code: def testPruneInferenceWorks_PruningStepCallbackNotRequired(self):
      model = prune.prune_low_magnitude(
          keras.Sequential(
              [
                  layers.Dense(
                      10, activation="relu", input_shape=(100,)
                  ),
                  layers.Dense(2, activation="sigmoid"),
              ]
          ),
          **self.params,
      )
  
      model.compile(
          loss=keras.losses.categorical_crossentropy,
          optimizer=keras.optimizers.SGD(),
          metrics=["accuracy"],
      )
  
      model.predict(np.random.rand(1000, 100))
      model.evaluate(
          np.random.rand(1000, 100),
          keras.utils.to_categorical(
              np.random.randint(2, size=(1000, 1))
          ),
      )
  
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 15 for calling the PruneTest obj's underlying member method code with example usage: obj.testPruneInferenceWorks_PruningStepCallbackNotRequired() without return types