code: def check_rnn_layer_w_rand_inputs(layer):
      layer.initialize(device=[mx.cpu(0), mx.gpu(0)])
      x = mx.np.random.uniform(size=(10, 16, 30))
      with mx.gpu(0):
          x = x.copyto(mx.gpu(0))
          states = layer.begin_state(16)
          go, gs = layer(x, states)
  
      with mx.cpu(0):
          x = x.copyto(mx.cpu(0))
          states = layer.begin_state(16)
          co, cs = layer(x, states)
  
      assert_almost_equal(go, co)
      for g, c in zip(gs, cs):
          assert_almost_equal(g, c)
  
imports: import sys
  import os
  import time
  import random
  import mxnet
  import multiprocessing
  from mxnet.test_utils import check_consistency, set_default_device, assert_almost_equal, rand_ndarray, environment
  import numpy
  import math
  from mxnet import autograd
  import pytest
  from common import assert_raises_cudnn_not_satisfied, run_in_spawned_process, random_seed
  from test_gluon import *
  from test_loss import *
  from test_numpy_loss import *
  from test_gluon_rnn import *
label_desc: Use image node_id 2 for calling a global function with example usage: check_rnn_layer_w_rand_inputs(layer) without return types