code: def init_net_params(scale, layer_sizes, rs=npr.RandomState(0)):
      """Build a (weights, biases) tuples for all layers."""
      return [
          (
              scale * rs.randn(m, n),  # weight matrix
              scale * rs.randn(n),
          )  # bias vector
          for m, n in zip(layer_sizes[:-1], layer_sizes[1:])
      ]
  
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 6 for calling a global function with example usage: init_net_params(scale, layer_sizes, rs) and returns: unknown