code: def make_keras_sequential_model() -> tf.keras.models.Model:
      net = keras.models.Sequential(
          (
              keras.layers.Dense(
                  units=1,
                  input_shape=(5,),
                  dtype=tf.float64,
                  use_bias=False,
                  kernel_initializer=keras.initializers.Ones(),
              ),
          )
      )
      opt = keras.optimizers.Adam(0.002, 0.5)
      net.compile(
          optimizer=opt,
          loss="binary_crossentropy",
          metrics=["accuracy"],
      )
      # net.fit(x=input_tensor, y=tf.constant([[15.0]]))
      return net
  
imports: from __future__ import annotations
  import typing
  import keras
  import numpy
  import tensorflow
  import bentoml
  from .None import FrameworkTestModel
  from .None import FrameworkTestModelConfiguration
  from .None import FrameworkTestModelInput
label_desc: Use image node_id 1 for calling a global function with example usage: make_keras_sequential_model() and returns: net