code: def initiate_pruning_process(self, model):
      total_trials = int(vars(self.parser_args)["total_trials"])
  
      trial_index = None
      for i in range(total_trials):
          parameters, trial_index = self.ax_client.get_next_trial()
          print(
              "***************************************************************************"
          )
          print(f"Running Trial {i + 1}")
          print(
              "***************************************************************************"
          )
          with mlflow.start_run(
              nested=True, run_name="Iteration" + str(i)
          ):
              mlflow.set_tags({"AX_TRIAL": i})
  
              # calling the model
              test_accuracy = self.iterative_prune(model, parameters)
  
              # completion of trial
      self.ax_client.complete_trial(
          trial_index=trial_index, raw_data=test_accuracy.item()
      )
  
      # Ending the Base run
      mlflow.end_run()
  
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 9 for calling the IterativePrune obj's underlying member method code with example usage: obj.initiate_pruning_process(model) without return types