code: def run_mnist_model(self, base=False):
      if base:
          mlflow.start_run(run_name="BaseModel")
      mlflow.pytorch.autolog()
      dm = MNISTDataModule()
      dm.setup(stage="fit")
  
      model = LightningMNISTClassifier()
      trainer = L.Trainer(max_epochs=self.parser_args.max_epochs)
      trainer.fit(model, dm)
      trainer.test(datamodule=dm)
      if os.path.exists(self.base_model_path):
          shutil.rmtree(self.base_model_path)
      mlflow.pytorch.save_model(
          trainer.lightning_module, self.base_model_path
      )
      return trainer
  
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 2 for calling the IterativePrune obj's underlying member method code with example usage: obj.run_mnist_model(base) and returns: trainer