code: def test_data_binary(request):
      return [
          # Binary accuracy on input of shape (N, 1) or (N, )
          (
              torch.randint(0, 2, size=(10,)).long(),
              torch.randint(0, 2, size=(10,)).long(),
              1,
          ),
          (
              torch.randint(0, 2, size=(10, 1)).long(),
              torch.randint(0, 2, size=(10, 1)).long(),
              1,
          ),
          # updated batches
          (
              torch.randint(0, 2, size=(50,)).long(),
              torch.randint(0, 2, size=(50,)).long(),
              16,
          ),
          (
              torch.randint(0, 2, size=(50, 1)).long(),
              torch.randint(0, 2, size=(50, 1)).long(),
              16,
          ),
          # Binary accuracy on input of shape (N, L)
          (
              torch.randint(0, 2, size=(10, 5)).long(),
              torch.randint(0, 2, size=(10, 5)).long(),
              1,
          ),
          (
              torch.randint(0, 2, size=(10, 8)).long(),
              torch.randint(0, 2, size=(10, 8)).long(),
              1,
          ),
          # updated batches
          (
              torch.randint(0, 2, size=(50, 5)).long(),
              torch.randint(0, 2, size=(50, 5)).long(),
              16,
          ),
          (
              torch.randint(0, 2, size=(50, 8)).long(),
              torch.randint(0, 2, size=(50, 8)).long(),
              16,
          ),
          # Binary accuracy on input of shape (N, H, W, ...)
          (
              torch.randint(0, 2, size=(4, 1, 12, 10)).long(),
              torch.randint(0, 2, size=(4, 1, 12, 10)).long(),
              1,
          ),
          (
              torch.randint(0, 2, size=(15, 1, 20, 10)).long(),
              torch.randint(0, 2, size=(15, 1, 20, 10)).long(),
              1,
          ),
          # updated batches
          (
              torch.randint(0, 2, size=(50, 1, 12, 10)).long(),
              torch.randint(0, 2, size=(50, 1, 12, 10)).long(),
              16,
          ),
          (
              torch.randint(0, 2, size=(50, 1, 20, 10)).long(),
              torch.randint(0, 2, size=(50, 1, 20, 10)).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 5 for calling a global function with example usage: test_data_binary(request) and returns: list