IndexError: list index out of range

I'm implementing a sequence-2-sequence model with RNN-VAE architecture, and I use an attention mechanism. I have problem in the decoder part. I'm struggling with this error: IndexError: list index out of range When I run this code:

decoder_inputs = Input(shape=(len_target,))
decoder_emb = Embedding(input_dim=vocab_out_size, output_dim=embedding_dim)
decoder_lstm = LSTM(units=units, return_sequences=True, return_state=True)
decoder_lstm_out, _, _ = decoder_lstm(decoder_emb(decoder_inputs), 
             initial_state=encoder_states)

print("enc_outputs", encoder_outputs.shape) # == (?,256)
print("decoder_lstm_out", decoder_lstm_out.shape)# == (?,12,256)
print("zzzzzz", z.shape) # == (?,256)

attn_layer = AttentionLayer(name='attention_layer')
attn_out, attn_states = attn_layer([z,z], decoder_lstm_out)

The error is raised at the last line, and the traceback given:

Traceback (most recent call last):
     File "malek_tuto.py", line 197, in module
           attn_out, attn_states = attn_layer([z,z], decoder_lstm_out)
     File "C:\Users\lightland\Anaconda3\lib\site- 
       packages\tensorflow\python\keras\engine\base_layer.py", line 728, in 
       __call__    self.build(input_shapes)
     File "D:\PFE\Contribution\modele\layers\attention.py", line 24, in 
        build shape=tf.TensorShape((input_shape[0][3], input_shape[0][3])),
     File "C:\Users\lightland\Anaconda3\lib\site- 
       packages\tensorflow\python\framework\tensor_shape.py", line 615, in 
         __getitem__   return self._dims[key]
     IndexError: list index out of range

in AttentionLayer class , build function id defined by:

 def build(self, input_shape):

      assert isinstance(input_shape, list)
      print("hhhhhhhhhh",input_shape)
      print("jjknkjnjk")
      # Create a trainable weight variable for this layer.

       self.W_a = self.add_weight(name='W_a',
                               shape=tf.TensorShape((input_shape[0][2], 
                                input_shape[0][2])),
                               initializer='uniform',
                               trainable=True)
       self.U_a = self.add_weight(name='U_a',
                               shape=tf.TensorShape((input_shape[1][2], 
                               input_shape[0][2])),
                               initializer='uniform',
                               trainable=True)
       self.V_a = self.add_weight(name='V_a',
                               shape=tf.TensorShape((input_shape[0][2], 1)),
                               initializer='uniform',
                               trainable=True)

       super(AttentionLayer, self).build(input_shape)

If someone can help me I'll be so thankful, I cannot understant where the problem is, and how to resolve it.

Thanks in advance