code: def test_sgld():
      opt1 = mx.optimizer.SGLD
      opt2 = mx.optimizer.SGLD
      shapes = [(3, 4, 5), (10, 4), (7,)]
      ns_options = [1234, 42]
      cg_options = [{}, {"clip_gradient": 0.4}, {"clip_gradient": 0.5}]
      wd_options = [{}, {"wd": 0.03}, {"wd": 0.05}, {"wd": 0.07}]
      mp_options = [
          {"multi_precision": False},
          {"multi_precision": True},
      ]
      agg_options = [
          {"aggregate_num": 0},
          {"aggregate_num": 1},
          {"aggregate_num": 4},
          {"aggregate_num": np.inf},
      ]
  
      for seed in ns_options:
          for dtype in [np.float16, np.float32]:
              for params in itertools.product(
                  cg_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
                  atol = 1e-2 if dtype == np.float16 else 1e-3
                  rtol = 1e-4 if dtype == np.float16 else 1e-5
                  compare_optimizer_noise_seeded(
                      opt1(**kwarg),
                      opt2(**kwarg),
                      shapes,
                      dtype,
                      seed,
                      atol=atol,
                      rtol=rtol,
                  )
  
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 10 for calling a global function with example usage: test_sgld() without return types