code: def test_jit(self, device, dtype):
      batch_size, channels, height, width = 1, 2, 10, 20
      inp = torch.rand(
          batch_size,
          channels,
          height,
          width,
          device=device,
          dtype=dtype,
      )
      op = enhance.equalize_clahe
      op_script = torch.jit.script(op)
      self.assert_close(op(inp), op_script(inp))
  
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 8 for calling the TestEqualization obj's underlying member method code with example usage: obj.test_jit(device, dtype) without return types