code: def testGetSparseTensorValue(self):
      sparse_tensor_value = tf.compat.v1.SparseTensorValue(
          indices=[[0, 0, 0], [0, 1, 0], [0, 1, 1]],
          values=["", "one", "two"],
          dense_shape=[1, 2, 2],
      )
      fpl_with_sparse_tensor = types.FeaturesPredictionsLabels(
          input_ref=0, features={}, predictions={}, labels={}
      )
  
      meta_feature_extractor._set_feature_value(
          fpl_with_sparse_tensor.features, "sparse", sparse_tensor_value
      )
      self.assertEqual(
          ["", "one", "two"],
          meta_feature_extractor.get_feature_value(
              fpl_with_sparse_tensor, "sparse"
          ),
      )
  
file_path: model-analysis/tensorflow_model_analysis/extractors/legacy_meta_feature_extractor_test.py
imports: import apache_beam
  from apache_beam.testing import util
  import numpy
  import tensorflow
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.eval_saved_model import testutil
  from tensorflow_model_analysis.extractors import legacy_meta_feature_extractor
  from tensorflow_model_analysis.extractors import slice_key_extractor
  from tensorflow_model_analysis.slicer import slicer_lib
label_desc: Use image node_id 4 for calling the MetaFeatureExtractorTest obj's underlying member method code with example usage: obj.testGetSparseTensorValue() without return types