code: def init_random_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/neural_net_regression.py
imports: from __future__ import absolute_import
  from __future__ import print_function
  import matplotlib.pyplot
  import autograd.numpy
  import autograd.numpy.random
  import autograd.scipy.stats.norm
  from autograd import grad
  from autograd.misc import flatten
  from autograd.misc.optimizers import adam
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