code: def neural_net_predict(params, inputs):
      """Params is a list of (weights, bias) tuples.
      inputs is an (N x D) matrix.
      Applies batch normalization to every layer but the last."""
      for W, b in params[:-1]:
          outputs = batch_normalize(
              np.dot(inputs, W) + b
          )  # linear transformation
          inputs = relu(outputs)  # nonlinear transformation
      outW, outb = params[-1]
      outputs = np.dot(inputs, outW) + outb
      return outputs
  
imports: from __future__ import absolute_import, division
  from __future__ import print_function
  import autograd.numpy
  import autograd.numpy.random
  import autograd.scipy.stats.norm
  from autograd.scipy.special import expit
  from autograd import grad
  from autograd.misc.optimizers import adam
  from data import load_mnist, save_images
label_desc: Use image node_id 8 for calling a global function with example usage: neural_net_predict(params, inputs) and returns: outputs