code: def get_holdout_set(train, target_column):
      """This is a sample callable to demonstrate how the Environment's `holdout_dataset` is evaluated. If you do provide a
      callable, it should expect two inputs: the train_dataset (pandas.DataFrame), and the target_column name (string). You should
      return two DataFrames: a modified train_dataset, and a holdout_dataset. What happens in between is up to you, perhaps split
      apart a portion of the rows, but the idea is to remove part of train_dataset, and turn it into holdout_dataset. For this
      example, we'll just copy train_dataset, which is a VERY BAD IDEA in practice. Don't actually do this
      """
      return train, train.copy()
  
imports: from hyperparameter_hunter import Environment, CVExperiment
  from hyperparameter_hunter.utils.learning_utils import get_toy_classification_data
  from sklearn.model_selection import StratifiedKFold
  from xgboost import XGBClassifier
label_desc: Use image node_id 1 for calling a global function with example usage: get_holdout_set(train, target_column) and returns: train,  train