code: def testPrunesMnist_ReachesTargetSparsity(self, model_type):
      model = test_utils.build_mnist_model(model_type, self.params)
      if model_type == "layer_list":
          model = keras.Sequential(
              prune.prune_low_magnitude(model, **self.params)
          )
      elif model_type in ["sequential", "functional"]:
          model = prune.prune_low_magnitude(model, **self.params)
  
      model.compile(
          loss="categorical_crossentropy",
          optimizer="sgd",
          metrics=["accuracy"],
      )
      test_utils.assert_model_sparsity(
          self, 0.0, model, rtol=1e-4, atol=1e-4
      )
      model.fit(
          np.random.rand(32, 28, 28, 1),
          keras.utils.to_categorical(
              np.random.randint(10, size=(32, 1)), 10
          ),
          callbacks=[pruning_callbacks.UpdatePruningStep()],
      )
  
      test_utils.assert_model_sparsity(
          self, 0.5, model, rtol=1e-4, atol=1e-4
      )
  
      self._check_strip_pruning_matches_original(model, 0.5)
  
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 25 for calling the PruneIntegrationTest obj's underlying member method code with example usage: obj.testPrunesMnist_ReachesTargetSparsity(model_type) without return types