code: def _test_dl(x, y, recs):
      assert len(recs) == 3
      assert recs[0].img is None
      # assert recs[0].img.shape == recs[1].img.shape == recs[2].img.shape == (384, 384, 3)
  
      assert x.shape == torch.Size([3, 3, 384, 384])
      if y is not None:
          assert y.shape[1] == 6
  
          # check batch size of 3 inside y
          assert np.all(
              np.unique(y[:, 0].numpy().astype(int))
              == np.array([0, 1, 2])
          )
  
          labels_in_batch = np.unique(y[:, 1].numpy().astype(int))
          possible_labels = np.arange(
              0, 4 + 1
          )  # 4 = number of classes in parser.class_map - 1 (given we don't have background)
          # is labels_in_batch a subset of possible_labels?
          assert (
              len(np.setdiff1d(labels_in_batch, possible_labels)) == 0
          )
  
imports: import pytest
  from icevision.all import *
label_desc: Use image node_id 1 for calling a global function with example usage: _test_dl(x, y, recs) without return types