code: def img(self, device, dtype):
      height, width = 20, 20
      # TODO: test with a more realistic pattern
      img = (
          torch.arange(width, device=device)
          .div(float(width - 1))[None]
          .expand(height, width)[None][None]
      )
      return img
  
imports: from typing import Tuple
  import pytest
  import torch
  from torch.autograd import gradcheck
  from kornia import enhance
  from kornia.geometry import rotate
  from kornia.testing import BaseTester, tensor_to_gradcheck_var
label_desc: Use image node_id 10 for calling the TestEqualization obj's underlying member method code with example usage: obj.img(device, dtype) and returns: img