class_docstr: 根据原文，对图像进行裁剪,该方法应放在ToTensor前
  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
class_docstr_tok: ["\u6839\u636e\u539f\u6587\uff0c\u5bf9\u56fe\u50cf\u8fdb\u884c\u88c1\u526a",",","\u8be5\u65b9\u6cd5\u5e94\u653e\u5728ToTensor\u524d","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),
      )
      self.dboxes = dboxes300_coco()
  
imports: import random
  import torch
  import torchvision.transforms
  from torchvision.transforms import functional
  from src import dboxes300_coco, calc_iou_tensor, Encoder
label_desc: Use image node_id 1 to create a new SSDCropping object from inherited base classes: object with example: obj = SSDCropping()