code: def generate_from_noise(gen_params, num_samples, noise_dim, rs):
      noise = rs.rand(num_samples, noise_dim)
      samples = neural_net_predict(gen_params, noise)
      return sigmoid(samples)
  
file_path: autograd/examples/generative_adversarial_net.py
imports: from __future__ import absolute_import, division
  from __future__ import print_function
  import autograd.numpy
  import autograd.numpy.random
  from autograd import grad
  from autograd.misc import flatten
  from data import load_mnist, save_images
label_desc: Use image node_id 7 for calling a global function with example usage: generate_from_noise(gen_params, num_samples, noise_dim, rs) and returns: sigmoid