code: def test_exception(
      self, B, clip, grid, exception_type, expected_error_msg
  ):
      C, H, W = 1, 10, 20
      img = torch.rand(B, C, H, W)
      with pytest.raises(exception_type) as errinfo:
          enhance.equalize_clahe(img, clip, grid)
      assert expected_error_msg in str(errinfo)
  
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 4 for calling the TestEqualization obj's underlying member method code with example usage: obj.test_exception(B, clip, grid, exception_type, expected_error_msg) without return types