code: def test_invert_colorize(rgb_min):
      transfo = ColorInversion(min_val=rgb_min)
      input_t = torch.ones((8, 3, 32, 32), dtype=torch.float32)
      out = transfo(input_t)
      assert torch.all(out <= 1 - rgb_min + 1e-4)
      assert torch.all(out >= 0)
  
      input_t = torch.full((8, 3, 32, 32), 255, dtype=torch.uint8)
      out = transfo(input_t)
      assert torch.all(
          out <= int(math.ceil(255 * (1 - rgb_min + 1e-4)))
      )
      assert torch.all(out >= 0)
  
      # FP16
      input_t = torch.ones((8, 3, 32, 32), dtype=torch.float16)
      out = transfo(input_t)
      assert out.dtype == torch.float16
  
file_path: doctr/tests/pytorch/test_transforms_pt.py
imports: import math
  import numpy
  import pytest
  import torch
  from doctr.transforms import ChannelShuffle, ColorInversion, GaussianNoise, RandomCrop, RandomHorizontalFlip, RandomRotate, RandomShadow, Resize
  from doctr.transforms.functional import crop_detection, rotate_sample
label_desc: Use image node_id 2 for calling a global function with example usage: test_invert_colorize(rgb_min) without return types