code: def get_model(checkpoint_dir: str):
      model = resnet18()
      model.conv1 = nn.Conv2d(
          1, 64, kernel_size=7, stride=1, padding=3, bias=False
      )
  
      model_state_dict = torch.load(
          os.path.join(checkpoint_dir, "model.pt"), map_location="cpu"
      )
      model.load_state_dict(model_state_dict)
  
      return model
  
imports: import argparse
  import atexit
  import json
  import os
  import tempfile
  import time
  import subprocess
  import ray
  from ray.train import Checkpoint, ScalingConfig, RunConfig
  from ray.tune.tune_config import TuneConfig
  import requests
  import torch
  import torch.nn
  import torchvision.transforms
  from filelock import FileLock
  from ray import serve, tune, train
  from ray.train.torch import TorchTrainer
  from ray.tune import Tuner
  from torch.utils.data import DataLoader, Subset
  from torchvision.datasets import MNIST
  from torchvision.models import resnet18
label_desc: Use image node_id 6 for calling a global function with example usage: get_model(checkpoint_dir) and returns: model