code: def evaluate_batch(
      self,
      dataloader_iter: Optional[Iterator[pytorch.TorchData]],
      batch_idx: int,
  ) -> Dict[str, Any]:
      x, y = self.context.to_device(next(dataloader_iter))
  
      def take_step(model):
          preds = model(x)
          loss = self.loss(y, preds)
          return loss
  
      return {
          "loss1": take_step(self.model1),
          "loss2": take_step(self.model2),
      }
  
imports: from typing import Any, Dict, Iterator, Optional, Tuple, Union
  import attrdict
  import deepspeed
  import deepspeed.runtime.dataloader
  import numpy
  import torch
  import determined.pytorch.deepspeed
  import tests.experiment.fixtures.pytorch_counter_callback
  from determined import pytorch
label_desc: Use image node_id 3 for calling the LinearTwoEngineTrial obj's underlying member method code with example usage: obj.evaluate_batch(dataloader_iter, batch_idx) and returns: dict, loss