code: def init_random_params(scale, layer_sizes, rs=npr.RandomState(0)):
      """Build a list of (weights, biases) tuples,
      one for each layer in the net."""
      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:])
      ]
  
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 1 for calling a global function with example usage: init_random_params(scale, layer_sizes, rs) and returns: unknown