code: def test_clahe(self, img):
      clip_limit: float = 2.0
      grid_size: Tuple = (8, 8)
      res = enhance.equalize_clahe(
          img, clip_limit=clip_limit, grid_size=grid_size
      )
      res_diff = enhance.equalize_clahe(
          img,
          clip_limit=clip_limit,
          grid_size=grid_size,
          slow_and_differentiable=True,
      )
      # NOTE: for next versions we need to improve the computation of the LUT
      # and test with a better image
      expected = torch.tensor(
          [
              [
                  [
                      0.1216,
                      0.8745,
                      0.9373,
                      0.9163,
                      0.8745,
                      0.8745,
                      0.9373,
                      0.8745,
                      0.8745,
                      0.8118,
                      0.9373,
                      0.8745,
                      0.8745,
                      0.8118,
                      0.8745,
                      0.8745,
                      0.8327,
                      0.8118,
                      0.8745,
                      1.0000,
                  ]
              ]
          ],
          dtype=res.dtype,
          device=res.device,
      )
      exp_diff = torch.tensor(
          [
              [
                  [
                      0.1250,
                      0.8752,
                      0.9042,
                      0.9167,
                      0.8401,
                      0.8852,
                      0.9302,
                      0.9120,
                      0.8750,
                      0.8370,
                      0.9620,
                      0.9077,
                      0.8750,
                      0.8754,
                      0.9204,
                      0.9167,
                      0.8370,
                      0.8806,
                      0.9096,
                      1.0000,
                  ]
              ]
          ],
          dtype=res.dtype,
          device=res.device,
      )
      self.assert_close(res[..., 0, :], expected, low_tolerance=True)
      self.assert_close(
          res_diff[..., 0, :], exp_diff, 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 13 for calling the TestEqualization obj's underlying member method code with example usage: obj.test_clahe(img) without return types