TypeError: Inputs to a layer should be tensors. Got: last_hidden_state
```
def build_model():        
      auto_layer = TFLongformerModel.from_pretrained(PRE_TRAINED_MODEL_NAME, return_dict=False)
    
      context_input_ids = Input(shape=(max_len,), dtype=tf.int32, name="context_input_ids")
      context_attention_mask = Input(shape=(max_len,), dtype=tf.int32, name="context_attention_mask")
      context_position_ids = Input(shape=(max_len,), dtype=tf.int32, name="context_position_ids")
    
      context_sequence_output, context_clf_output = auto_layer(context_input_ids, 
                                                               attention_mask=context_attention_mask,
                                                             token_type_ids=context_position_ids
                                                            )
    
      reponse_input_ids = Input(shape=(max_len,), dtype=tf.int32, name="reponse_input_ids")
      reponse_attention_mask = Input(shape=(max_len,), dtype=tf.int32, name="reponse_attention_mask")
      reponse_position_ids = Input(shape=(max_len,), dtype=tf.int32, name="reponse_position_ids")
    
      reponse_sequence_output, reponse_clf_output = auto_layer(reponse_input_ids, 
                                                               attention_mask=reponse_attention_mask,
                                                               token_type_ids=reponse_position_ids
                                                              )
      cnn_layer_response = Conv1D(
          filters=256,
          kernel_size=3,
          activation='relu',
          # Use 'same' padding so outputs have the same shape as inputs.
          padding='same')
      
      cnn_layer_context = Conv1D(
          filters=256,
          kernel_size=3,
          activation='relu',
          # Use 'same' padding so outputs have the same shape as inputs.
          padding='same')
    
      query_seq_encoding = cnn_layer_response(reponse_sequence_output)
      value_seq_encoding = cnn_layer_context(context_sequence_output)
    
      query_encoding = GlobalAveragePooling1D()(query_seq_encoding)
      value_encoding = GlobalAveragePooling1D()(value_seq_encoding)
    
      aoa_kv_clf_output = AOA()([query_seq_encoding, value_seq_encoding]) # AOA Attention
    
      conc = concatenate([query_encoding, value_encoding, aoa_kv_clf_output])
    
      conc = Dropout(0.2)(conc)
      out = Dense(1, activation='sigmoid')(conc)
      model = Model(inputs=[context_input_ids, context_attention_mask, context_position_ids, reponse_input_ids, reponse_attention_mask, reponse_position_ids], outputs=out)
      return model
```

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-42-fd334eefe081> in <module>()
      1 with strategy.scope():
----> 2   model = build_model()
      3 
      4   METRICS = [
      5       tf.keras.metrics.BinaryAccuracy(),

2 frames
<ipython-input-41-c3d20d15e326> in build_model()
     50 
     51   # Query encoding of shape [batch_size, Tq, filters].
---> 52   query_seq_encoding = cnn_layer_response(reponse_sequence_output)
     53   # Value encoding of shape [batch_size, Tv, filters].
     54   value_seq_encoding = cnn_layer_context(context_sequence_output)

/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
   1018         training=training_mode):
   1019 
-> 1020       input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
   1021       if eager:
   1022         call_fn = self.call

/usr/local/lib/python3.7/dist-packages/keras/engine/input_spec.py in assert_input_compatibility(input_spec, inputs, layer_name)
    194     # have a `shape` attribute.
    195     if not hasattr(x, 'shape'):
--> 196       raise TypeError('Inputs to a layer should be tensors. Got: %s' % (x,))
    197 
    198   if len(inputs) != len(input_spec):

TypeError: Inputs to a layer should be tensors. Got: last_hidden_state
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