code: def forward(
      self, image: Tensor, target: Optional[Dict[str, Tensor]] = None
  ) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
      if target is None:
          raise ValueError(
              "The targets can't be None for this transform."
          )
  
      if isinstance(image, torch.Tensor):
          if image.ndimension() not in {2, 3}:
              raise ValueError(
                  f"image should be 2/3 dimensional. Got {image.ndimension()} dimensions."
              )
          elif image.ndimension() == 2:
              image = image.unsqueeze(0)
  
      _, orig_h, orig_w = F.get_dimensions(image)
  
      while True:
          # sample an option
          idx = int(
              torch.randint(low=0, high=len(self.options), size=(1,))
          )
          min_jaccard_overlap = self.options[idx]
          if (
              min_jaccard_overlap >= 1.0
          ):  # a value larger than 1 encodes the leave as-is option
              return image, target
  
          for _ in range(self.trials):
              # check the aspect ratio limitations
              r = self.min_scale + (
                  self.max_scale - self.min_scale
              ) * torch.rand(2)
              new_w = int(orig_w * r[0])
              new_h = int(orig_h * r[1])
              aspect_ratio = new_w / new_h
              if not (
                  self.min_aspect_ratio
                  <= aspect_ratio
                  <= self.max_aspect_ratio
              ):
                  continue
  
              # check for 0 area crops
              r = torch.rand(2)
              left = int((orig_w - new_w) * r[0])
              top = int((orig_h - new_h) * r[1])
              right = left + new_w
              bottom = top + new_h
              if left == right or top == bottom:
                  continue
  
              # check for any valid boxes with centers within the crop area
              cx = 0.5 * (target["boxes"][:, 0] + target["boxes"][:, 2])
              cy = 0.5 * (target["boxes"][:, 1] + target["boxes"][:, 3])
              is_within_crop_area = (
                  (left < cx)
                  & (cx < right)
                  & (top < cy)
                  & (cy < bottom)
              )
              if not is_within_crop_area.any():
                  continue
  
              # check at least 1 box with jaccard limitations
              boxes = target["boxes"][is_within_crop_area]
              ious = torchvision.ops.boxes.box_iou(
                  boxes,
                  torch.tensor(
                      [[left, top, right, bottom]],
                      dtype=boxes.dtype,
                      device=boxes.device,
                  ),
              )
              if ious.max() < min_jaccard_overlap:
                  continue
  
              # keep only valid boxes and perform cropping
              target["boxes"] = boxes
              target["labels"] = target["labels"][is_within_crop_area]
              target["boxes"][:, 0::2] -= left
              target["boxes"][:, 1::2] -= top
              target["boxes"][:, 0::2].clamp_(min=0, max=new_w)
              target["boxes"][:, 1::2].clamp_(min=0, max=new_h)
              image = F.crop(image, top, left, new_h, new_w)
  
              return image, target
  
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 2 for calling the RandomIoUCrop obj's underlying member method code with example usage: obj.forward(image, target) and returns: image,  target, image,  target