code: def HUnorm(
      image, HU_min=-1200, HU_max=600, HU_nan=-2000, multiply_255=True
  ):
      """
      Convert CT HU unit into uint8 values. First bound HU values by predfined min
      and max, and then normalize. Due to paddle.nn.conv3D doesn't support uint8, we need to convert
      the returned image as float32.
  
      image: 3D numpy array of raw HU values from CT series in [z, y, x] order.
      HU_min: float, min HU value.
      HU_max: float, max HU value.
      HU_nan: float, value for nan in the raw CT image.
      """
  
      if not isinstance(image, np.ndarray):
          image = np.array(image)
      image = np.nan_to_num(image, copy=False, nan=HU_nan)
  
      # normalize to [0, 1]
      image = (image - HU_min) / (HU_max - HU_min)
      if multiply_255:
          image *= 255
          np.clip(image, 0, 255, out=image)
  
      return image
  
imports: import sys
  import os
  import tools.preprocess_utils.global_var
label_desc: Use image node_id 3 for calling a global function with example usage: HUnorm(image, HU_min, HU_max, HU_nan, multiply_255) and returns: image