code: def _build_model():
      """Create the baseline model."""
      i = tf.keras.layers.Input(shape=(28, 28), name="input")
      x = tf.keras.layers.Reshape((28, 28, 1))(i)
      x = tf.keras.layers.Conv2D(
          filters=12,
          kernel_size=(3, 3),
          activation="relu",
          name="conv1",
      )(x)
      x = tf.keras.layers.MaxPool2D(2, 2)(x)
      x = tf.keras.layers.Flatten()(x)
      output = tf.keras.layers.Dense(10, name="fc2")(x)
      model = tf.keras.Model(inputs=[i], outputs=[output])
  
      return model
  
imports: import numpy
  import tensorflow
  from tensorflow_model_optimization.python.core.clustering.keras import cluster
  from tensorflow_model_optimization.python.core.clustering.keras import cluster_config
  from tensorflow_model_optimization.python.core.clustering.keras.experimental import cluster
  from tensorflow_model_optimization.python.core.quantization.keras import quantize
  from tensorflow_model_optimization.python.core.quantization.keras.collab_opts.cluster_preserve import cluster_utils
  from tensorflow_model_optimization.python.core.quantization.keras.collab_opts.cluster_preserve import default_8bit_cluster_preserve_quantize_scheme
  from tensorflow_model_optimization.python.core.sparsity.keras import prune
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_callbacks
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_schedule
label_desc: Use image node_id 1 for calling a global function with example usage: _build_model() and returns: model