code: def testPruneThenLoadTFWeights_DoesNotPreserveWeights(self):
      model = self._get_pretrained_model()
  
      _, tf_weights = tempfile.mkstemp(".tf")
      model.save_weights(tf_weights)
  
      # load weights into pruned model.
      same_architecture_model = (
          keras_test_utils.build_simple_dense_model()
      )
      pruned_model = prune.prune_low_magnitude(
          same_architecture_model, **self.params
      )
      pruned_model.load_weights(tf_weights)
  
      self._assert_weights_different_values(model, pruned_model)
  
imports: import tempfile
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_model_optimization.python.core.keras import test_utils
  from tensorflow_model_optimization.python.core.sparsity.keras import prune
  from tensorflow_model_optimization.python.core.sparsity.keras import prune_registry
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_callbacks
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_schedule
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_wrapper
  from tensorflow_model_optimization.python.core.sparsity.keras import test_utils
label_desc: Use image node_id 14 for calling the PruneIntegrationTest obj's underlying member method code with example usage: obj.testPruneThenLoadTFWeights_DoesNotPreserveWeights() without return types