code: def check_rnn_consistency(
      fused_layer,
      stack_layer,
      loss,
      mode,
      num_layers,
      input_size,
      hidden_size,
      bidirectional=False,
      rtol=1e-2,
      atol=1e-4,
  ):
      x = mx.np.random.normal(size=(1, 5, input_size))
      fused_begin_state = fused_layer.begin_state(1)
      stack_states = stack_layer.begin_state(batch_size=1)
      fused_layer.infer_shape(x, fused_begin_state)
      fused_layer_params = fused_layer.collect_params()
      stack_layer.infer_shape(0, x, False)
      stack_layer.initialize()
      stack_layer_params = stack_layer.collect_params()
  
      fused_weight_shape = fused_layer_params["rnn_param"].shape
      w = mx.np.zeros(shape=fused_weight_shape)
      fused_layer_params_split = split_rnn_params(
          w, mode, num_layers, input_size, hidden_size, bidirectional
      )
      for name, value in fused_layer_params_split.items():
          if "bias" in name:
              fused_layer_params_split[name] = mx.np.random.normal(
                  size=value.shape
              )
      _dir = 2 if bidirectional else 1
      params = (
          fused_layer_params_split[
              "{}{}_{}_{}".format(d, l, g, t)
          ].reshape(-1)
          for t in ["weight", "bias"]
          for l in range(num_layers)
          for d in ["l", "r"][:_dir]
          for g in ["i2h", "h2h"]
      )
      fused_params = mx.np.concatenate(params)
      fused_layer_params["rnn_param"].set_data(fused_params)
      for name, value in fused_layer_params_split.items():
          cur = name.split("_")[0]
          num = cur[1:]
          stack_name = (
              "{}.{}_cell.".format(num, name[0])
              if bidirectional
              else num + "."
          ) + name[len(cur) + 1 :]
          stack_layer_params[stack_name].set_data(value)
  
      fx = x.copy()
      sx = x.copy()
      y = mx.np.random.uniform(
          size=(1, 5, hidden_size * 2 if bidirectional else hidden_size)
      )
      fx.attach_grad()
      with mx.autograd.record():
          fused_out, fused_states = fused_layer(fx, fused_begin_state)
          l = loss(fused_out, y).mean()
      l.backward()
      mx.npx.waitall()
      fused_grads = split_rnn_params(
          fused_layer.collect_params()["rnn_param"].data().grad,
          mode,
          num_layers,
          input_size,
          hidden_size,
          bidirectional,
      )
      fused_input_grad = fx.grad.asnumpy()
  
      sx.attach_grad()
      with mx.autograd.record():
          stack_out, stack_states = stack_layer.unroll(
              5, sx, begin_state=stack_states, merge_outputs=True
          )
          l = loss(stack_out, y).mean()
      l.backward()
      mx.npx.waitall()
      stack_grads = dict(
          [
              (name, p.grad())
              for name, p in stack_layer.collect_params().items()
          ]
      )
      stack_input_grad = sx.grad.asnumpy()
  
      assert_allclose(
          fused_out.asnumpy(), stack_out.asnumpy(), rtol=rtol, atol=atol
      )
      assert_allclose(
          fused_input_grad, stack_input_grad, rtol=rtol, atol=atol
      )
      for name, value in fused_grads.items():
          cur = name.split("_")[0]
          num = cur[1:]
          stack_name = (
              "{}.{}_cell.".format(num, name[0])
              if bidirectional
              else num + "."
          ) + name[len(cur) + 1 :]
          assert_allclose(
              value.asnumpy(),
              stack_grads[stack_name].asnumpy(),
              rtol=rtol,
              atol=atol,
          )
  
      num_layers = fused_begin_state[0].shape[0] // (
          2 if bidirectional else 1
      )
      check_rnn_states(
          fused_states,
          stack_states,
          num_layers,
          bidirectional,
          len(fused_begin_state) == 2,
      )
  
imports: import mxnet
  from mxnet import gluon, np
  import numpy
  import copy
  from functools import partial
  from numpy.testing import assert_allclose
  from mxnet.gluon.utils import split_rnn_params
  import pytest
  from mxnet.test_utils import almost_equal, assert_almost_equal, default_device
  from common import assert_raises_cudnn_not_satisfied, retry
label_desc: Use image node_id 23 for calling a global function with example usage: check_rnn_consistency(fused_layer, stack_layer, loss, mode, num_layers, input_size, hidden_size, bidirectional, rtol, atol) without return types