class_docstr: Cropping for SSD, according to original paper
  Choose between following 3 conditions:
  1. Preserve the original image
  2. Random crop minimum IoU is among 0.1, 0.3, 0.5, 0.7, 0.9
  3. Random crop
  Reference to https://github.com/chauhan-utk/src.DomainAdaptation
code: def __init__(self):
      self.sample_options = (
          # Do nothing
          None,
          # min IoU, max IoU
          (0.1, None),
          (0.3, None),
          (0.5, None),
          (0.7, None),
          (0.9, None),
          # no IoU requirements
          (None, None),
      )
  
imports: import torch
  import torchvision.transforms
  import torch.utils.data
  from PIL import Image
  import os
  import numpy
  import random
  import itertools
  import torch.nn.functional
  import json
  import time
  import bz2
  import pickle
  from math import sqrt
label_desc: Use image node_id 1 to create a new SSDCropping object from inherited base classes: object with example: obj = SSDCropping()