code: def write_prune_summary(summary, params):
      with tempfile.TemporaryDirectory() as tempdir:
          summary_file = os.path.join(
              tempdir, "pruned_model_summary.txt"
          )
          params = "Total Trainable Parameters :" + str(params)
          with open(summary_file, "w") as f:
              f.write(str(summary))
              f.write("\n")
              f.write(str(params))
  
          mlflow.log_artifact(local_path=summary_file)
  
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 7 for calling the IterativePrune obj's underlying member method code with example usage: obj.write_prune_summary(summary, params) without return types