code: def get_Gaussian_Data(nfeat, numX, numY):
      """
      Args:
          nfeat (int): Number of features
          numX (int): Size of X
          numY (int): Size of Y
  
      Returns:
          Datasets X and Y
      """
      np.random.seed(0)
      X = np.random.normal(0.0, 1.0, (numX, nfeat))
      Y = np.random.normal(0.0, 1.0, (numY, nfeat))
  
      for i in range(numX):
          X[i, :] = X[i, :] / np.linalg.norm(X[i, :])
  
      for i in range(numY):
          Y[i, :] = Y[i, :] / np.linalg.norm(Y[i, :])
  
      return (X, Y)
  
file_path: explainx/lib/PDASH_utils.py
imports: from __future__ import print_function
  import numpy
  from sklearn.preprocessing import OneHotEncoder
  from cvxopt.solvers import qp
  from cvxopt import matrix, spmatrix
  from numpy import array, ndarray
  from scipy.spatial.distance import cdist
label_desc: Use image node_id 5 for calling a global function with example usage: get_Gaussian_Data(nfeat, numX, numY) and returns: X,  Y