code: def create_numpy_features(base_features, sentinel):
      r"""Calculate the sum, mean, standard deviation, and variance
      of each row.
  
      Parameters
      ----------
      base_features : numpy array
          The feature dataframe.
      sentinel : float
          The number to be imputed for NaN values.
  
      Returns
      -------
      np_features : numpy array
          The calculated NumPy features.
      np_fnames : list
          The NumPy feature names.
  
      """
  
      logger.info("Creating NumPy Features")
  
      # Calculate the total, mean, standard deviation, and variance.
  
      np_funcs = {
          "sum": np.sum,
          "mean": np.mean,
          "std": np.std,
          "var": np.var,
      }
  
      features = []
      for k in np_funcs:
          logger.info("NumPy Feature: %s", k)
          feature = np_funcs[k](base_features, axis=1)
          feature = impute_values(feature, "float64", sentinel)
          features.append(feature)
  
      # Stack and scale the new features.
  
      np_features = np.column_stack(features)
      np_features = StandardScaler().fit_transform(np_features)
  
      # Return new NumPy features
  
      logger.info("NumPy Feature Count : %d", np_features.shape[1])
      return np_features, np_funcs.keys()
  
imports: from alphapy.globals import BSEP, LOFF, NULLTEXT
  from alphapy.globals import PSEP, SSEP, USEP
  from alphapy.globals import Encoders
  from alphapy.globals import ModelType
  from alphapy.globals import Scalers
  from alphapy.variables import Variable
  from alphapy.variables import vparse
  import category_encoders
  from importlib import import_module
  import itertools
  import logging
  import math
  import numpy
  import os
  import pandas
  import re
  from scipy import sparse
  import scipy.stats
  from sklearn.cluster import MiniBatchKMeans
  from sklearn.decomposition import PCA
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.feature_selection import chi2
  from sklearn.feature_selection import f_classif
  from sklearn.feature_selection import f_regression
  from sklearn.feature_selection import SelectFdr
  from sklearn.feature_selection import SelectFpr
  from sklearn.feature_selection import SelectFwe
  from sklearn.feature_selection import SelectKBest
  from sklearn.feature_selection import SelectPercentile
  from sklearn.feature_selection import VarianceThreshold
  from sklearn.impute import SimpleImputer
  from sklearn.manifold import Isomap
  from sklearn.manifold import TSNE
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.preprocessing import PolynomialFeatures
  from sklearn.preprocessing import StandardScaler
  import sys
label_desc: Use image node_id 10 for calling a global function with example usage: create_numpy_features(base_features, sentinel) and returns: np_features,  np_funcs