class_docstr: Tests clip_and_aggreate_gradients.
docstr: Tests the outcome is in the desired (dense or sparse) tensor form.
docstr_tok: ["Tests","the","outcome","is","in","the","desired","(","dense","or","sparse",")","tensor","form","."]
code: def testSparse(self, keep_sparse_threshold):
      """Tests the outcome is in the desired (dense or sparse) tensor form."""
      with tf.GradientTape() as tape:
          loss, test_vars = self._get_loss_and_vars(3)
          results = cag.clip_and_aggregate_gradients(
              tape,
              loss,
              test_vars,
              normalize=False,
              l2_norm_clip=1.0,
              aggregate_method="mean",
              unconnected_gradients="zero",
              keep_sparse_threshold=keep_sparse_threshold,
          )
      grads0, grads1, grads2 = results
      # emb0 has 4 items so grads0 should be in the sparse, i.e.
      # `tf.IndexedSlices`, form iff `keep_sparse_threshold` is in [0, 4].
      if keep_sparse_threshold >= 0 and keep_sparse_threshold <= 4:
          self.assertIsInstance(grads0, tf.IndexedSlices)
          self.assertLen(grads0.indices, 3)
      else:
          self.assertIsInstance(grads0, tf.Tensor)
      # grads1 and grads2 should always be in the dense, i.e. `tf.Tensor`, form.
      self.assertIsInstance(grads1, tf.Tensor)
      self.assertIsInstance(grads2, tf.Tensor)
  
file_path: privacy/tensorflow_privacy/privacy/optimizers/clip_and_aggregate_gradients_test.py
imports: from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_privacy.privacy.optimizers import clip_and_aggregate_gradients
label_desc: Use image node_id 7 for calling the ClipAndAggregateGradientsTest obj's underlying member method code with example usage: obj.testSparse(keep_sparse_threshold) without return types