code: def _fixed_transformation(fs, x, f_index=0):
      """
      Transform and standardize a vector by a transformation function.
      If the given index is within the function list f_index < len(fs), then use fs[f_index] as the transformation function
      otherwise, randomly choose a function from the function list.
      Parameters
      ----------
      fs : list
          A collection of functions for transformation.
      x : list
          Feature values to be transformed.
      f_index : int, optional
          The function index to be used to select a transformation function.
      """
      try:
          y = fs[f_index](x)
      except IndexError:
          y = fs[np.asscalar(np.random.choice(len(fs), 1))](x)
      y = _standardize(y)
      return y
  
imports: import random
  import numpy
  import pandas
  from sklearn.datasets import make_classification
  from scipy.optimize import fsolve
  from scipy.special import expit
  from scipy.special import logit
label_desc: Use image node_id 9 for calling a global function with example usage: _fixed_transformation(fs, x, f_index) and returns: y