code: def upsample_train_data(train_inputs, train_targets):
      # Get positive examples
      # NOTE: Differs from original kernel with addition of `["target"]` - Inputs always DataFrames
      pos = pd.Series(train_targets["target"] == 1)
      # Add positive examples
      train_inputs = pd.concat(
          [train_inputs, train_inputs.loc[pos]], axis=0
      )
      train_targets = pd.concat(
          [train_targets, train_targets.loc[pos]], axis=0
      )
      # Shuffle data
      idx = np.arange(len(train_inputs))
      np.random.shuffle(idx)
      train_inputs = train_inputs.iloc[idx]
      train_targets = train_targets.iloc[idx]
      return train_inputs, train_targets
  
file_path: hyperparameter_hunter/examples/feature_engineering_examples/olivier_porto_seguro_kaggle.py
imports: from hyperparameter_hunter import Environment, CVExperiment, FeatureEngineer, EngineerStep
  import numpy
  import pandas
  from sklearn.model_selection import StratifiedKFold
  from sklearn.preprocessing import LabelEncoder
  from xgboost import XGBClassifier
label_desc: Use image node_id 7 for calling a global function with example usage: upsample_train_data(train_inputs, train_targets) and returns: train_inputs,  train_targets