class_docstr: Randomly resizes the image and its bounding boxes  within the specified scale range.
  The class implements the Scale Jitter augmentation as described in the paper
  `"Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation" <https://arxiv.org/abs/2012.07177>`_.
  
  Args:
      target_size (tuple of ints): The target size for the transform provided in (height, weight) format.
      scale_range (tuple of ints): scaling factor interval, e.g (a, b), then scale is randomly sampled from the
          range a <= scale <= b.
      interpolation (InterpolationMode): Desired interpolation enum defined by
          :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
code: def __init__(
      self,
      target_size: Tuple[int, int],
      scale_range: Tuple[float, float] = (0.1, 2.0),
      interpolation: InterpolationMode = InterpolationMode.BILINEAR,
      antialias=True,
  ):
      super().__init__()
      self.target_size = target_size
      self.scale_range = scale_range
      self.interpolation = interpolation
      self.antialias = antialias
  
imports: from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torchvision
  from torch import nn, Tensor
  from torchvision import ops
  from torchvision.transforms import functional, InterpolationMode, transforms
label_desc: Use image node_id 1 to create a new ScaleJitter object from inherited base classes: nn with example: obj = ScaleJitter(target_size, scale_range, interpolation, antialias)