code: def get_holdout_data(train, target_column):
      train_data, holdout_data = train_test_split(train, random_state=1)
      return train_data, holdout_data
  
imports: from hyperparameter_hunter import __version__
  from hyperparameter_hunter import Environment, CVExperiment, FeatureEngineer, EngineerStep
  from hyperparameter_hunter import Categorical, Integer, BayesianOptPro, GBRT, RF, ET, DummyOptPro
  from hyperparameter_hunter.utils.learning_utils import get_breast_cancer_data, get_boston_data
  from hyperparameter_hunter.utils.optimization_utils import get_choice_dimensions
  from hyperparameter_hunter.utils.version_utils import HHVersion
  import numpy
  from os import makedirs
  import pandas
  import pytest
  from shutil import rmtree
  import sys
  from sklearn.ensemble import AdaBoostRegressor
  from sklearn.linear_model import Ridge
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import MinMaxScaler, Normalizer, QuantileTransformer, StandardScaler
label_desc: Use image node_id 16 for calling a global function with example usage: get_holdout_data(train, target_column) and returns: train_data,  holdout_data