class_docstr: Randomly select a scale factor to resize the image according to given range.
  
  Args:
      min_scale_factor(float): The minimum scale factor for targeted scale.
      max_scale_factor(float): The maximum scale factor for targeted scale.
      scale_step_size(float): Scale interval.
class_docstr_tok: ["Randomly","select","a","scale","factor","to","resize","the","image","according","to","given","range",".","Args",":","min_scale_factor","(","float",")",":","The","minimum","scale","factor","for","targeted","scale",".","max_scale_factor","(","float",")",":","The","maximum","scale","factor","for","targeted","scale",".","scale_step_size","(","float",")",":","Scale","interval","."]
code: def __init__(
      self,
      min_scale_factor: float = 0.75,
      max_scale_factor: float = 1.25,
      scale_step_size: float = 0.25,
  ):
      if min_scale_factor > max_scale_factor:
          raise ValueError(
              "min_scale_factor must be less than max_scale_factor, "
              "but they are {} and {}.".format(
                  min_scale_factor, max_scale_factor
              )
          )
      self.min_scale_factor = min_scale_factor
      self.max_scale_factor = max_scale_factor
      self.scale_step_size = scale_step_size
  
imports: import random
  from typing import Callable, Union, List, Tuple
  import cv2
  import PIL
  import numpy
  import paddlehub.vision.functional
label_desc: Use image node_id 1 to create a new ResizeStepScaling object  with example: obj = ResizeStepScaling(min_scale_factor, max_scale_factor, scale_step_size)