code: def create_titanic_pipeline(X_train, y_train):
      """Create a sklearn pipeline for the titanic dataset.
  
      param X_train: The training data.
      type X_train: numpy.ndarray or pandas.DataFrame
      param y_train: The training labels.
      type y_train: numpy.ndarray or pandas.DataFrame
      return: A sklearn pipeline for the titanic dataset.
      rtype: sklearn.pipeline.Pipeline
      """
  
      def conv(X):
          """Convert a pandas series to a numpy array.
  
          param X: The data.
          type X: pandas.Series
          return: The data as a numpy array.
          rtype: numpy.ndarray
          """
          if isinstance(X, pd.Series):
              return X.values
          return X
  
      many_to_one_transformer = FunctionTransformer(
          lambda x: conv(x.sum(axis=1)).reshape(-1, 1)
      )
      many_to_many_transformer = FunctionTransformer(
          lambda x: np.hstack(
              (
                  conv(np.prod(x, axis=1)).reshape(-1, 1),
                  conv(np.prod(x, axis=1) ** 2).reshape(-1, 1),
              )
          )
      )
      transformations = ColumnTransformer(
          [
              (
                  "age_fare_1",
                  Pipeline(
                      steps=[
                          ("imputer", SimpleImputer(strategy="median")),
                          ("scaler", StandardScaler()),
                      ]
                  ),
                  ["age", "fare"],
              ),
              ("age_fare_2", many_to_one_transformer, ["age", "fare"]),
              ("age_fare_3", many_to_many_transformer, ["age", "fare"]),
              (
                  "embarked",
                  Pipeline(
                      steps=[
                          (
                              "imputer",
                              SimpleImputer(
                                  strategy="constant",
                                  fill_value="missing",
                              ),
                          ),
                          ("encoder", OneHotEncoder(sparse=False)),
                      ]
                  ),
                  ["embarked"],
              ),
              (
                  "sex_pclass",
                  OneHotEncoder(sparse=False),
                  ["sex", "pclass"],
              ),
          ]
      )
      clf = Pipeline(
          steps=[
              ("preprocessor", transformations),
              ("classifier", LogisticRegression(solver="lbfgs")),
          ]
      )
      clf.fit(X_train, y_train)
      return clf
  
file_path: responsible-ai-toolbox/rai_test_utils/rai_test_utils/models/sklearn/sklearn_model_utils.py
imports: import numpy
  import pandas
  from sklearn import svm
  from sklearn.compose import ColumnTransformer
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  from sklearn.impute import SimpleImputer
  from sklearn.linear_model import LogisticRegression
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import FunctionTransformer, OneHotEncoder, StandardScaler
label_desc: Use image node_id 6 for calling a global function with example usage: create_titanic_pipeline(X_train, y_train) and returns: clf, X, X