code: def standard_scale_1(train_inputs, validation_inputs):
      scaler = StandardScaler()
      train_inputs[train_inputs.columns] = scaler.fit_transform(
          train_inputs.values
      )
      validation_inputs[train_inputs.columns] = scaler.transform(
          validation_inputs.values
      )
      return train_inputs, validation_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 6 for calling a global function with example usage: standard_scale_1(train_inputs, validation_inputs) and returns: train_inputs,  validation_inputs