code: def test_unsharp_masking_with_different_radii(
      radius, shape, multichannel, preserve
  ):
      amount = 1.0
      dtype = np.float64
      array = (np.random.random(shape) * 96).astype(dtype)
      if preserve is False:
          array /= max(np.abs(array).max(), 1.0)
      channel_axis = -1 if multichannel else None
      output = unsharp_mask(
          array,
          radius,
          amount,
          preserve_range=preserve,
          channel_axis=channel_axis,
      )
      assert output.dtype in [np.float32, np.float64]
      assert output.shape == shape
  
file_path: scikit-image/skimage/filters/tests/test_unsharp_mask.py
imports: import numpy
  import pytest
  from skimage._shared.utils import _supported_float_type
  from skimage.filters import unsharp_mask
label_desc: Use image node_id 2 for calling a global function with example usage: test_unsharp_masking_with_different_radii(radius, shape, multichannel, preserve) without return types