code: def KerasFunctionalModel() -> keras.models.Model:
      class FunctionalModel(keras.Model):
          def __init__(self, **kwargs: t.Any):
              super().__init__(**kwargs)
              self.dense_1 = keras.layers.Dense(30, activation="relu")
              self.dense_2 = keras.layers.Dense(10)
  
          @tf.function(
              input_signature=[
                  tf.TensorSpec(shape=(None, 32), dtype=tf.float32)
              ]
          )
          def call(self, inputs: tf.Tensor) -> tf.Tensor:
              inputs = inputs[:30]
              x = self.dense_1(inputs)
              return self.dense_2(x)
  
      model = FunctionalModel()
      model.predict(tf.ones((1, 32)))
      return model
  
imports: from __future__ import annotations
  import typing
  from typing import TYPE_CHECKING
  import keras
  import keras.layers
  import keras.optimizers
  import numpy
  import tensorflow
  import bentoml
  from .None import FrameworkTestModel
  from .None import FrameworkTestModelConfiguration
  from .None import FrameworkTestModelInput
label_desc: Use image node_id 3 for calling a global function with example usage: KerasFunctionalModel() and returns: model, self