code: def test_sparse_nd_norm():
      def check_sparse_nd_norm(stype, shape, density, **kwargs):
          data, _ = rand_sparse_ndarray(shape, stype, density)
          norm = data.norm(**kwargs)
          expected_norm = data.tostype("default").norm(**kwargs)
          assert_almost_equal(norm.asnumpy(), expected_norm.asnumpy())
  
      shape = (5, 5)
      stypes = ["row_sparse", "csr"]
      densities = [0, 0.5, 1]
      for stype in stypes:
          for density in densities:
              check_sparse_nd_norm(
                  stype,
                  shape,
                  density,
                  axis=None,
                  keepdims=False,
                  ord=2,
              )
  
      # test fallback
      check_sparse_nd_norm(
          stype, shape, density, axis=0, keepdims=False, ord=2
      )
      check_sparse_nd_norm(
          stype, shape, density, axis=None, keepdims=True, ord=2
      )
  
imports: import pickle
  from mxnet.ndarray import NDArray
  import mxnet
  from mxnet.test_utils import *
  from common import random_seed
  from mxnet.base import mx_real_t
  from numpy.testing import assert_allclose
  import numpy.random
  import numpy
  import scipy.sparse
  from common import assertRaises, xfail_when_nonstandard_decimal_separator
  from mxnet.ndarray.sparse import RowSparseNDArray, CSRNDArray
  import pytest
label_desc: Use image node_id 38 for calling a global function with example usage: test_sparse_nd_norm() without return types