code: def test_optimize_layout(np_shape_array, amp_init, model, ndim):
      m = model(ndim)
      m.initialize(ctx=mx.gpu())
      m.hybridize()
      x = mx.np.random.uniform(
          low=0,
          high=10,
          size=(32, 2, 17, 15, 12)[: ndim + 2],
          ctx=mx.gpu(),
      )
      m(x)
      param_init = {
          k: v.data().copy() for k, v in m.collect_params().items()
      }
      for v in m.collect_params().values():
          v.data().attach_grad()
      with mx.autograd.record():
          y = m(x)
      y.backward()
      with optimize_layout():
          m2 = model(ndim)
          m2.initialize(ctx=mx.gpu())
          m2.load_dict(param_init, device=mx.gpu())
          m2.hybridize()
          for v in m2.collect_params().values():
              v.data().attach_grad()
          with mx.autograd.record():
              y2 = m2(x)
          y2.backward()
      rtol = 1e-2
      atol = 1e-2
      assert_allclose(y2, y, rtol=rtol, atol=atol)
      for k, v in m.collect_params().items():
          if v.grad_req == "null":
              continue
          assert_allclose(
              m2.collect_params()[k].grad(),
              v.grad(),
              rtol=rtol,
              atol=atol,
          )
  
imports: from contextlib import contextmanager
  import ctypes
  import numpy
  import pytest
  import mxnet
  from mxnet import amp
  from mxnet.base import check_call, _LIB
  from mxnet.gluon import nn
  from mxnet.test_utils import assert_allclose
label_desc: Use image node_id 5 for calling a global function with example usage: test_optimize_layout(np_shape_array, amp_init, model, ndim) without return types