code: def standard_scale_BAD(all_inputs):
      """If you wanted to standard-scale, by fitting on your entire dataset, rather than only on your
      train dataset (which is not recommended), this is how you could do it
      """
      scaler = StandardScaler()
      all_inputs[all_inputs.columns] = scaler.fit_transform(
          all_inputs.values
      )
      return all_inputs
  
file_path: hyperparameter_hunter/examples/feature_engineering_examples/target_transform_inverse_example.py
imports: from hyperparameter_hunter import Environment, CVExperiment, FeatureEngineer
  from hyperparameter_hunter import DummyOptPro, Categorical
  from hyperparameter_hunter.utils.learning_utils import get_boston_data
  import numpy
  from sklearn.linear_model import Ridge
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import QuantileTransformer, StandardScaler
label_desc: Use image node_id 5 for calling a global function with example usage: standard_scale_BAD(all_inputs) and returns: all_inputs