class_docstr: Prepare dataset for image Colorization.
  
  Args:
     ab_thresh(float): Thresh value for setting mask value.
     p(float): Probability for ignoring hint in an iteration.
     num_points(int): Number of selected hints in an iteration.
     samp(str): Sample method, default is normal.
     use_avg(bool): Whether to use mean in selected hint area.
     is_train(bool): Training process or not.
  
  Return:
      data(dict)：The preprocessed data for colorization.
code: def __init__(
      self,
      ab_thresh: float = 0.0,
      p: float = 0.0,
      points: int = None,
      samp: str = "normal",
      use_avg: bool = True,
  ):
      self.ab_thresh = ab_thresh
      self.p = p
      self.num_points = points
      self.samp = samp
      self.use_avg = use_avg
      self.gethint = ColorizeHint(
          percent=self.p,
          num_points=self.num_points,
          samp=self.samp,
          use_avg=self.use_avg,
      )
  
imports: import paddle
  import numpy
label_desc: Use image node_id 1 to create a new ColorizePreprocess object  with example: obj = ColorizePreprocess(ab_thresh, p, points, samp, use_avg)