code: def evaluate_batch(self, batch: pytorch.TorchData) -> Dict[str, Any]:
      val = batch[0]
      np_rand = np.random.randint(1, 1000)
      rand_rand = random.randint(0, 1000)
      torch_rand = torch.randint(1000, (1,))
      gpu_rand = torch.randint(1000, (1,), device=self.context.device)
  
      print(
          f"finished evaluate_batch for rank {self.context.distributed.get_rank()}"
      )
  
      return {
          "validation_error": val,
          "np_rand": np_rand,
          "rand_rand": rand_rand,
          "torch_rand": torch_rand,
          "gpu_rand": gpu_rand,
      }
  
imports: import random
  from typing import Any, Dict, Tuple
  import numpy
  import torch
  from torch import nn
  from determined import pytorch
label_desc: Use image node_id 3 for calling the NoopPyTorchTrial obj's underlying member method code with example usage: obj.evaluate_batch(batch) and returns: dict