code: def neural_net_predict(params, inputs):
      """Params is a list of (weights, bias) tuples.
      inputs is an (N x D) matrix."""
      inpW, inpb = params[0]
      inputs = relu(np.dot(inputs, inpW) + inpb)
      for W, b in params[1:-1]:
          outputs = batch_normalize(np.dot(inputs, W) + b)
          inputs = relu(outputs)
      outW, outb = params[-1]
      outputs = np.dot(inputs, outW) + outb
      return outputs
  
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 6 for calling a global function with example usage: neural_net_predict(params, inputs) and returns: outputs