code: def compute_batch_loss(self, image1, image2):
      flow, outputs = self.model.calculate_flow_with_image_pairs(
          image1, image2
      )
  
      frame_losses = []
      for output in outputs:
          frame_losses.append(
              self.loss_functions.distortion_fn(output[1], output[0])
          )
  
      loss = torch.stack(frame_losses).mean()
  
      image2_est = ncF.dense_image_warp(
          image1, flow.permute(0, 2, 3, 1).detach()
      )
  
      return (
          LossValues(
              distortion_loss=loss,
              flow_entropy_loss=None,
              resid_entropy_loss=None,
          ),
          OutputTensors(
              flow=flow.detach(),
              image1=image1.detach(),
              image2=image2.detach(),
              image2_est=image2_est.detach(),
          ),
      )
  
imports: from typing import Callable, NamedTuple, Optional
  import torch
  import torch.nn
  from torch import Tensor
  import neuralcompression.functional
label_desc: Use image node_id 4 for calling the DvcStage1 obj's underlying member method code with example usage: obj.compute_batch_loss(image1, image2) and returns: LossValues,  OutputTensors