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 os
  from argparse import ArgumentParser
  import matplotlib.pyplot
  import numpy
  import pandas
  import torch
  from captum.attr import IntegratedGradients, LayerConductance, NeuronConductance
  from prettytable import PrettyTable
  from scipy import stats
  from sklearn.model_selection import train_test_split
  from torch import nn
  import mlflow
label_desc: Use image node_id 3 for calling a global function with example usage: count_model_parameters(model) and returns: table,  total_params