code: def init_nn_params(scale, layer_sizes, rs=npr.RandomState(0)):
      """Build a list of (weights, biases) tuples, one for each layer."""
      return [
          (
              rs.randn(insize, outsize) * scale,  # weight matrix
              rs.randn(outsize) * scale,
          )  # bias vector
          for insize, outsize in zip(layer_sizes[:-1], layer_sizes[1:])
      ]
  
file_path: autograd/examples/ode_net.py
imports: from __future__ import absolute_import
  from __future__ import print_function
  import matplotlib.pyplot
  import numpy
  import autograd.numpy
  from autograd import grad
  from autograd.scipy.integrate import odeint
  from autograd.builtins import tuple
  from autograd.misc.optimizers import adam
  import autograd.numpy.random
label_desc: Use image node_id 3 for calling a global function with example usage: init_nn_params(scale, layer_sizes, rs) and returns: unknown