code: def testPruneScope_NotNeededForTFCheckpoint(self):
      model = keras_test_utils.build_simple_dense_model()
      pruned_model = prune.prune_low_magnitude(model)
  
      _, tf_weights = tempfile.mkstemp(".tf")
      pruned_model.save_weights(tf_weights)
  
      same_architecture_model = (
          keras_test_utils.build_simple_dense_model()
      )
      same_architecture_model = prune.prune_low_magnitude(
          same_architecture_model
      )
  
      # would error if `prune_scope` was needed.
      same_architecture_model.load_weights(tf_weights)
  
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 28 for calling the PruneTest obj's underlying member method code with example usage: obj.testPruneScope_NotNeededForTFCheckpoint() without return types