code: def _instantiate_tfe_layer(keras_layer_config):
      """instantiate TFE layer based on Keras layer config.
  
      Arguments:
        keras_layer_config: result of layer.get_config().
  
      Returns:
        A TFE Keras layer instance reproducing the behavior of the
        original Keras layer.
      """
  
      # Identify tf.keras layer type, and grab the corresponding tfe.keras layer
      keras_layer_type = keras_layer_config["class_name"]
      try:
          tfe_layer_cls = getattr(tfe.keras.layers, keras_layer_type)
      except AttributeError:
          # TODO: rethink how we warn the user about this, maybe codegen a list of
          #       supported layers in a doc somewhere
          raise RuntimeError(
              "TF Encrypted does not yet support the {lcls} layer.".format(
                  lcls=keras_layer_type
              )
          )
  
      # get layer config to instiate the tfe layer with the right parameters
      config = keras_layer_config["config"]
  
      return tfe_layer_cls(**config)
  
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 3 for calling a global function with example usage: _instantiate_tfe_layer(keras_layer_config) and returns: tfe_layer_cls