code: def build_sequential_model(input_shape=(28, 28)):
      num_classes = 12
  
      return tf.keras.Sequential(
          [
              layers.InputLayer(input_shape=input_shape),
              layers.Conv2D(
                  32,
                  5,
                  padding="same",
                  activation="relu",
                  input_shape=input_shape,
              ),
              layers.MaxPooling2D((2, 2), (2, 2), padding="same"),
              layers.Conv2D(64, 5, padding="same", activation="relu"),
              layers.MaxPooling2D((2, 2), (2, 2), padding="same"),
              layers.Flatten(),
              layers.Dense(1024, activation="relu"),
              layers.Dropout(0.4),
              layers.Dense(num_classes, activation="softmax"),
          ]
      )
  
file_path: model-optimization/tensorflow_model_optimization/python/examples/quantization_with_sparsity/keras/mnist_cnn.py
imports: from __future__ import print_function
  from absl import app
  import numpy
  import tensorflow
  from tensorflow_model_optimization.python.core.quantization.keras import quantize
  from tensorflow_model_optimization.python.core.quantization.keras.collab_opts.prune_preserve import default_8bit_prune_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_sequential_model(input_shape) and returns: tf