code: def model_from_config(config):
      """Instantiates a TFE Keras model from its config.
  
      Arguments:
        config: Configuration dictionary matching the output of
            model.get_weights().
  
      Returns:
        A TFE Keras Sequential instance.
      """
  
      tfe_model = tfe.keras.Sequential([])
  
      for k_l_c in config["layers"]:
          # [TODO] zjn: support InputLayer
          if k_l_c["class_name"] != "InputLayer":
              tfe_layer = _instantiate_tfe_layer(k_l_c)
              tfe_model.add(tfe_layer)
  
      return tfe_model
  
file_path: tf-encrypted/tf_encrypted/keras/models/sequential.py
imports: import tensorflow
  import tf_encrypted
  from tf_encrypted.keras import optimizers
  from tf_encrypted.keras.engine import Layer
  from tf_encrypted.keras.engine.input_layer import Input
  from tf_encrypted.keras.engine.input_layer import InputLayer
  from .base_model import BaseModel
label_desc: Use image node_id 1 for calling a global function with example usage: model_from_config(config) and returns: tfe_model