code: def get_unwrapped_bert_encoder(
      input_bert_encoder: tfm.nlp.networks.BertEncoder,
  ) -> tfm.nlp.networks.BertEncoder:
      """Creates a new BERT encoder whose layers are core Keras layers."""
      dedup_bert_encoder(input_bert_encoder)
      core_test_outputs = gradient_clipping_utils.generate_model_outputs_using_core_keras_layers(
          input_bert_encoder,
          custom_layer_set={tfm.nlp.layers.TransformerEncoderBlock},
      )
      return tf.keras.Model(
          inputs=input_bert_encoder.inputs,
          outputs=core_test_outputs,
      )
  
imports: import tensorflow
  import tensorflow_models
  from tensorflow_privacy.privacy.fast_gradient_clipping import gradient_clipping_utils
label_desc: Use image node_id 2 for calling a global function with example usage: get_unwrapped_bert_encoder(input_bert_encoder) and returns: tf