code: def get_functional_model(keras):
      inputs = keras.Input((3,), batch_size=2)
      x = keras.layers.Dense(4, activation="relu")(inputs)
      residual = x
      x = keras.layers.BatchNormalization(
          moving_mean_initializer="uniform", gamma_initializer="uniform"
      )(x)
      x = keras.layers.Dense(4, activation="relu")(x)
      x = keras.layers.add([x, residual])
      outputs = keras.layers.Dense(5, activation="softmax")(x)
      return keras.Model(inputs, outputs)
  
imports: import os
  import numpy
  import pytest
  import keras
  from keras import layers
  from keras import models
  from keras import ops
  from keras import testing
  from keras.legacy.saving import legacy_h5_format
  from keras.saving import object_registration
  from keras.saving import serialization_lib
label_desc: Use image node_id 2 for calling a global function with example usage: get_functional_model(keras) and returns: keras