code: def forward(
      self, image: Tensor, target: Optional[Dict[str, Tensor]] = None
  ) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
      if torch.rand(1) < self.p:
          image = F.hflip(image)
          if target is not None:
              _, _, width = F.get_dimensions(image)
              target["boxes"][:, [0, 2]] = (
                  width - target["boxes"][:, [2, 0]]
              )
              if "masks" in target:
                  target["masks"] = target["masks"].flip(-1)
              if "keypoints" in target:
                  keypoints = target["keypoints"]
                  keypoints = _flip_coco_person_keypoints(
                      keypoints, width
                  )
                  target["keypoints"] = keypoints
      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 1 for calling the RandomHorizontalFlip obj's underlying member method code with example usage: obj.forward(image, target) and returns: image,  target