code: def test_optimize_for():
      class TestBlock(gluon.HybridBlock):
          def __init__(self):
              super(TestBlock, self).__init__()
              self.d = mx.gluon.nn.Dense(1)
  
          def forward(self, a):
              res = self.d(a)
              return res
  
      a = mx.np.random.uniform(low=-1, high=1, size=(1, 1))
  
      net = TestBlock()
      net.initialize()
      net.hybridize()
  
      out = net(a)
      b = net.collect_params().pop("d.weight").data()
      net.optimize_for(a, b, backend="ONEDNN")
      out2 = net(a)
  
imports: from __future__ import absolute_import
  from __future__ import division
  import os
  from uuid import uuid4
  import numpy
  import mxnet
  from mxnet import gluon, autograd, np, npx
  from mxnet.test_utils import use_np, assert_almost_equal, check_gluon_hybridize_consistency, assert_allclose
  from mxnet.gluon import nn
  from mxnet.base import MXNetError
  import random
  import pytest
label_desc: Use image node_id 12 for calling a global function with example usage: test_optimize_for() and returns: res