code: def test_full_suite_coco_torch(device):
      if torch.cuda.is_available():
          coco_train_gpu = coco_torch.load_dataset(
              train=True,
              object_type="VisionData",
              n_samples=200,
              device=device,
              pin_memory=False,
          )
          coco_test_gpu = coco_torch.load_dataset(
              train=False,
              object_type="VisionData",
              n_samples=200,
              device=device,
              pin_memory=False,
          )
          suite = full_suite(
              imaginery_kwarg="just to make sure all checks have kwargs in the init"
          )
          arguments = (
              dict(
                  train_dataset=coco_train_gpu,
                  test_dataset=coco_test_gpu,
              ),
          )
          for args in arguments:
              result = suite.run(**args)
              length = get_expected_results_length(suite, args)
              validate_suite_result(result, length)
  
file_path: deepchecks/tests/vision/gpu_tests/full_suite_test.py
imports: import tensorflow
  import torch
  from deepchecks.vision.datasets.classification import mnist_tensorflow
  from deepchecks.vision.datasets.classification import mnist_torch
  from deepchecks.vision.datasets.detection import coco_tensorflow, coco_torch
  from deepchecks.vision.suites.default_suites import full_suite
  from tests.common import get_expected_results_length, validate_suite_result
label_desc: Use image node_id 3 for calling a global function with example usage: test_full_suite_coco_torch(device) without return types