code: def generate_synthetic_data(N):
      Y = np.random.randint(size=N, low=0, high=num_classes)  # labels
      X = (np.random.randn(N, input_dim) + 3) * (Y[:, None] + 1)  # data
      # Our model expects float32 features, and cross-entropy expects one-hot encoded labels.
      Y = scipy.sparse.csr_matrix(
          (np.ones(N, np.float32), (range(N), Y)),
          shape=(N, num_classes),
      )
      X = X.astype(np.float32)
      return X, Y
  
imports: from __future__ import print_function
  import cntk
  import numpy
  import scipy.sparse
label_desc: Use image node_id 1 for calling a global function with example usage: generate_synthetic_data(N) and returns: X,  Y