code: def testPruneScope_NeededForKerasModel(self):
      model = keras_test_utils.build_simple_dense_model()
      pruned_model = prune.prune_low_magnitude(model)
  
      _, keras_model = tempfile.mkstemp(".h5")
      pruned_model.save(keras_model)
  
      with self.assertRaises(ValueError):
          tf.keras.models.load_model(keras_model)
  
      # works with `prune_scope`
      with prune.prune_scope():
          tf.keras.models.load_model(keras_model)
  
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 26 for calling the PruneTest obj's underlying member method code with example usage: obj.testPruneScope_NeededForKerasModel() without return types