code: def forward(self, img1, img2, flow, valid_flow_mask):
      if torch.rand(1) > self.p:
          return img1, img2, flow, valid_flow_mask
  
      img1 = F.vflip(img1)
      img2 = F.vflip(img2)
      flow = F.vflip(flow) * torch.tensor([1, -1])[:, None, None]
      if valid_flow_mask is not None:
          valid_flow_mask = F.vflip(valid_flow_mask)
      return img1, img2, flow, valid_flow_mask
  
imports: import torch
  import torchvision.transforms
  import torchvision.transforms.functional
label_desc: Use image node_id 1 for calling the RandomVerticalFlip obj's underlying member method code with example usage: obj.forward(img1, img2, flow, valid_flow_mask) and returns: img1,  img2,  flow,  valid_flow_mask, img1,  img2,  flow,  valid_flow_mask