code: def square_sum_feature(all_inputs):
      all_inputs["square_sum"] = all_inputs.agg(
          lambda row: np.sqrt(np.sum([np.square(_) for _ in row])),
          axis="columns",
      )
      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 6 for calling a global function with example usage: square_sum_feature(all_inputs) and returns: all_inputs