code: def count_model_parameters(model):
      table = PrettyTable(["Modules", "Parameters"])
      total_params = 0
  
      for name, parameter in model.named_parameters():
          if not parameter.requires_grad:
              continue
  
          param = parameter.nonzero(as_tuple=False).size(0)
          table.add_row([name, param])
          total_params += param
      return table, total_params
  
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 6 for calling the IterativePrune obj's underlying member method code with example usage: obj.count_model_parameters(model) and returns: table,  total_params