code: def _build_mnist_functional_model():
      # pylint: disable=missing-docstring
      inp = keras.Input(shape=(28, 28, 1))
      x = l.Conv2D(32, 5, padding="same", activation="relu")(inp)
      x = l.MaxPooling2D((2, 2), (2, 2), padding="same")(x)
      x = l.BatchNormalization()(x)
      x = l.Conv2D(64, 5, padding="same", activation="relu")(x)
      x = l.MaxPooling2D((2, 2), (2, 2), padding="same")(x)
      x = l.Flatten()(x)
      x = l.Dense(1024, activation="relu")(x)
      x = l.Dropout(0.4)(x)
      out = l.Dense(10, activation="softmax")(x)
  
      return keras.models.Model([inp], [out])
  
file_path: model-optimization/tensorflow_model_optimization/python/core/sparsity/keras/test_utils.py
imports: import tempfile
  import numpy
  import tensorflow
  from tensorflow_model_optimization.python.core.sparsity.keras import prune
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_utils
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_wrapper
label_desc: Use image node_id 3 for calling a global function with example usage: _build_mnist_functional_model() and returns: keras