code: def forward(self, *_inputs):
      conv1 = self.conv1(_inputs[0])
      pool1 = self.pool1(conv1)
      conv2 = self.conv2(pool1)
      pool2 = self.pool2(conv2)
      return pool2
  
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 2 for calling the M_2_stem obj's underlying member method code with example usage: obj.forward() and returns: pool2