code: def create_rnn_params(
      input_size,
      state_size,
      output_size,
      param_scale=0.01,
      rs=npr.RandomState(0),
  ):
      return {
          "init hiddens": rs.randn(1, state_size) * param_scale,
          "change": rs.randn(input_size + state_size + 1, state_size)
          * param_scale,
          "predict": rs.randn(state_size + 1, output_size)
          * param_scale,
      }
  
file_path: autograd/examples/rnn.py
imports: from __future__ import absolute_import
  from __future__ import print_function
  from builtins import range
  import autograd.numpy
  import autograd.numpy.random
  from autograd import grad
  from autograd.scipy.special import logsumexp
  from os.path import dirname, join
  from autograd.misc.optimizers import adam
label_desc: Use image node_id 3 for calling a global function with example usage: create_rnn_params(input_size, state_size, output_size, param_scale, rs) and returns: dict