Define custom LSTM with multiple inputs - tensorflow

Question

Following the tutorial writing custom layer, I am trying to implement a custom LSTM layer with multiple input tensors. I am providing two vectors input_1 and input_2 as a list [input_1, input_2] as suggested in the tutorial. The single input code is working but when I change the code for multiple inputs, its throwing the error,
self.kernel = self.add_weight(shape=(input_shape[0][-1], self.units),
TypeError: 'NoneType' object is not subscriptable.
What change I have to do to get rid of the error? Here is the modified code.
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)
input_1 = keras.Input((None, 5))
input_2 = keras.Input((None, 5))
layer = RNN(cell)
y = layer([input_1, input_2])

Answer 1

Error is because of the line, y = layer([input_1, input_2]).
Replacing that line with y = layer((input_1, input_2)) (passing as Tuple of Inputs rather than List of Inputs), will resolve the error.
Complete working code using tf.keras is shown below:
from tensorflow import keras
from tensorflow.keras import backend as K
from tensorflow.keras.layers import RNN
import tensorflow as tf
class MinimalRNNCell(tf.keras.layers.Layer):
def __init__(self, units, **kwargs):
self.units = units
self.state_size = units
#self.state_size = [tf.TensorShape([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)
input_1 = tf.keras.Input((None, 5))
input_2 = tf.keras.Input((None, 5))
layer = RNN(cell)
y = layer((input_1, input_2))
Output of the above code is:
<class 'tuple'>
Hope this helps. Happy Learning!