code: def titanic_dataset():
      df = pd.read_csv("tutorials/assets/titanic3.csv")
      df = df[
          [
              "age",
              "embarked",
              "fare",
              "parch",
              "pclass",
              "sex",
              "sibsp",
              "survived",
          ]
      ]
      df["age"] = df["age"].fillna(df["age"].mean())
      df["fare"] = df["fare"].fillna(df["fare"].mean())
  
      df_enc = pd.get_dummies(
          df, columns=["embarked", "pclass", "sex"]
      ).sample(frac=1)
      X = df_enc.drop(["survived"], axis=1).values.astype(float)
      Y = df_enc["survived"].values.astype(int)
      _, test_features, _, test_labels = train_test_split(
          X, Y, test_size=0.3
      )
      return {"x_batch": test_features, "y_batch": test_labels}
  
imports: import pytest
  import pickle
  import torch
  import numpy
  from keras.datasets import cifar10
  import pandas
  from sklearn.model_selection import train_test_split
  import os
  from quantus.helpers.model.models import LeNet, LeNetTF, CifarCNNModel, ConvNet1D, ConvNet1DTF, TitanicSimpleTFModel, TitanicSimpleTorchModel
label_desc: Use image node_id 19 for calling a global function with example usage: titanic_dataset() and returns: dict