code: def test_sparse_ndarray_load_csr_npz_scipy(tmp_path, save_fn):
      csr_sp = spsp.rand(50, 100, density=0.5, format="csr")
      spsp.save_npz(tmp_path / "csr.npz", csr_sp)
  
      csr_mx = mx.nd.load(str(tmp_path / "csr.npz"))[""]
      assert np.sum(csr_mx.data.asnumpy() != csr_sp.data) == 0
      assert np.sum(csr_mx.indices.asnumpy() != csr_sp.indices) == 0
      assert np.sum(csr_mx.indptr.asnumpy() != csr_sp.indptr) == 0
  
      csr_mx = save_fn(str(tmp_path / "csr_mx.npz"), csr_mx)
      csr_mx_loaded = mx.nd.load(str(tmp_path / "csr_mx.npz"))
      csr_mx_loaded = (
          csr_mx_loaded[0]
          if save_fn is mx.nd.save
          else csr_mx_loaded["arr_0"]
      )
      assert np.sum(csr_mx_loaded.data.asnumpy() != csr_sp.data) == 0
      assert (
          np.sum(csr_mx_loaded.indices.asnumpy() != csr_sp.indices) == 0
      )
      assert (
          np.sum(csr_mx_loaded.indptr.asnumpy() != csr_sp.indptr) == 0
      )
  
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 26 for calling a global function with example usage: test_sparse_ndarray_load_csr_npz_scipy(tmp_path, save_fn) without return types