code: def forward(self, img1, img2, flow, valid_flow_mask):
      if torch.rand(1) < self.p:
          # asymmetric: different transform for img1 and img2
          img1 = super().forward(img1)
          img2 = super().forward(img2)
      else:
          # symmetric: same transform for img1 and img2
          batch = torch.stack([img1, img2])
          batch = super().forward(batch)
          img1, img2 = batch[0], batch[1]
  
      return img1, img2, flow, valid_flow_mask
  
imports: import torch
  import torchvision.transforms
  import torchvision.transforms.functional
label_desc: Use image node_id 2 for calling the AsymmetricColorJitter 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