code: def __init__(self):
      super().__init__()
      self.conv1 = torch.nn.Conv2d(
          out_channels=32, in_channels=1, kernel_size=5
      )
      self.pool1 = torch.nn.MaxPool2d(kernel_size=2)
      self.conv2 = torch.nn.Conv2d(
          out_channels=64, in_channels=32, kernel_size=5
      )
      self.pool2 = torch.nn.MaxPool2d(kernel_size=2)
  
imports: from pathlib import Path
  import pytest
  import torch
  import torch.nn
  import torchmetrics
  from torchvision.datasets import MNIST
  from torchvision import transforms
  from pytorch_lightning import seed_everything
  import nni
  from nni.experiment.config import RemoteConfig, RemoteMachineConfig
  from nni.nas.evaluator.pytorch import Lightning, DataLoader
  from nni.nas.execution import SequentialExecutionEngine
  from nni.nas.execution.cgo import CrossGraphOptimization
  from nni.nas.execution.cgo.evaluator import MultiModelLightningModule, MultiModelTrainer
  from nni.nas.execution.cgo.logical_optimizer.logical_plan import LogicalPlan
  from nni.nas.execution.cgo.logical_optimizer.opt_dedup_input import DedupInputOptimizer
  from nni.nas.space import Node, ModelStatus
  from nni.nas.space.pytorch import PytorchGraphModelSpace
  from nni.runtime.trial_command_channel import get_default_trial_command_channel, set_default_trial_command_channel
  from ut.sdk.helper.trial_command_channel import TestHelperTrialCommandChannel
label_desc: Use image node_id 1 to create a new M_2_stem object from inherited base classes: nn with example: obj = M_2_stem()