code: def build_model() -> tf.keras.Model:
      model = tf.keras.Sequential(
          [
              tf.keras.layers.InputLayer(input_shape=()),
              # Add feature dimension, expanding (batch_size,) to (batch_size, 1).
              tf.keras.layers.Flatten(),
              tf.keras.layers.Dense(10),
              tf.keras.layers.Dense(1),
          ]
      )
      return model
  
imports: from typing import Dict, Tuple
  from unittest.mock import patch
  import os
  import pytest
  import numpy
  import tensorflow
  import ray
  from ray import train
  from ray.air.integrations.keras import ReportCheckpointCallback
  from ray.train.constants import TRAIN_DATASET_KEY
  from ray.train import ScalingConfig
  from ray.train.tensorflow import TensorflowTrainer, TensorflowPredictor, TensorflowCheckpoint
label_desc: Use image node_id 2 for calling a global function with example usage: build_model() and returns: model