code: def get_holdout_data(train, target_column):
      train_data, holdout_data = train_test_split(train, random_state=1)
      return train_data, holdout_data
  
file_path: hyperparameter_hunter/examples/feature_engineering_examples/target_transform_inverse_example.py
imports: from hyperparameter_hunter import Environment, CVExperiment, FeatureEngineer
  from hyperparameter_hunter import DummyOptPro, Categorical
  from hyperparameter_hunter.utils.learning_utils import get_boston_data
  import numpy
  from sklearn.linear_model import Ridge
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import QuantileTransformer, StandardScaler
label_desc: Use image node_id 1 for calling a global function with example usage: get_holdout_data(train, target_column) and returns: train_data,  holdout_data