code: def make_model():
      inputs = tf.keras.Input(shape=(None,), dtype="int64")
  
      # use `Embedding` layer in `bigdl.nano.tf.keras.layers`
      x = Embedding(max_features, embedding_dim)(inputs)
  
      predictions = make_backbone()(x)
      model = Model(inputs, predictions)
  
      # use `SparseAdam` optimizer in `bigdl.nano.tf.optimizers`
      model.compile(
          loss="binary_crossentropy",
          optimizer=SparseAdam(),
          metrics=["accuracy"],
      )
  
      return model
  
file_path: BigDL/python/nano/tutorial/training/tensorflow/tensorflow_sparse_embedding.py
imports: import os
  import re
  import string
  import tensorflow
  import tensorflow_datasets
  from tensorflow.keras import layers
  from tensorflow.keras.layers import TextVectorization
  from bigdl.nano.tf.keras.layers import Embedding
  from bigdl.nano.tf.optimizers import SparseAdam
  from bigdl.nano.tf.keras import Model
label_desc: Use image node_id 3 for calling a global function with example usage: make_model() and returns: model