code: def _load_mnist(n_models: int = 1):
      path = Path(__file__).parent / "mnist_pytorch.json"
      with open(path) as f:
          mnist_model = PytorchGraphModelSpace._load(**nni.load(fp=f))
      mnist_model.evaluator = create_evaluator()
      mnist_model.status = ModelStatus.Frozen
  
      if n_models == 1:
          return mnist_model
      else:
          models = [mnist_model]
          for _ in range(n_models - 1):
              forked_model = mnist_model.fork()
              forked_model.status = ModelStatus.Frozen
              models.append(forked_model)
          return models
  
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 a global function with example usage: _load_mnist(n_models) and returns: mnist_model, models