code: def prune_scope():
      """Provides a scope in which Pruned layers and models can be deserialized.
  
      For TF 2.X: this is not needed for SavedModel or TF checkpoints, which are
      the recommended serialization formats.
  
      For TF 1.X: if a tf.keras h5 model or layer has been pruned, it needs to be
      within this
      scope to be successfully deserialized. This is not needed for loading just
      keras weights.
  
      Returns:
          Object of type `CustomObjectScope` with pruning objects included.
  
      Example:
  
      ```python
      pruned_model = prune_low_magnitude(model, **self.params)
      keras.models.save_model(pruned_model, keras_file)
  
      with prune_scope():
        loaded_model = keras.models.load_model(keras_file)
      ```
      """
      return custom_object_scope(
          {"PruneLowMagnitude": pruning_wrapper.PruneLowMagnitude}
      )
  
imports: import tensorflow
  from tensorflow_model_optimization.python.core.keras import metrics
  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 1 for calling a global function with example usage: prune_scope() and returns: custom_object_scope