code: def test_cardinality(self, B, C, device, dtype):
      H, W = 10, 20
      if B is None:
          img = torch.rand(C, H, W, device=device, dtype=dtype)
      else:
          img = torch.rand(B, C, H, W, device=device, dtype=dtype)
      res = enhance.equalize_clahe(img)
      assert res.shape == img.shape
  
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 2 for calling the TestEqualization obj's underlying member method code with example usage: obj.test_cardinality(B, C, device, dtype) without return types