code: def test_nag():
      opt1 = mx.optimizer.NAG
      opt2 = mx.optimizer.NAG
      shapes = [(3, 4, 5), (10, 4), (7,)]
      mom_options = [{}, {"momentum": 0.9}]
      cg_options = [{}, {"clip_gradient": 0.4}, {"clip_gradient": 0.5}]
      rg_options = [{}, {"rescale_grad": 0.14}, {"rescale_grad": 0.8}]
      wd_options = [{}, {"wd": 0.03}, {"wd": 0.05}]
      mp_options = [
          {"multi_precision": False},
          {"multi_precision": True},
      ]
      agg_options = [
          {"aggregate_num": 0},
          {"aggregate_num": 1},
          {"aggregate_num": 4},
          {"aggregate_num": np.inf},
      ]
  
      for dtype in [np.float16, np.float32]:
          for params in itertools.product(
              mom_options,
              cg_options,
              rg_options,
              wd_options,
              mp_options,
              agg_options,
          ):
              kwarg = {
                  k: v for param in params for k, v in param.items()
              }
              if dtype == np.float16 and (
                  "multi_precision" not in kwarg
                  or not kwarg["multi_precision"]
              ):
                  continue
              compare_optimizer(
                  opt1(use_fused_step=False, **kwarg),
                  opt2(use_fused_step=True, **kwarg),
                  shapes,
                  dtype,
                  rtol=1e-3,
                  atol=1e-4,
              )
  
imports: import itertools
  import numpy
  import itertools
  import mxnet
  import mxnet.lr_scheduler
  from mxnet import gluon
  import unittest
  import pytest
  import math
  from mxnet.test_utils import *
  from common import retry, xfail_when_nonstandard_decimal_separator
label_desc: Use image node_id 6 for calling a global function with example usage: test_nag() without return types