code: def test_dnnl_sparse():
      # This test is trying to create a race condition describedd in
      # https://github.com/apache/mxnet/issues/10189
      arr = mx.nd.random.uniform(shape=(10, 10, 32, 32))
      weight1 = mx.nd.random.uniform(shape=(10, 10, 3, 3))
      arr = mx.nd.Convolution(
          data=arr,
          weight=weight1,
          no_bias=True,
          kernel=(3, 3),
          num_filter=10,
      )
  
      rs_arr = mx.nd.sparse.row_sparse_array(
          (mx.nd.zeros_like(arr), np.arange(arr.shape[0]))
      )
      weight2 = mx.nd.random.uniform(
          shape=(10, np.prod(arr.shape[1:4]))
      )
      fc_res = mx.nd.FullyConnected(
          data=arr, weight=weight2, no_bias=True, num_hidden=10
      )
      sum_res = mx.nd.elemwise_sub(arr, rs_arr)
      res1 = np.dot(
          mx.nd.flatten(sum_res).asnumpy(), weight2.asnumpy().T
      )
      print(res1 - fc_res.asnumpy())
      almost_equal(res1, fc_res.asnumpy())
  
imports: from mxnet.test_utils import *
  from mxnet.base import MXNetError
  import pytest
  from common import assertRaises
  import random
  import warnings
label_desc: Use image node_id 34 for calling a global function with example usage: test_dnnl_sparse() without return types