code: def _generate_splines(
      n_functions=10,
      n_initial_points=10,
      s=0.01,
      x_min=-3,
      x_max=3,
      y_min=0,
      y_max=1,
      random_seed=2019,
  ):
      """
      Generate a list of spline functions for feature
      transformation.
  
      Parameters
      ----------
      n_functions : int, optional
          Number of spline functions to be created.
      n_initial_points: int, optional
          Number of initial random points to be placed on a 2D plot to fit a spline.
      s:  float or None, optional
          Positive smoothing factor used to choose the number of knots (arg in scipy.interpolate.UnivariateSpline).
      x_min: int or float, optional
          The minimum value of the X range.
      x_max:  int or float, optional
          The maximum value of the X range.
      y_min: int or float, optional
          The minimum value of the Y range.
      y_max: int or float, optional
          The maxium value of the Y range.
      random_seed: int, optional
          Random seed.
  
      Returns
      -------
      spls: list
          List of spline functions.
      """
      np.random.seed(random_seed)
      spls = []
      for i in range(n_functions):
          x = np.linspace(x_min, x_max, n_initial_points)
          y = np.random.uniform(y_min, y_max, n_initial_points)
          spl = UnivariateSpline(x, y, s=s)
          spls.append(spl)
      return spls
  
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 7 for calling a global function with example usage: _generate_splines(n_functions, n_initial_points, s, x_min, x_max, y_min, y_max, random_seed) and returns: spls