code: def build_autoencoder_model() -> tf.keras.Model:
      model = tf.keras.Sequential(
          [
              tf.keras.Input(shape=(784,)),
              # encoder
              tf.keras.layers.Dense(128, activation="relu"),
              tf.keras.layers.Dense(64, activation="relu"),
              tf.keras.layers.Dense(32, activation="relu"),
              # decoder
              tf.keras.layers.Dense(64, activation="relu"),
              tf.keras.layers.Dense(128, activation="relu"),
              tf.keras.layers.Dense(784, activation="sigmoid"),
          ]
      )
      return model
  
file_path: ray/python/ray/train/examples/tf/tensorflow_autoencoder_example.py
imports: import argparse
  import numpy
  import pandas
  import tensorflow
  import tensorflow_datasets
  import ray
  from ray import train
  from ray.air.integrations.keras import ReportCheckpointCallback
  from ray.data.datasource import SimpleTensorFlowDatasource
  from ray.data.extensions import TensorArray
  from ray.train import Result
  from ray.train.tensorflow import TensorflowTrainer, prepare_dataset_shard
label_desc: Use image node_id 2 for calling a global function with example usage: build_autoencoder_model() and returns: model