code: def test_group_adagrad():
      mx.random.seed(0)
      opt1 = mx.optimizer.contrib.GroupAdaGrad
      opt2 = mx.optimizer.contrib.GroupAdaGrad
      shapes = [(3, 4), [5, 6]]
      eps_options = [{}, {"epsilon": 1e-8}]
      cg_options = [{}, {"clip_gradient": 0.4}, {"clip_gradient": 0.5}]
      rg_options = [{}, {"rescale_grad": 0.14}, {"rescale_grad": 0.8}]
      agg_options = [
          {},
          {"aggregate_num": 0},
          {"aggregate_num": 1},
          {"aggregate_num": 4},
          {"aggregate_num": np.inf},
      ]
      for dtype in [np.float32]:
          for options in itertools.product(
              eps_options, cg_options, rg_options, agg_options
          ):
              kwarg = dict(wd=0.0)
              for option in options:
                  kwarg.update(option)
              compare_optimizer(
                  opt1(use_fused_step=False, **kwarg),
                  opt2(use_fused_step=True, **kwarg),
                  shapes,
                  dtype,
              )
              compare_optimizer(
                  opt1(use_fused_step=False, **kwarg),
                  opt2(use_fused_step=True, **kwarg),
                  shapes,
                  dtype,
                  w_stype="row_sparse",
                  g_stype="row_sparse",
              )
              compare_optimizer(
                  opt1(use_fused_step=False, **kwarg),
                  opt2(use_fused_step=True, **kwarg),
                  shapes,
                  dtype,
                  g_stype="row_sparse",
              )
  
file_path: mxnet/tests/python/unittest/test_contrib_optimizer.py
imports: import itertools
  import numpy
  import mxnet
  from mxnet.test_utils import *
  import pytest
  from common import xfail_when_nonstandard_decimal_separator
label_desc: Use image node_id 1 for calling a global function with example usage: test_group_adagrad() without return types