code: def my_quantile_transform(train_targets, non_train_targets):
      transformer = QuantileTransformer(output_distribution="uniform")
      train_targets[train_targets.columns] = transformer.fit_transform(
          train_targets.values
      )
      non_train_targets[train_targets.columns] = transformer.transform(
          non_train_targets.values
      )
      return train_targets, non_train_targets
  
file_path: hyperparameter_hunter/tests/integration_tests/feature_engineering/test_both_stages_transform.py
imports: from hyperparameter_hunter import Environment, CVExperiment
  from hyperparameter_hunter.callbacks.recipes import dataset_recorder
  from hyperparameter_hunter.feature_engineering import FeatureEngineer
  from hyperparameter_hunter.utils.boltons_utils import chunked
  from numpy.testing import assert_array_almost_equal
  import pandas
  import pytest
  from sklearn.linear_model import Ridge
  from sklearn.preprocessing import QuantileTransformer
label_desc: Use image node_id 2 for calling a global function with example usage: my_quantile_transform(train_targets, non_train_targets) and returns: train_targets,  non_train_targets