code: def pos_upsample(train_inputs, train_targets):
      # Find and add positive examples
      sample_mask = pd.Series(train_targets["target"] == 1)
      train_inputs = pd.concat(
          [train_inputs, train_inputs.loc[sample_mask]], axis=0
      )
      train_targets = pd.concat(
          [train_targets, train_targets.loc[sample_mask]], 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
  
imports: from hyperparameter_hunter import __version__
  from hyperparameter_hunter import Environment, CVExperiment, FeatureEngineer, EngineerStep
  from hyperparameter_hunter import Categorical, Integer, BayesianOptPro, GBRT, RF, ET, DummyOptPro
  from hyperparameter_hunter.utils.learning_utils import get_breast_cancer_data, get_boston_data
  from hyperparameter_hunter.utils.optimization_utils import get_choice_dimensions
  from hyperparameter_hunter.utils.version_utils import HHVersion
  import numpy
  from os import makedirs
  import pandas
  import pytest
  from shutil import rmtree
  import sys
  from sklearn.ensemble import AdaBoostRegressor
  from sklearn.linear_model import Ridge
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import MinMaxScaler, Normalizer, QuantileTransformer, StandardScaler
label_desc: Use image node_id 8 for calling a global function with example usage: pos_upsample(train_inputs, train_targets) and returns: train_inputs,  train_targets