code: def testStripPruningFunctionalModel(self):
      i1 = keras.Input(shape=(10,))
      i2 = keras.Input(shape=(10,))
      x1 = layers.Dense(10)(i1)
      x2 = layers.Dense(10)(i2)
      outputs = layers.Add()([x1, x2])
      model = keras.Model(inputs=[i1, i2], outputs=outputs)
  
      pruned_model = prune.prune_low_magnitude(model, **self.params)
      stripped_model = prune.strip_pruning(pruned_model)
  
      self.assertEqual(self._count_pruned_layers(stripped_model), 0)
      self.assertEqual(model.get_config(), stripped_model.get_config())
  
imports: import json
  import tempfile
  import numpy
  import tensorflow
  from tensorflow_model_optimization.python.core.keras import test_utils
  from tensorflow_model_optimization.python.core.sparsity.keras import prunable_layer
  from tensorflow_model_optimization.python.core.sparsity.keras import prune
  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 25 for calling the PruneTest obj's underlying member method code with example usage: obj.testStripPruningFunctionalModel() without return types