code: def test_float_input(self, device, dtype):
      data = torch.ones(2, 3, 1, 1, device=device, dtype=dtype)
      data += 2
  
      mean: float = 2.0
      std: float = 0.5
  
      # expected output
      expected = torch.ones_like(data) + 2.5
  
      f = kornia.enhance.Denormalize(mean, std)
      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 4 for calling the TestDenormalize obj's underlying member method code with example usage: obj.test_float_input(device, dtype) without return types