code: def test_no_denoising_for_small_h(fast_mode, dtype):
      img = np.zeros((40, 40))
      img[10:-10, 10:-10] = 1.0
      img += 0.3 * np.random.standard_normal(img.shape)
      img = img.astype(dtype)
      # very small h should result in no averaging with other patches
      denoised = restoration.denoise_nl_means(
          img, 7, 5, 0.01, fast_mode=fast_mode, channel_axis=None
      )
      assert np.allclose(denoised, img)
      denoised = restoration.denoise_nl_means(
          img, 7, 5, 0.01, fast_mode=fast_mode, channel_axis=None
      )
      assert np.allclose(denoised, img)
  
imports: import functools
  import itertools
  import numpy
  import pytest
  from numpy.testing import assert_array_almost_equal, assert_array_equal, assert_warns
  from skimage import color, data, img_as_float, restoration
  from skimage._shared._warnings import expected_warnings
  from skimage._shared.utils import _supported_float_type, slice_at_axis
  from skimage.metrics import peak_signal_noise_ratio, structural_similarity
  from skimage.restoration._denoise import _wavelet_threshold
label_desc: Use image node_id 34 for calling a global function with example usage: test_no_denoising_for_small_h(fast_mode, dtype) without return types