code: def evaluate_batch(
      self, iter_dataloader, batch_idx
  ) -> Dict[str, Any]:
      inputs, labels = self.context.to_device(next(iter_dataloader))
      if self.fp16:
          inputs = inputs.half()
      outputs = self.model_engine(inputs)
      loss = self.criterion(outputs, labels)
      return {"val_loss": loss.item()}
  
imports: from typing import Any, Dict
  import deepspeed
  import torch
  import torch.nn
  from attrdict import AttrDict
  from torch.utils.data import Dataset
  from torchvision import models
  from determined.pytorch import DataLoader, dsat
  from determined.pytorch.deepspeed import DeepSpeedTrial, DeepSpeedTrialContext
label_desc: Use image node_id 3 for calling the TorchvisionTrial obj's underlying member method code with example usage: obj.evaluate_batch(iter_dataloader, batch_idx) and returns: dict