code: def forward(self, *_inputs):
      M_1__inputs_to_M_2_stem = _inputs[0]
      M_1_stem = self.M_1_stem(_inputs[0])
      M_2_stem = self.M_2_stem(M_1__inputs_to_M_2_stem)
      M_1_flatten = self.M_1_flatten(M_1_stem)
      M_2_flatten = self.M_2_flatten(M_2_stem)
      M_1_fc1 = self.M_1_fc1(M_1_flatten)
      M_2_fc1 = self.M_2_fc1(M_2_flatten)
      M_1_fc2 = self.M_1_fc2(M_1_fc1)
      M_2_fc2 = self.M_2_fc2(M_2_fc1)
      M_1_softmax = self.M_1_softmax(M_1_fc2)
      M_2_softmax = self.M_2_softmax(M_2_fc2)
      return M_1_softmax, M_2_softmax
  
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 _model_cpu obj's underlying member method code with example usage: obj.forward() and returns: M_1_softmax,  M_2_softmax