code: def make_backbone():
      inputs = tf.keras.Input(shape=(None, embedding_dim))
      x = layers.Dropout(0.5)(inputs)
      x = layers.Conv1D(
          128, 7, padding="valid", activation="relu", strides=3
      )(x)
      x = layers.Conv1D(
          128, 7, padding="valid", activation="relu", strides=3
      )(x)
      x = layers.GlobalMaxPooling1D()(x)
      x = layers.Dense(128, activation="relu")(x)
      x = layers.Dropout(0.5)(x)
      predictions = layers.Dense(
          1, activation="sigmoid", name="predictions"
      )(x)
  
      model = Model(inputs, predictions)
      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 2 for calling a global function with example usage: make_backbone() and returns: model