code: def visualize_importances(
      feature_names,
      importances,
      title="Average Feature Importances",
      plot=True,
      axis_title="Features",
  ):
      print(title)
      feature_imp = PrettyTable(["feature_name", "importances"])
      feature_imp_dict = {}
      for i in range(len(feature_names)):
          print(feature_names[i], ": ", "%.3f" % (importances[i]))
          feature_imp.add_row([feature_names[i], importances[i]])
          feature_imp_dict[str(feature_names[i])] = importances[i]
      x_pos = np.arange(len(feature_names))
      if plot:
          fig, ax = plt.subplots(figsize=(12, 6))
          ax.bar(x_pos, importances, align="center")
          ax.set(title=title, xlabel=axis_title)
          ax.set_xticks(x_pos)
          ax.set_xticklabels(feature_names, rotation="vertical")
          mlflow.log_figure(fig, title + ".png")
      return feature_imp, feature_imp_dict
  
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 4 for calling a global function with example usage: visualize_importances(feature_names, importances, title, plot, axis_title) and returns: feature_imp,  feature_imp_dict