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