code: def log_posterior(params, inputs, targets, L2_reg):
      log_prior = -L2_reg * l2_norm(params)
      log_lik = np.sum(neural_net_predict(params, inputs) * targets)
      return log_prior + log_lik
  
file_path: autograd/examples/neural_net.py
imports: from __future__ import absolute_import, division
  from __future__ import print_function
  import autograd.numpy
  import autograd.numpy.random
  from autograd.scipy.special import logsumexp
  from autograd import grad
  from autograd.misc.flatten import flatten
  from autograd.misc.optimizers import adam
  from data import load_mnist
label_desc: Use image node_id 4 for calling a global function with example usage: log_posterior(params, inputs, targets, L2_reg) and returns: unknown