code: def build_raw_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(1),
          ]
      )
      return model
  
imports: import re
  from typing import Tuple
  import numpy
  import pandas
  import pytest
  import tensorflow
  from ray.air.constants import MAX_REPR_LENGTH
  from ray.air.util.data_batch_conversion import _convert_batch_type_to_pandas, _convert_pandas_to_batch_type
  from ray.data.preprocessor import Preprocessor
  from ray.train.predictor import TYPE_TO_ENUM
  from ray.train.tensorflow import TensorflowCheckpoint, TensorflowPredictor
  from ray.train.tests.dummy_preprocessor import DummyPreprocessor
label_desc: Use image node_id 1 for calling a global function with example usage: build_raw_model() and returns: model