code: def load_base_model(self):
      path = Path(_download_artifact_from_uri(self.base_model_path))
      model_file_path = os.path.join(path, "data/model.pth")
      return torch.load(model_file_path)
  
imports: import argparse
  import copy
  import os
  import shutil
  import tempfile
  from pathlib import Path
  import lightning
  import torch
  from ax.service.ax_client import AxClient
  from mnist import LightningMNISTClassifier, MNISTDataModule
  from prettytable import PrettyTable
  from torch.nn.utils import prune
  import mlflow.pytorch
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
label_desc: Use image node_id 3 for calling the IterativePrune obj's underlying member method code with example usage: obj.load_base_model() and returns: torch