code: def test_fit_stop(self) -> None:
      Batch = Tuple[torch.Tensor, torch.Tensor]
  
      class FitStop(TrainUnit[Batch], EvalUnit[Batch]):
          def __init__(
              self, input_dim: int, steps_before_stopping: int
          ) -> None:
              super().__init__()
              # initialize module, loss_fn, & optimizer
              self.module = nn.Linear(input_dim, 2)
              self.loss_fn = nn.CrossEntropyLoss()
              self.optimizer = torch.optim.SGD(
                  self.module.parameters(), lr=0.01
              )
              self.steps_processed = 0
              self.steps_before_stopping = steps_before_stopping
  
          def train_step(
              self, state: State, data: Batch
          ) -> Tuple[torch.Tensor, torch.Tensor]:
              inputs, targets = data
  
              outputs = self.module(inputs)
              loss = self.loss_fn(outputs, targets)
              loss.backward()
  
              self.optimizer.step()
              self.optimizer.zero_grad()
  
              assert state.train_state
              if (
                  my_unit.train_progress.num_steps_completed_in_epoch
                  + 1
                  == self.steps_before_stopping
              ):
                  state.stop()
  
              self.steps_processed += 1
              return loss, outputs
  
          def eval_step(
              self, state: State, data: Batch
          ) -> Tuple[torch.Tensor, torch.Tensor]:
              inputs, targets = data
              outputs = self.module(inputs)
              loss = self.loss_fn(outputs, targets)
              self.steps_processed += 1
              return loss, outputs
  
      input_dim = 2
      dataset_len = 10
      batch_size = 2
      max_epochs = 3
      max_steps_per_epoch = 4
      steps_before_stopping = 2
  
      my_unit = FitStop(
          input_dim=input_dim,
          steps_before_stopping=steps_before_stopping,
      )
      train_dl = generate_random_dataloader(
          dataset_len, input_dim, batch_size
      )
      eval_dl = generate_random_dataloader(
          dataset_len, input_dim, batch_size
      )
  
      fit(
          my_unit,
          train_dataloader=train_dl,
          eval_dataloader=eval_dl,
          max_epochs=max_epochs,
          max_train_steps_per_epoch=max_steps_per_epoch,
      )
  
      self.assertEqual(my_unit.train_progress.num_epochs_completed, 1)
      self.assertEqual(
          my_unit.train_progress.num_steps_completed_in_epoch, 0
      )
      self.assertEqual(
          my_unit.steps_processed,
          my_unit.train_progress.num_steps_completed,
      )
      self.assertEqual(my_unit.steps_processed, steps_before_stopping)
      self.assertEqual(my_unit.eval_progress.num_epochs_completed, 1)
      self.assertEqual(my_unit.eval_progress.num_steps_completed, 0)
      self.assertEqual(
          my_unit.eval_progress.num_steps_completed_in_epoch, 0
      )
  
imports: import math
  import unittest
  from typing import Tuple
  from unittest.mock import MagicMock
  import torch
  from torch import nn
  from torchtnt.framework._test_utils import DummyFitUnit, generate_random_dataloader
  from torchtnt.framework.callback import Callback
  from torchtnt.framework.fit import fit
  from torchtnt.framework.state import ActivePhase, State
  from torchtnt.framework.unit import EvalUnit, TrainUnit, TTrainUnit
  from torchtnt.utils.timer import Timer
label_desc: Use image node_id 3 for calling the FitTest obj's underlying member method code with example usage: obj.test_fit_stop() and returns: loss,  outputs, loss,  outputs