code: def test_pad():
      a = [1, 2, 3]
      b = [4, 5]
      c = [7]
      ret = PadCollator(pad_val=1)((a, b, c))
      assert torch.all(
          ret == torch.as_tensor([[1, 2, 3], [4, 5, 1], [7, 1, 1]])
      )
  
      ret = PadCollator(pad_val=1, round_to=2)((a, b, c))
      assert torch.all(
          ret
          == torch.as_tensor([[1, 2, 3, 1], [4, 5, 1, 1], [7, 1, 1, 1]])
      )
  
      ret = PadCollator(pad_val=1, max_length=4)((a, b, c))
      assert torch.all(
          ret
          == torch.as_tensor([[1, 2, 3, 1], [4, 5, 1, 1], [7, 1, 1, 1]])
      )
  
      ret, valid_lengths = PadCollator(pad_val=1, ret_length=True)(
          (a, b, c)
      )
      assert torch.all(valid_lengths == torch.as_tensor([3, 2, 1]))
  
      a = [[1, 4], [2, 5], [3, 6]]
      b = [[7], [8], [9]]
      c = [[10, 11, 12], [13, 14, 15], [16, 17, 18]]
  
      ret = PadCollator(axis=1)((a, b, c))
      assert torch.all(
          ret
          == torch.as_tensor(
              [
                  [[1, 4, 0], [2, 5, 0], [3, 6, 0]],
                  [[7, 0, 0], [8, 0, 0], [9, 0, 0]],
                  [[10, 11, 12], [13, 14, 15], [16, 17, 18]],
              ]
          )
      )
  
imports: import torch
  from autogluon.multimodal.data.collator import DictCollator, ListCollator, PadCollator, StackCollator, TupleCollator
label_desc: Use image node_id 2 for calling a global function with example usage: test_pad() without return types