def METHOD_NAME(input_shape, **pruning_params):
  return tf.keras.Sequential([
      l.Conv2D(
          32, 5, padding='same', activation='relu', input_shape=input_shape),
      l.MaxPooling2D((2, 2), (2, 2), padding='same'),
      l.Conv2D(64, 5, padding='same'),
      l.BatchNormalization(),
      l.ReLU(),
      l.MaxPooling2D((2, 2), (2, 2), padding='same'),
      l.Flatten(),
      prune.prune_low_magnitude(
          l.Dense(1024, activation='relu'), **pruning_params),
      l.Dropout(0.4),
      prune.prune_low_magnitude(
          l.Dense(num_classes, activation='softmax'), **pruning_params)
  ])