code: def test_sparse_take():
      def check_sparse_take(density, mode):
          data_shape = rand_shape_2d()
          idx_shape = (np.random.randint(low=1, high=10),)
          data = rand_ndarray(
              data_shape, "csr", density=density
          ).astype("int32")
          idx = mx.nd.array(
              np.random.randint(low=-5, high=15, size=idx_shape)
          )
          data_np = data.asnumpy()
          idx_np = idx.asnumpy().astype("int32")
          expected_result = np.take(data_np, idx_np, mode=mode, axis=0)
          result = mx.nd.take(data, idx, mode=mode)
          assert_almost_equal(result.asnumpy(), expected_result)
          assert result.indptr[0].asscalar() == 0
  
      densities = [0, 0.5, 1]
      modes = ["clip", "wrap"]
      for d in densities:
          for m in modes:
              check_sparse_take(d, m)
  
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 40 for calling a global function with example usage: test_sparse_take() without return types