code: def make_nested_model(input_shape):
      """
      This model will have the following architecture:
      Functional (fun_model)
          Dense (fun_first_dense)
          Sequential (seq_sub)
              Dense
              Dense
          Dense (fun_middle_dense)
          Functional (fun_sub)
              Dense
              Dense
          Dense (fun_last_dense)
      """
      seq_sub = Sequential(name="seq_sub")
      seq_sub.add(
          Dense(
              5,
              activation="linear",
              input_shape=(5,),
              name="seq_sub_dense_1",
          )
      )
      seq_sub.add(Dense(3, activation="linear", name="seq_sub_dense_2"))
  
      fun_input = Input(shape=(8,), name="fun_input")
      fun_x = Dense(7, activation="linear", name="fun_sub_dense_1")(
          fun_input
      )
      fun_x = Dense(6, activation="linear", name="fun_sub_dense_2")(
          fun_x
      )
      fun_sub = Model(inputs=fun_input, outputs=fun_x, name="fun_sub")
  
      input = Input(shape=input_shape, name="model_input")
      x = Dense(5, activation="linear", name="fun_first_dense")(input)
      x = seq_sub(x)
      x = Dense(8, activation="linear", name="fun_middle_dense")(x)
      x = fun_sub(x)
      x = Dense(4, activation="linear", name="fun_last_dense")(x)
      fun_model = Model(inputs=input, outputs=x, name="fun_model")
  
      return fun_model
  
file_path: hls4ml/test/pytest/test_keras_nested_model.py
imports: from pathlib import Path
  import numpy
  import pytest
  from tensorflow.keras.layers import Dense, Input
  from tensorflow.keras.models import Model, Sequential
  import hls4ml
label_desc: Use image node_id 1 for calling a global function with example usage: make_nested_model(input_shape) and returns: fun_model