Error on custom RNN/LSTM with multiple inputs

I want to implement a custom RNN/LSTM model similar to this. The model should take two separate vectors as input and process them. I was following keras tutorial to implement a custom keras layer and inputting two vectors a and b as a list [a,b] to the layer as shown below.

import keras
from keras.layers.recurrent import RNN
import keras.backend as K

class MinimalRNNCell(keras.layers.Layer):
    def __init__(self, units, **kwargs):
        self.units = units
        self.state_size = units
        super(MinimalRNNCell, self).__init__(**kwargs)
    def build(self, input_shape):
        print(type(input_shape))
        self.kernel = self.add_weight(shape=(input_shape[0][-1], self.units),
                                      initializer='uniform',
                                      name='kernel')
        self.recurrent_kernel = self.add_weight(
            shape=(self.units, self.units),
            initializer='uniform',
            name='recurrent_kernel')
        self.built = True
    def call(self, inputs, states):
        prev_output = states[0]
        h = K.dot(inputs[0], self.kernel)
        output = h + K.dot(prev_output, self.recurrent_kernel)
        return output, [output]

# Let's use this cell in a RNN layer:
cell = MinimalRNNCell(32)
a = keras.Input((None, 5))
b = keras.Input((None, 5))
layer = RNN(cell)
y = layer([a,b])

But I am getting the error TypeError: 'NoneType' object has no attribute '__getitem__' at

self.kernel = self.add_weight(shape=(input_shape[0][-1], self.units),
                                          initializer='uniform',
                                          name='kernel')

Also the type of the input_shape is showing as <type 'tuple'>, not a list.
Whats wrong I am doing and how to overcome this error. Please help