code: def testSerializedSparseAndSparseFeatureAndDenseWithNoDefault(self):
      expected_st_a = (
          sparse_tensor.SparseTensorValue(  # indices, values, shape
              np.empty((0, 2), dtype=np.int64),  # indices
              np.empty((0,), dtype=np.int64),  # sp_a is DT_INT64
              np.array([2, 0], dtype=np.int64),
          )
      )  # batch == 2, max_elems = 0
      expected_sp = (
          sparse_tensor.SparseTensorValue(  # indices, values, shape
              np.array([[0, 0], [0, 3], [1, 7]], dtype=np.int64),
              np.array(["a", "b", "c"], dtype="|S"),
              np.array([2, 13], dtype=np.int64),
          )
      )  # batch == 4, max_elems = 13
  
      original = [
          example(
              features=features(
                  {
                      "c": float_feature([3, 4]),
                      "val": bytes_feature([b"a", b"b"]),
                      "idx": int64_feature([0, 3]),
                  }
              )
          ),
          example(
              features=features(
                  {
                      "c": float_feature([1, 2]),
                      "val": bytes_feature([b"c"]),
                      "idx": int64_feature([7]),
                  }
              )
          ),
      ]
  
      serialized = [m.SerializeToString() for m in original]
  
      a_default = [1, 2, 3]
      b_default = np.random.rand(3, 3).astype(bytes)
      expected_output = {
          "st_a": expected_st_a,
          "sp": expected_sp,
          "a": np.array(2 * [[a_default]]),
          "b": np.array(2 * [b_default]),
          "c": np.array([[3, 4], [1, 2]], dtype=np.float32),
      }
  
      self._test(
          ops.convert_to_tensor(serialized),
          {
              "st_a": parsing_ops.VarLenFeature(dtypes.int64),
              "sp": parsing_ops.SparseFeature(
                  "idx", "val", dtypes.string, 13
              ),
              "a": parsing_ops.FixedLenFeature(
                  (1, 3), dtypes.int64, default_value=a_default
              ),
              "b": parsing_ops.FixedLenFeature(
                  (3, 3), dtypes.string, default_value=b_default
              ),
              # Feature "c" must be provided, since it has no default_value.
              "c": parsing_ops.FixedLenFeature((2,), dtypes.float32),
          },
          expected_values=expected_output,
          create_iterator_twice=True,
      )
  
imports: import copy
  from absl.testing import parameterized
  import numpy
  from tensorflow.core.example import example_pb2
  from tensorflow.core.example import feature_pb2
  from tensorflow.python.data.experimental.ops import parsing_ops
  from tensorflow.python.data.kernel_tests import checkpoint_test_base
  from tensorflow.python.data.kernel_tests import test_base
  from tensorflow.python.data.kernel_tests import tf_record_test_base
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import options
  from tensorflow.python.eager import context
  from tensorflow.python.framework import combinations
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors_impl
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import tensor
  from tensorflow.python.ops import parsing_ops
  from tensorflow.python.ops.ragged import ragged_factory_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 15 for calling the ParseExampleDatasetTest obj's underlying member method code with example usage: obj.testSerializedSparseAndSparseFeatureAndDenseWithNoDefault() without return types