code: def testPruneModelDoesNotWrapAlreadyWrappedLayer(self):
      model = keras.Sequential(
          [
              layers.Dense(10),
              prune.prune_low_magnitude(
                  layers.Dense(10), **self.params
              ),
          ]
      )
      pruned_model = prune.prune_low_magnitude(model, **self.params)
      pruned_model.build(input_shape=(10, 1))
  
      self.assertEqual(len(model.layers), len(pruned_model.layers))
      self._validate_pruned_layer(
          model.layers[0], pruned_model.layers[0]
      )
      # Second layer is used as-is since it's already a pruned layer.
      self.assertEqual(model.layers[1], pruned_model.layers[1])
  
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 13 for calling the PruneTest obj's underlying member method code with example usage: obj.testPruneModelDoesNotWrapAlreadyWrappedLayer() without return types