12, 10)).long(),
              torch.randint(0, 2, size=(50, 5, 12, 10)).long(),
              16,
          ),
          (
              torch.randint(0, 2, size=(50, 10, 12, 8)).long(),
              torch.randint(0, 2, size=(50, 10, 12, 8)).long(),
              16,
          ),
      ][request.param]
  
imports: import os
  import pytest
  import torch
  from packaging.version import Version
  from sklearn.metrics import accuracy_score
  import ignite.distributed
  from ignite.engine import Engine
  from ignite.exceptions import NotComputableError
  from ignite.metrics import Accuracy
label_desc: Use image node_id 12 for calling a global function with example usage: test_data_multilabel(request) and returns: list