code: 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
  
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 DummyTrainUnit, generate_random_dataloader
  from torchtnt.framework.callback import Callback
  from torchtnt.framework.state import State
  from torchtnt.framework.train import train
  from torchtnt.framework.unit import TrainUnit, TTrainUnit
  from torchtnt.utils.timer import Timer
label_desc: Use image node_id 1 to create a new StopTrainUnit object from inherited base classes: TrainUnit with example: obj = StopTrainUnit(input_dim, steps_before_stopping)