code: def testSparse(self):
      def _sparse(i):
          return sparse_tensor.SparseTensorValue(
              indices=[[0]], values=(i * [1]), dense_shape=[1]
          )
  
      dataset = dataset_ops.Dataset.range(10).map(_sparse).batch(5)
      expected_output = [
          sparse_tensor.SparseTensorValue(
              indices=[[0, 0], [1, 0], [2, 0], [3, 0], [4, 0]],
              values=[
                  i * 5,
                  i * 5 + 1,
                  i * 5 + 2,
                  i * 5 + 3,
                  i * 5 + 4,
              ],
              dense_shape=[5, 1],
          )
          for i in range(2)
      ]
      self.assertDatasetProduces(
          dataset, expected_output=expected_output
      )
  
imports: import time
  from absl.testing import parameterized
  import numpy
  from tensorflow.python.checkpoint import checkpoint
  from tensorflow.python.checkpoint import checkpoint_management
  from tensorflow.python.data.experimental.ops import random_access
  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.data.util import nest
  from tensorflow.python.framework import combinations
  from tensorflow.python.framework import dtypes
  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 script_ops
  from tensorflow.python.ops.ragged import ragged_concat_ops
  from tensorflow.python.ops.ragged import ragged_factory_ops
  from tensorflow.python.ops.ragged import ragged_math_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.platform import test
label_desc: Use image node_id 4 for calling the BatchTest obj's underlying member method code with example usage: obj.testSparse() and returns: sparse_tensor