code: def test_sparse_nd_slice():
      shape = (rnd.randint(2, 10), rnd.randint(2, 10))
      stype = "csr"
      A, _ = rand_sparse_ndarray(shape, stype)
      A2 = A.asnumpy()
      start = rnd.randint(0, shape[0] - 1)
      end = rnd.randint(start + 1, shape[0])
      assert same(A[start:end].asnumpy(), A2[start:end])
      assert same(A[start - shape[0] : end].asnumpy(), A2[start:end])
      assert same(A[start:].asnumpy(), A2[start:])
      assert same(A[:end].asnumpy(), A2[:end])
      ind = rnd.randint(-shape[0], shape[0] - 1)
      assert same(A[ind].asnumpy(), A2[ind][np.newaxis, :])
  
      start_col = rnd.randint(0, shape[1] - 1)
      end_col = rnd.randint(start_col + 1, shape[1])
      result = mx.nd.slice(
          A, begin=(start, start_col), end=(end, end_col)
      )
      result_dense = mx.nd.slice(
          mx.nd.array(A2), begin=(start, start_col), end=(end, end_col)
      )
      assert same(result_dense.asnumpy(), result.asnumpy())
  
      A = mx.nd.sparse.zeros("csr", shape)
      A2 = A.asnumpy()
      assert same(A[start:end].asnumpy(), A2[start:end])
      result = mx.nd.slice(
          A, begin=(start, start_col), end=(end, end_col)
      )
      result_dense = mx.nd.slice(
          mx.nd.array(A2), begin=(start, start_col), end=(end, end_col)
      )
      assert same(result_dense.asnumpy(), result.asnumpy())
  
      def check_slice_nd_csr_fallback(shape):
          stype = "csr"
          A, _ = rand_sparse_ndarray(shape, stype)
          A2 = A.asnumpy()
          start = rnd.randint(0, shape[0] - 1)
          end = rnd.randint(start + 1, shape[0])
  
          # non-trivial step should fallback to dense slice op
          result = mx.nd.sparse.slice(
              A, begin=(start,), end=(end + 1,), step=(2,)
          )
          result_dense = mx.nd.slice(
              mx.nd.array(A2), begin=(start,), end=(end + 1,), step=(2,)
          )
          assert same(result_dense.asnumpy(), result.asnumpy())
  
      shape = (rnd.randint(2, 10), rnd.randint(1, 10))
      check_slice_nd_csr_fallback(shape)
  
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 6 for calling a global function with example usage: test_sparse_nd_slice() without return types