code: def build_model(base_model_tag, hparams):
      if base_model_tag == "FUNCTIONAL":
          model = make_mlp_functional_model(hparams)
      elif base_model_tag == "SEQUENTIAL":
          model = make_mlp_sequential_model(hparams)
      else:
          model = make_mlp_subclass_model(hparams)
      return model
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import threading
  from absl import app
  from absl import flags
  from absl import logging
  import attr
  import neural_structured_learning
  from research.carls import dynamic_embedding_config_pb2
  from research.carls import dynamic_embedding_neighbor_cache
  from research.carls import graph_regularization
  from research.carls import kbs_server_helper_pybind
  import tensorflow
  from google.protobuf import text_format
label_desc: Use image node_id 11 for calling a global function with example usage: build_model(base_model_tag, hparams) and returns: model