code: def __init__(
      self,
      min_scale: float = 0.3,
      max_scale: float = 1.0,
      min_aspect_ratio: float = 0.5,
      max_aspect_ratio: float = 2.0,
      sampler_options: Optional[List[float]] = None,
      trials: int = 40,
  ):
      super().__init__()
      # Configuration similar to https://github.com/weiliu89/caffe/blob/ssd/examples/ssd/ssd_coco.py#L89-L174
      self.min_scale = min_scale
      self.max_scale = max_scale
      self.min_aspect_ratio = min_aspect_ratio
      self.max_aspect_ratio = max_aspect_ratio
      if sampler_options is None:
          sampler_options = [0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0]
      self.options = sampler_options
      self.trials = trials
  
imports: from typing import List, Tuple, Dict, Optional
  import torch
  import torchvision
  from torch import nn, Tensor
  from torchvision.transforms import functional
  from torchvision.transforms import transforms
label_desc: Use image node_id 1 to create a new RandomIoUCrop object from inherited base classes: nn with example: obj = RandomIoUCrop(min_scale, max_scale, min_aspect_ratio, max_aspect_ratio, sampler_options, trials)