code: def testInterleaveSparse(self):
      def _map_fn(i):
          return sparse_tensor.SparseTensorValue(
              indices=[[0, 0], [1, 1]],
              values=(i * [1, -1]),
              dense_shape=[2, 2],
          )
  
      def _interleave_fn(x):
          return dataset_ops.Dataset.from_tensor_slices(
              sparse_ops.sparse_to_dense(
                  x.indices, x.dense_shape, x.values
              )
          )
  
      dataset = (
          dataset_ops.Dataset.range(10)
          .map(_map_fn)
          .interleave(_interleave_fn, cycle_length=1)
      )
      get_next = self.getNext(dataset)
      for i in range(10):
          for j in range(2):
              expected = [i, 0] if j % 2 == 0 else [0, -i]
              self.assertAllEqual(expected, self.evaluate(get_next()))
      with self.assertRaises(errors.OutOfRangeError):
          self.evaluate(get_next())
      with self.assertRaises(errors.OutOfRangeError):
          self.evaluate(get_next())
  
imports: import multiprocessing
  import os
  from absl.testing import parameterized
  import numpy
  from tensorflow.python.data.experimental.ops import testing
  from tensorflow.python.data.kernel_tests import checkpoint_test_base
  from tensorflow.python.data.kernel_tests import test_base
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import options
  from tensorflow.python.framework import combinations
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import sparse_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 4 for calling the InterleaveTest obj's underlying member method code with example usage: obj.testInterleaveSparse() and returns: sparse_tensor, dataset_ops