code: def testPruneModelValidLayersSuccessful(self):
      model = keras.Sequential(
          [
              self.keras_prunable_layer,
              self.keras_non_prunable_layer,
              self.custom_prunable_layer,
          ]
      )
      pruned_model = prune.prune_low_magnitude(model, **self.params)
      pruned_model.build(input_shape=(1, 28, 28, 1))
  
      self.assertEqual(len(model.layers), len(pruned_model.layers))
      for layer, pruned_layer in zip(model.layers, pruned_model.layers):
          self._validate_pruned_layer(layer, pruned_layer)
  
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 10 for calling the PruneTest obj's underlying member method code with example usage: obj.testPruneModelValidLayersSuccessful() without return types