code: def testPruneModelRecursivelyForLatencyOnXNNPackPolicy(self):
      original_model = keras.Sequential(
          [
              layers.ZeroPadding2D(padding=1, input_shape=(8, 8, 3)),
              layers.Conv2D(
                  filters=4,
                  kernel_size=(3, 3),
                  strides=(2, 2),
                  padding="valid",
              ),
              keras.Sequential(
                  [
                      layers.Conv2D(filters=8, kernel_size=[1, 1]),
                      layers.Conv2D(filters=16, kernel_size=[1, 1]),
                  ]
              ),
              layers.GlobalAveragePooling2D(),
          ]
      )
      pruned_model = prune.prune_low_magnitude(
          original_model,
          pruning_policy=pruning_policy.PruneForLatencyOnXNNPack(),
          **self.params,
      )
      self.assertEqual(self._count_pruned_layers(pruned_model), 2)
  
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 7 for calling the PruningPolicyTest obj's underlying member method code with example usage: obj.testPruneModelRecursivelyForLatencyOnXNNPackPolicy() without return types