class_docstr: based on https://github.com/cgtuebingen/learning-blind-motion-deblurring/blob/master/synthblur/src/flow.cpp#L44
code: def visualize(self, nnf):
      assert len(nnf.shape) == 3
      assert nnf.shape[2] == 2
  
      RY, YG, GC = 15, 6, 4
      YG, GC, CB = 6, 4, 11
      BM, MR = 13, 6
      NCOLS = RY + YG + GC + CB + BM + MR
  
      fx = nnf[:, :, 0].astype(np.float32)
      fy = nnf[:, :, 1].astype(np.float32)
  
      h, w = fx.shape[:2]
      fx = fx.reshape([-1])
      fy = fy.reshape([-1])
  
      rad = np.sqrt(fx * fx + fy * fy)
  
      normalizer = max(rad.max(), 1)
      # This parameter controls how sensitive the visualization is to small displacement
      # The smaller it is, the more sensitive the visualization is.
      # We don't let it be smaller than 1 since we don't want to be sensitive to noise.
  
      a = np.arctan2(-fy, -fx) / np.pi
      fk = (a + 1.0) / 2.0 * (NCOLS - 1)
      k0 = fk.astype(np.int32)
      k1 = (k0 + 1) % NCOLS
      f = (fk - k0).astype(np.float32)
  
      color0 = self.wheel[k0, :]
      color1 = self.wheel[k1, :]
  
      f = np.stack([f, f, f], axis=-1)
      color = (1 - f) * color0 + f * color1
  
      color = 1 - (np.expand_dims(rad, axis=-1) / normalizer) * (
          1 - color
      )
  
      return color.reshape(h, w, 3)[:, :, ::-1]
  
imports: import numpy
label_desc: Use image node_id 4 for calling the Flow obj's underlying member method code with example usage: obj.visualize(nnf) and returns: color