code: def test_tencrop_image2label():
      x = (
          torch.tensor(
              [[1, 2, 3, 4], [5, 6, 7, 8], [9, 0, 1, 2], [3, 4, 5, 6]]
          )
          .unsqueeze(0)
          .unsqueeze(0)
          .float()
      )
      model = SumAll()
  
      output = tta.tencrop_image2label(model, x, (2, 2))
      expected = (
          2
          * (
              (1 + 2 + 5 + 6)
              + (3 + 4 + 7 + 8)
              + (9 + 0 + 3 + 4)
              + (1 + 2 + 5 + 6)
              + (6 + 7 + 0 + 1)
          )
      ) / 10
  
      assert int(output) == expected
  
imports: from collections import defaultdict
  import cv2
  import numpy
  import pytest
  import torch
  from torch import nn
  from pytorch_toolbelt.inference import tta
  from pytorch_toolbelt.utils.torch_utils import to_numpy
label_desc: Use image node_id 9 for calling a global function with example usage: test_tencrop_image2label() without return types