code: def test_random_normalize_different_parameter_types(self, mean, std):
      f = kornia.enhance.Normalize(mean=mean, std=std)
      data = torch.ones(2, 3, 256, 313)
      if isinstance(mean, float):
          expected = (data - torch.as_tensor(mean)) / torch.as_tensor(
              std
          )
      else:
          expected = (
              data - torch.as_tensor(mean[0])
          ) / torch.as_tensor(std[0])
      self.assert_close(f(data), expected)
  
imports: import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  import kornia.testing
  from kornia.testing import BaseTester
label_desc: Use image node_id 11 for calling the TestNormalize obj's underlying member method code with example usage: obj.test_random_normalize_different_parameter_types(mean, std) without return types