code: def testFunctionalModelForLatencyOnXNNPackPolicy(self):
      i1 = keras.Input(shape=(16, 16, 3))
      x1 = layers.ZeroPadding2D(padding=1)(i1)
      x1 = layers.Conv2D(
          filters=16,
          kernel_size=(3, 3),
          strides=(2, 2),
          padding="valid",
      )(x1)
      x1_1 = layers.DepthwiseConv2D(kernel_size=(3, 3), padding="same")(
          x1
      )
      x1_2 = layers.Conv2D(filters=16, kernel_size=[1, 1])(x1)
      x1 = layers.Add()([x1_1, x1_2])
  
      i2 = keras.Input(shape=(16, 16, 3))
      x2 = layers.ZeroPadding2D(padding=1)(i2)
      x2 = layers.Conv2D(
          filters=8,
          kernel_size=(3, 3),
          strides=(2, 2),
          padding="valid",
      )(x2)
      x2 = layers.ZeroPadding2D(padding=1)(x2)
      x2 = layers.DepthwiseConv2D(
          kernel_size=(3, 3),
          strides=(2, 2),
          padding="valid",
      )(x2)
      x2_1 = CompatGlobalAveragePooling2D(keepdims=True)(x2)
      x2_1 = layers.Conv2D(filters=32, kernel_size=[1, 1])(x2_1)
      x2_1 = layers.Activation("sigmoid")(x2_1)
      x2_2 = layers.Conv2D(filters=32, kernel_size=[1, 1])(x2)
      x2_2 = layers.UpSampling2D(interpolation="bilinear")(x2_2)
      x2 = layers.Multiply()([x2_1, x2_2])
  
      x2 = layers.Conv2D(filters=16, kernel_size=[1, 1])(x2)
      x = layers.Add()([x1, x2])
      x = CompatGlobalAveragePooling2D(keepdims=True)(x)
  
      o1 = layers.Conv2D(filters=7, kernel_size=[1, 1])(x)
      o2 = layers.Conv2D(filters=3, kernel_size=[1, 1])(x)
      model = keras.Model(inputs=[i1, i2], outputs=[o1, o2])
  
      pruned_model = prune.prune_low_magnitude(
          model,
          pruning_policy=pruning_policy.PruneForLatencyOnXNNPack(),
          **self.params,
      )
      self.assertEqual(self._count_pruned_layers(pruned_model), 6)
  
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 10 for calling the PruningPolicyTest obj's underlying member method code with example usage: obj.testFunctionalModelForLatencyOnXNNPackPolicy() without return types