code: def get_titanic():
      """
      we now preprocess the data by converting some categorical features such as
      gender, location of embarcation, and passenger class into one-hot encodings
      We also remove some features that are more difficult to analyze
      After processing, the features we have are:
      Age: Passenger Age
      Sibsp: Number of Siblings / Spouses Aboard
      Parch: Number of Parents / Children Aboard
      Fare: Fare Amount Paid in British Pounds
      Female: Binary variable indicating whether passenger is female
      Male: Binary variable indicating whether passenger is male
      EmbarkC : Binary var indicating whether passenger embarked @ Cherbourg
      EmbarkQ : Binary var indicating whether passenger embarked @ Queenstown
      EmbarkS : Binary var indicating whether passenger embarked @ Southampton
      Class1 : Binary var indicating whether passenger was in first class
      Class2 : Binary var indicating whether passenger was in second class
      Class3 : Binary var indicating whether passenger was in third class
      url = "https://biostat.app.vumc.org/wiki/pub/Main/DataSets/titanic3.csv"
      """
      url = "https://biostat.app.vumc.org/wiki/pub/Main/DataSets/titanic3.csv"
      titanic_data = pd.read_csv(url)
      titanic_data = pd.concat(
          [
              titanic_data,
              pd.get_dummies(titanic_data["sex"], dtype=np.uint8),
              pd.get_dummies(
                  titanic_data["embarked"],
                  prefix="embark",
                  dtype=np.uint8,
              ),
              pd.get_dummies(
                  titanic_data["pclass"], prefix="class", dtype=np.uint8
              ),
          ],
          axis=1,
      )
  
      titanic_data["age"] = titanic_data["age"].fillna(
          titanic_data["age"].mean()
      )
      titanic_data["fare"] = titanic_data["fare"].fillna(
          titanic_data["fare"].mean()
      )
      titanic_data = titanic_data.drop(
          [
              "name",
              "ticket",
              "cabin",
              "boat",
              "body",
              "home.dest",
              "sex",
              "embarked",
              "pclass",
          ],
          axis=1,
      )
      return titanic_data
  
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 1 for calling a global function with example usage: get_titanic() and returns: titanic_data