code: def standard_scale_0(train_inputs, holdout_inputs):
      scaler = StandardScaler()
      train_inputs[train_inputs.columns] = scaler.fit_transform(
          train_inputs.values
      )
      holdout_inputs[train_inputs.columns] = scaler.transform(
          holdout_inputs.values
      )
      return train_inputs, holdout_inputs
  
imports: from hyperparameter_hunter import Environment, CVExperiment, FeatureEngineer, EngineerStep
  from hyperparameter_hunter.callbacks.recipes import dataset_recorder
  import numpy
  from numpy.testing import assert_array_almost_equal
  import pandas
  from pandas.testing import assert_frame_equal
  import pytest
  from sklearn.ensemble import AdaBoostClassifier
  from sklearn.preprocessing import StandardScaler
label_desc: Use image node_id 5 for calling a global function with example usage: standard_scale_0(train_inputs, holdout_inputs) and returns: train_inputs,  holdout_inputs