code: def sqr_sum_feature(all_inputs):
      """De Gua's theorem (Pythagorean theorem in n-dimensional space)"""
      all_inputs["square_sum_pre"] = all_inputs.agg(
          lambda row: np.sqrt(np.sum([np.square(_) for _ in row])),
          axis="columns",
      )
      return all_inputs
  
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 7 for calling a global function with example usage: sqr_sum_feature(all_inputs) and returns: all_inputs