code: def _MakeEnumerateDataWithMissingDataAtLeaves() -> (
      Iterable[EnumerateStructNullValueTestData]
  ):
      """Test that having only nulls at leaf values gets translated correctly."""
      test_data_type = pa.list_(
          pa.struct([("f2", pa.list_(pa.float64()))])
      )
      struct_column_as_list_dicts = [
          [],  # first element of 'c'; note this is not counted as missing.
          [  # second element of 'c' -- a list<struct> of length 2.
              {
                  "f2": [2.0],
              },
              None,  # f2 is missing
          ],
          [  # third element of 'c'
              None,  # f2 is missing
          ],
          [],  # fourth element of 'c'; note this is not counted as missing.
      ]
  
      array = pa.array(struct_column_as_list_dicts, type=test_data_type)
  
      batch = pa.RecordBatch.from_arrays([array], ["c"])
  
      full_expected_results = {
          types.FeaturePath(["c"]): pa.array(
              [[], [{"f2": [2.0]}, None], [None], []]
          ),
          types.FeaturePath(["c", "f2"]): pa.array([[2.0], None, None]),
      }
      yield "Basic", batch, full_expected_results
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import itertools
  from typing import Dict, Iterable, NamedTuple
  from absl.testing import absltest
  from absl.testing import parameterized
  import numpy
  import pyarrow
  import six
  from tensorflow_data_validation import types
  from tensorflow_data_validation.arrow import arrow_util
  from tensorflow_data_validation.utils.example_weight_map import ExampleWeightMap
  from tfx_bsl.arrow import array_util
  from tfx_bsl.types import tfx_namedtuple
label_desc: Use image node_id 1 for calling a global function with example usage: _MakeEnumerateDataWithMissingDataAtLeaves() without return types