code: def test_ahe(self, img):
      clip_limit: float = 0.0
      grid_size: Tuple = (8, 8)
      res = enhance.equalize_clahe(
          img, clip_limit=clip_limit, grid_size=grid_size
      )
      # NOTE: for next versions we need to improve the computation of the LUT
      # and test with a better image
      self.assert_close(
          res[..., 0, :],
          torch.tensor(
              [
                  [
                      [
                          0.2471,
                          0.4980,
                          0.7490,
                          0.6667,
                          0.4980,
                          0.4980,
                          0.7490,
                          0.4993,
                          0.4980,
                          0.2471,
                          0.7490,
                          0.4993,
                          0.4980,
                          0.2471,
                          0.4980,
                          0.4993,
                          0.3333,
                          0.2471,
                          0.4980,
                          1.0000,
                      ]
                  ]
              ],
              dtype=res.dtype,
              device=res.device,
          ),
          low_tolerance=True,
      )
  
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 12 for calling the TestEqualization obj's underlying member method code with example usage: obj.test_ahe(img) without return types