code: def test_forward_rnn():
      """test RNN layer"""
      net = _lib().make_network(1)
      batch = 1
      inputs = 4
      outputs = 4
      steps = 1
      activation = ACTIVATION.RELU
      batch_normalize = 0
      adam = 0
      layer_1 = _lib().make_rnn_layer(
          batch,
          inputs,
          outputs,
          steps,
          activation,
          batch_normalize,
          adam,
      )
      net.layers[0] = layer_1
      net.inputs = inputs
      net.outputs = outputs
      net.w = net.h = 0
      _lib().resize_network(net, net.w, net.h)
      states = {"rnn0_state": np.zeros([1, net.inputs])}
      _test_rnn_network(net, states)
      _lib().free_network(net)
  
imports: from cffi import FFI
  import numpy
  import tvm
  from tvm.contrib import graph_executor
  from tvm.contrib.download import download_testdata
  from tvm.relay.testing.darknet import LAYERTYPE
  from tvm.relay.testing.darknet import __darknetffi__
  from tvm.relay.frontend.darknet import ACTIVATION
  from tvm import relay
label_desc: Use image node_id 31 for calling a global function with example usage: test_forward_rnn() without return types