code: def testPruneThenLoadsKerasWeights_Fails(self):
      model = self._get_pretrained_model()
  
      _, keras_weights = tempfile.mkstemp(".h5")
      model.save_weights(keras_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
      )
  
      # error since number of keras_weights is fewer than weights in pruned model
      # because pruning introduces weights.
      with self.assertRaises(ValueError):
          pruned_model.load_weights(keras_weights)
  
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 16 for calling the PruneIntegrationTest obj's underlying member method code with example usage: obj.testPruneThenLoadsKerasWeights_Fails() without return types