class_docstr: Benchmark for restoration routines in scikit image.
code: def setup(self):
      nz = 32
      self.volume_f64 = (
          np.stack(
              [
                  camera()[::2, ::2],
              ]
              * nz,
              axis=-1,
          ).astype(float)
          / 255
      )
      self.sigma = 0.02
      self.psf_f64 = np.ones((5, 5, 5)) / 125
      self.psf_f32 = self.psf_f64.astype(np.float32)
      self.volume_f64 = ndi.convolve(self.volume_f64, self.psf_f64)
      self.volume_f64 += self.sigma * np.random.randn(
          *self.volume_f64.shape
      )
      self.volume_f32 = self.volume_f64.astype(np.float32)
  
imports: import inspect
  import numpy
  import scipy.ndimage
  from skimage.data import camera
  from skimage import restoration, data, color
  from skimage.morphology import binary_dilation
  from .None import _channel_kwarg, _skip_slow
label_desc: Use image node_id 1 for calling the DeconvolutionSuite obj's underlying member method code with example usage: obj.setup() without return types