code: def _sampler_helper(sampler, train_inputs, train_targets):
      _train_inputs, _train_targets = sampler.fit_resample(
          train_inputs, train_targets
      )
      train_inputs = pd.DataFrame(
          _train_inputs, columns=train_inputs.columns
      )
      train_targets = pd.DataFrame(
          _train_targets, columns=train_targets.columns
      )
      return train_inputs, train_targets
  
imports: from hyperparameter_hunter import Environment, ET, FeatureEngineer, EngineerStep
  from hyperparameter_hunter import Real, Integer, Categorical
  import pandas
  from imblearn.combine import SMOTETomek, SMOTEENN
  from imblearn.over_sampling import RandomOverSampler, SMOTE
  from imblearn.over_sampling import ADASYN, BorderlineSMOTE, SVMSMOTE
  from imblearn.under_sampling import ClusterCentroids, RandomUnderSampler, TomekLinks, NearMiss, CondensedNearestNeighbour, OneSidedSelection, NeighbourhoodCleaningRule, EditedNearestNeighbours, RepeatedEditedNearestNeighbours, AllKNN, InstanceHardnessThreshold
  from sklearn.datasets import make_classification
  from xgboost import XGBClassifier
label_desc: Use image node_id 1 for calling a global function with example usage: _sampler_helper(sampler, train_inputs, train_targets) and returns: train_inputs,  train_targets