class_docstr: Randomly resize the input. Output image size is different from the input image size.
  
  Args:
      scale_limit ((float, float) or float): scaling factor range. If scale_limit is a single float value, the
          range will be (-scale_limit, scale_limit). Note that the scale_limit will be biased by 1.
          If scale_limit is a tuple, like (low, high), sampling will be done from the range (1 + low, 1 + high).
          Default: (-0.1, 0.1).
      interpolation (OpenCV flag): flag that is used to specify the interpolation algorithm. Should be one of:
          cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4.
          Default: cv2.INTER_LINEAR.
      p (float): probability of applying the transform. Default: 0.5.
  
  Targets:
      image, mask, bboxes, keypoints
  
  Image types:
      uint8, float32
code: def get_params(self):
      return {
          "scale": random.uniform(
              self.scale_limit[0], self.scale_limit[1]
          )
      }
  
imports: import random
  from typing import Dict, Sequence, Tuple, Union
  import cv2
  import numpy
  from ...core.transforms_interface import BoxInternalType, DualTransform, KeypointInternalType, to_tuple
  from .None import functional
label_desc: Use image node_id 2 for calling the RandomScale obj's underlying member method code with example usage: obj.get_params() and returns: dict