code: def testFunctionalModelForLatencyOnDoubleXNNPackPolicy(self):
      i = keras.Input(shape=(8, 8, 3))
      x = layers.ZeroPadding2D(padding=1)(i)
      x = layers.Conv2D(
          filters=16,
          kernel_size=(3, 3),
          strides=(2, 2),
          padding="valid",
      )(x)
      x = layers.Conv2D(filters=16, kernel_size=[1, 1])(x)
      o = layers.GlobalAveragePooling2D()(x)
      model = keras.Model(inputs=[i], outputs=[o])
  
      pruned_model = prune.prune_low_magnitude(
          model,
          pruning_policy=pruning_policy.PruneForLatencyOnXNNPack(),
          **self.params,
      )
      self.assertEqual(self._count_pruned_layers(pruned_model), 1)
      double_pruned_model = prune.prune_low_magnitude(
          pruned_model,
          pruning_policy=pruning_policy.PruneForLatencyOnXNNPack(),
          **self.params,
      )
      self.assertEqual(
          self._count_pruned_layers(double_pruned_model), 1
      )
  
imports: import distutils.version
  import tensorflow
  from tensorflow_model_optimization.python.core.sparsity.keras import prune
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_policy
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_schedule
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_wrapper
label_desc: Use image node_id 13 for calling the PruningPolicyTest obj's underlying member method code with example usage: obj.testFunctionalModelForLatencyOnDoubleXNNPackPolicy() without return types