code: def create_data_files(
      num_classes, diff, train_filename, test_filename, regression
  ):
      print("Outputting %s and %s" % (train_filename, test_filename))
      mean = np.random.randn(num_classes)
      cov = np.eye(num_classes)
  
      for filename, N in [
          (train_filename, N_train),
          (test_filename, N_test),
      ]:
          X, Y = generate(N, mean, cov, diff)
  
          # output in CNTK Text format
          with open(filename, "w") as dataset:
              num_labels = int((1 + np.amax(Y)))
              for i in range(N):
                  dataset.write("|features ")
                  for d in range(Dim):
                      dataset.write("%f " % X[i, d])
                  if regression:
                      dataset.write("|labels %f\n" % Y[i])
                  else:
                      labels = ["0"] * num_labels
                      labels[int(Y[i])] = "1"
                      dataset.write("|labels %s\n" % " ".join(labels))
  
imports: import numpy
  from sklearn.utils import shuffle
label_desc: Use image node_id 2 for calling a global function with example usage: create_data_files(num_classes, diff, train_filename, test_filename, regression) without return types