code: def forward(self, img1, img2, flow, valid_flow_mask):
      if not all(
          isinstance(arg, torch.Tensor)
          for arg in (img1, img2, flow, valid_flow_mask)
          if arg is not None
      ):
          raise TypeError(
              "This method expects all input arguments to be of type torch.Tensor."
          )
      if not all(
          arg.dtype == torch.float32
          for arg in (img1, img2, flow)
          if arg is not None
      ):
          raise TypeError(
              "This method expects the tensors img1, img2 and flow of be of dtype torch.float32."
          )
  
      if img1.shape != img2.shape:
          raise ValueError("img1 and img2 should have the same shape.")
      h, w = img1.shape[-2:]
      if flow is not None and flow.shape != (2, h, w):
          raise ValueError(
              f"flow.shape should be (2, {h}, {w}) instead of {flow.shape}"
          )
      if valid_flow_mask is not None:
          if valid_flow_mask.shape != (h, w):
              raise ValueError(
                  f"valid_flow_mask.shape should be ({h}, {w}) instead of {valid_flow_mask.shape}"
              )
          if valid_flow_mask.dtype != torch.bool:
              raise TypeError(
                  "valid_flow_mask should be of dtype torch.bool instead of {valid_flow_mask.dtype}"
              )
  
      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 ValidateModelInput 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