Can't load LSTM encoder decoder model Keras IndexError: list assignment index out of range

I'm using keras and functional API to create an encoder decoder model containing 2 LSTM layers each for binary classification. The shape of input to encoder x is (samples, time steps, in_features) = (126144, 1, 113) The shape of labels y is (samples, time steps, out_features) =(126144, 1, 2) x and y are both numpy arrays.

import tensorflow as tf

import tensorflow.keras as keras
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import Dense
from numpy import array
from numpy import array_equal
from tensorflow.keras.layers import Lambda
from tensorflow.keras import backend as K
n_timesteps_in = 1
n_features = 113
out_features = 2
numberOfLSTMunits = 256
def create_hard_coded_decoder_input_model(batch_size):
# The first part is encoder
encoder_inputs = Input(shape=(n_timesteps_in, n_features), name='encoder_inputs')
encoder_lstm = LSTM(numberOfLSTMunits, return_state=True,return_sequences=True,  
name='encoder_lstm')
encoder_outputs, state_h1, state_c1 = encoder_lstm(encoder_inputs)
# Second LSTM Added
encoder_lstm2 = LSTM(numberOfLSTMunits, return_state=True,  name='encoder_lstm2')
_, state_h2, state_c2 = encoder_lstm2(encoder_outputs) 

states = [state_h1, state_c1, state_h2, state_c2]
decoder_inputs = Input(shape=(1, out_features),  name='decoder_inputs')
decoder_lstm = LSTM(numberOfLSTMunits, return_sequences=True, return_state=True, 
name='decoder_lstm')

# Second LSTM
decoder_lstm2 = LSTM(numberOfLSTMunits, return_sequences=True, return_state=True, 
name='decoder_lstm2')

decoder_dense = Dense(out_features, activation='softmax',  name='decoder_dense')
# New input decoder
all_outputs = []
decoder_input_data = np.zeros((batch_size, 1, out_features))
decoder_input_data[:, 0, 0] = -1 
inputs = decoder_input_data
states1 = [state_h1, state_c1]
states2 = [state_h2, state_c2]
for _ in range(n_timesteps_in):
  # Run the decoder on one time step
  outputs, dh1, dc1 = decoder_lstm(inputs,initial_state= states1)
  final, dh2, dc2 = decoder_lstm2(outputs, initial_state=states2)
  outputs = decoder_dense(final)
  # Store the current prediction (we will concatenate all predictions later)
  all_outputs.append(outputs)
  # Reinject the outputs as inputs for the next loop iteration
  # as well as update the states
  inputs = outputs
  states1 = [state_h1, state_c1]
  states2 = [state_h2, state_c2]
decoder_outputs = Lambda(lambda x: K.concatenate(x, axis=1))(all_outputs)
model = Model(encoder_inputs, decoder_outputs, name='model_encoder_decoder')
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
return model

I'm using 192 as the batch size. After training, I save the model with this code:

model.save('lstm.h5')

When I load the model:

savedModel=load_model('lstm.h5')

I get this error:

  /usr/local/lib/python3.7/dist-packages/keras/layers/recurrent.py in get_input_spec(shape)
  547       batch_index, time_step_index = (1, 0) if self.time_major else (0, 1)
  548       if not self.stateful:
  --> 549         input_spec_shape[batch_index] = None
  550       input_spec_shape[time_step_index] = None
  551       return InputSpec(shape=tuple(input_spec_shape))

  IndexError: list assignment index out of range

I've trying to solve the problem for days, but nothing seemed to work. I really appreciate any help with it.

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