code: def eval_step(
      self, state: State, data: Tuple[torch.Tensor, torch.Tensor]
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      inputs, targets = data
  
      outputs = self.module(inputs)
      loss = self.loss_fn(outputs, targets)
  
      assert state.eval_state
      if (
          self.eval_progress.num_steps_completed_in_epoch + 1
          == self.steps_before_stopping
      ):
          state.stop()
  
      self.steps_processed += 1
      return loss, outputs
  
imports: import unittest
  from typing import Any, Iterator, Mapping, Tuple
  from unittest.mock import MagicMock
  import torch
  from torch import nn
  from torchtnt.framework._test_utils import DummyEvalUnit, generate_random_dataloader
  from torchtnt.framework.callback import Callback
  from torchtnt.framework.evaluate import evaluate
  from torchtnt.framework.state import State
  from torchtnt.framework.unit import EvalUnit, TEvalUnit
  from torchtnt.utils.timer import Timer
label_desc: Use image node_id 2 for calling the StopEvalUnit obj's underlying member method code with example usage: obj.eval_step(state, data) and returns: loss,  outputs