code: def testPruneSequentialModel(self):
      # No InputLayer
      model = keras.Sequential(
          [
              layers.Dense(10),
              layers.Dense(10),
          ]
      )
      pruned_model = prune.prune_low_magnitude(model, **self.params)
      self.assertEqual(self._count_pruned_layers(pruned_model), 2)
  
      # With InputLayer
      model = keras.Sequential(
          [
              layers.Dense(10, input_shape=(10,)),
              layers.Dense(10),
          ]
      )
      pruned_model = prune.prune_low_magnitude(model, **self.params)
      self.assertEqual(self._count_pruned_layers(pruned_model), 2)
  
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 16 for calling the PruneTest obj's underlying member method code with example usage: obj.testPruneSequentialModel() without return types